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Sample records for yeast metabolic network

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

PubMed Central

Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

2014-01-01

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891
Network thermodynamic curation of human and yeast genome-scale metabolic models.

PubMed

Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

2014-07-15

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance

PubMed Central

Heavner, Benjamin D.; Smallbone, Kieran; Price, Nathan D.; Walker, Larry P.

2013-01-01

Updates to maintain a state-of-the art reconstruction of the yeast metabolic network are essential to reflect our understanding of yeast metabolism and functional organization, to eliminate any inaccuracies identified in earlier iterations, to improve predictive accuracy and to continue to expand into novel subsystems to extend the comprehensiveness of the model. Here, we present version 6 of the consensus yeast metabolic network (Yeast 6) as an update to the community effort to computationally reconstruct the genome-scale metabolic network of Saccharomyces cerevisiae S288c. Yeast 6 comprises 1458 metabolites participating in 1888 reactions, which are annotated with 900 yeast genes encoding the catalyzing enzymes. Compared with Yeast 5, Yeast 6 demonstrates improved sensitivity, specificity and positive and negative predictive values for predicting gene essentiality in glucose-limited aerobic conditions when analyzed with flux balance analysis. Additionally, Yeast 6 improves the accuracy of predicting the likelihood that a mutation will cause auxotrophy. The network reconstruction is available as a Systems Biology Markup Language (SBML) file enriched with Minimium Information Requested in the Annotation of Biochemical Models (MIRIAM)-compliant annotations. Small- and macromolecules in the network are referenced to authoritative databases such as Uniprot or ChEBI. Molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Yeast 6 is freely available at http://yeast.sf.net/ as three separate SBML files: a model using the SBML level 3 Flux Balance Constraint package, a model compatible with the MATLAB® COBRA Toolbox for backward compatibility and a reconstruction containing only reactions for which there is experimental evidence (without the non-biological reactions necessary for simulating growth). Database URL: http://yeast.sf.net/ PMID:23935056
Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

PubMed Central

2012-01-01

Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Conclusions Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a
Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.
Nitrile Metabolizing Yeasts

NASA Astrophysics Data System (ADS)

Bhalla, Tek Chand; Sharma, Monica; Sharma, Nitya Nand

Nitriles and amides are widely distributed in the biotic and abiotic components of our ecosystem. Nitrile form an important group of organic compounds which find their applications in the synthesis of a large number of compounds used as/in pharmaceutical, cosmetics, plastics, dyes, etc>. Nitriles are mainly hydro-lyzed to corresponding amide/acid in organic chemistry. Industrial and agricultural activities have also lead to release of nitriles and amides into the environment and some of them pose threat to human health. Biocatalysis and biotransformations are increasingly replacing chemical routes of synthesis in organic chemistry as a part of ‘green chemistry’. Nitrile metabolizing organisms or enzymes thus has assumed greater significance in all these years to convert nitriles to amides/ acids. The nitrile metabolizing enzymes are widely present in bacteria, fungi and yeasts. Yeasts metabolize nitriles through nitrilase and/or nitrile hydratase and amidase enzymes. Only few yeasts have been reported to possess aldoxime dehydratase. More than sixty nitrile metabolizing yeast strains have been hither to isolated from cyanide treatment bioreactor, fermented foods and soil. Most of the yeasts contain nitrile hydratase-amidase system for metabolizing nitriles. Transformations of nitriles to amides/acids have been carried out with free and immobilized yeast cells. The nitrilases of Torulopsis candida>and Exophiala oligosperma>R1 are enantioselec-tive and regiospecific respectively. Geotrichum>sp. JR1 grows in the presence of 2M acetonitrile and may have potential for application in bioremediation of nitrile contaminated soil/water. The nitrilase of E. oligosperma>R1 being active at low pH (3-6) has shown promise for the hydroxy acids. Immobilized yeast cells hydrolyze some additional nitriles in comparison to free cells. It is expected that more focus in future will be on purification, characterization, cloning, expression and immobilization of nitrile metabolizing
Metabolic reconstruction and flux analysis of industrial Pichia yeasts.

PubMed

Chung, Bevan Kai-Sheng; Lakshmanan, Meiyappan; Klement, Maximilian; Ching, Chi Bun; Lee, Dong-Yup

2013-03-01

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.
Transcriptional Regulation and the Diversification of Metabolism in Wine Yeast Strains

PubMed Central

Rossouw, Debra; Jacobson, Dan; Bauer, Florian F.

2012-01-01

Transcription factors and their binding sites have been proposed as primary targets of evolutionary adaptation because changes to single transcription factors can lead to far-reaching changes in gene expression patterns. Nevertheless, there is very little concrete evidence for such evolutionary changes. Industrial wine yeast strains, of the species Saccharomyces cerevisiae, are a geno- and phenotypically diverse group of organisms that have adapted to the ecological niches of industrial winemaking environments and have been selected to produce specific styles of wine. Variation in transcriptional regulation among wine yeast strains may be responsible for many of the observed differences and specific adaptations to different fermentative conditions in the context of commercial winemaking. We analyzed gene expression profiles of wine yeast strains to assess the impact of transcription factor expression on metabolic networks. The data provide new insights into the molecular basis of variations in gene expression in industrial strains and their consequent effects on metabolic networks important to wine fermentation. We show that the metabolic phenotype of a strain can be shifted in a relatively predictable manner by changing expression levels of individual transcription factors, opening opportunities to modify transcription networks to achieve desirable outcomes. PMID:22042577
A systems-level approach for metabolic engineering of yeast cell factories.

PubMed

Kim, Il-Kwon; Roldão, António; Siewers, Verena; Nielsen, Jens

2012-03-01

The generation of novel yeast cell factories for production of high-value industrial biotechnological products relies on three metabolic engineering principles: design, construction, and analysis. In the last two decades, strong efforts have been put on developing faster and more efficient strategies and/or technologies for each one of these principles. For design and construction, three major strategies are described in this review: (1) rational metabolic engineering; (2) inverse metabolic engineering; and (3) evolutionary strategies. Independent of the selected strategy, the process of designing yeast strains involves five decision points: (1) choice of product, (2) choice of chassis, (3) identification of target genes, (4) regulating the expression level of target genes, and (5) network balancing of the target genes. At the construction level, several molecular biology tools have been developed through the concept of synthetic biology and applied for the generation of novel, engineered yeast strains. For comprehensive and quantitative analysis of constructed strains, systems biology tools are commonly used and using a multi-omics approach. Key information about the biological system can be revealed, for example, identification of genetic regulatory mechanisms and competitive pathways, thereby assisting the in silico design of metabolic engineering strategies for improving strain performance. Examples on how systems and synthetic biology brought yeast metabolic engineering closer to industrial biotechnology are described in this review, and these examples should demonstrate the potential of a systems-level approach for fast and efficient generation of yeast cell factories. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.
Plasticity of genetic interactions in metabolic networks of yeast.

PubMed

Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

2007-02-13

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.
Glycosylceramide modifies the flavor and metabolic characteristics of sake yeast.

PubMed

Ferdouse, Jannatul; Yamamoto, Yuki; Taguchi, Seiga; Yoshizaki, Yumiko; Takamine, Kazunori; Kitagaki, Hiroshi

2018-01-01

In the manufacture of sake, Japanese traditional rice wine, sake yeast is fermented with koji, which is steamed rice fermented with the non-pathogenic fungus Aspergillus oryzae . During fermentation, sake yeast requires lipids, such as unsaturated fatty acids and sterols, in addition to substances provided by koji enzymes for fermentation. However, the role of sphingolipids on the brewing characteristics of sake yeast has not been studied. In this study, we revealed that glycosylceramide, one of the sphingolipids abundant in koji, affects yeast fermentation. The addition of soy, A. oryzae , and Grifola frondosa glycosylceramide conferred a similar effect on the flavor profiles of sake yeast. In particular, the addition of A. oryzae and G. frondosa glycosylceramide were very similar in terms of the decreases in ethyl caprylate and ethyl 9-decenoate. The addition of soy glycosylceramide induced metabolic changes to sake yeast such as a decrease in glucose, increases in ethanol and glycerol and changes in several amino acids and organic acids concentrations. Tricarboxylic acid (TCA) cycle, pyruvate metabolism, starch and sucrose metabolism, and glycerolipid metabolism were overrepresented in the cultures incubated with sake yeast and soy glycosylceramide. This is the first study of the effect of glycosylceramide on the flavor and metabolic profile of sake yeast.
Glycosylceramide modifies the flavor and metabolic characteristics of sake yeast

PubMed Central

Taguchi, Seiga; Yoshizaki, Yumiko; Takamine, Kazunori

2018-01-01

In the manufacture of sake, Japanese traditional rice wine, sake yeast is fermented with koji, which is steamed rice fermented with the non-pathogenic fungus Aspergillus oryzae. During fermentation, sake yeast requires lipids, such as unsaturated fatty acids and sterols, in addition to substances provided by koji enzymes for fermentation. However, the role of sphingolipids on the brewing characteristics of sake yeast has not been studied. In this study, we revealed that glycosylceramide, one of the sphingolipids abundant in koji, affects yeast fermentation. The addition of soy, A. oryzae, and Grifola frondosa glycosylceramide conferred a similar effect on the flavor profiles of sake yeast. In particular, the addition of A. oryzae and G. frondosa glycosylceramide were very similar in terms of the decreases in ethyl caprylate and ethyl 9-decenoate. The addition of soy glycosylceramide induced metabolic changes to sake yeast such as a decrease in glucose, increases in ethanol and glycerol and changes in several amino acids and organic acids concentrations. Tricarboxylic acid (TCA) cycle, pyruvate metabolism, starch and sucrose metabolism, and glycerolipid metabolism were overrepresented in the cultures incubated with sake yeast and soy glycosylceramide. This is the first study of the effect of glycosylceramide on the flavor and metabolic profile of sake yeast. PMID:29761062
Regulation of yeast central metabolism by enzyme phosphorylation

PubMed Central

Oliveira, Ana Paula; Ludwig, Christina; Picotti, Paola; Kogadeeva, Maria; Aebersold, Ruedi; Sauer, Uwe

2012-01-01

As a frequent post-translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino-acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol-3-phosphate dehydrogenase Gpd1 and phosphofructose-1-kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes. PMID:23149688
Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

PubMed Central

Kuang, Meihua Christina; Hutchins, Paul D; Russell, Jason D; Coon, Joshua J; Hittinger, Chris Todd

2016-01-01

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation. DOI: http://dx.doi.org/10.7554/eLife.19027.001 PMID:27690225
Accumulation and metabolism of selenium by yeast cells.

PubMed

Kieliszek, Marek; Błażejak, Stanisław; Gientka, Iwona; Bzducha-Wróbel, Anna

2015-07-01

This paper examines the process of selenium bioaccumulation and selenium metabolism in yeast cells. Yeast cells can bind elements in ionic from the environment and permanently integrate them into their cellular structure. Up to now, Saccharomyces cerevisiae, Candida utilis, and Yarrowia lipolytica yeasts have been used primarily in biotechnological studies to evaluate binding of minerals. Yeast cells are able to bind selenium in the form of both organic and inorganic compounds. The process of bioaccumulation of selenium by microorganisms occurs through two mechanisms: extracellular binding by ligands of membrane assembly and intracellular accumulation associated with the transport of ions across the cytoplasmic membrane into the cell interior. During intracellular metabolism of selenium, oxidation, reduction, methylation, and selenoprotein synthesis processes are involved, as exemplified by detoxification processes that allow yeasts to survive under culture conditions involving the elevated selenium concentrations which were observed. Selenium yeasts represent probably the best absorbed form of this element. In turn, in terms of wide application, the inclusion of yeast with accumulated selenium may aid in lessening selenium deficiency in a diet.
FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

PubMed

Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

2014-10-01

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.
Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism

PubMed Central

Aung, Hnin W.; Henry, Susan A.

2013-01-01

Abstract Genome-scale metabolic models are built using information from an organism's annotated genome and, correspondingly, information on reactions catalyzed by the set of metabolic enzymes encoded by the genome. These models have been successfully applied to guide metabolic engineering to increase production of metabolites of industrial interest. Congruity between simulated and experimental metabolic behavior is influenced by the accuracy of the representation of the metabolic network in the model. In the interest of applying the consensus model of Saccharomyces cerevisiae metabolism for increased productivity of triglycerides, we manually evaluated the representation of fatty acid, glycerophospholipid, and glycerolipid metabolism in the consensus model (Yeast v6.0). These areas of metabolism were chosen due to their tightly interconnected nature to triglyceride synthesis. Manual curation was facilitated by custom MATLAB functions that return information contained in the model for reactions associated with genes and metabolites within the stated areas of metabolism. Through manual curation, we have identified inconsistencies between information contained in the model and literature knowledge. These inconsistencies include incorrect gene-reaction associations, improper definition of substrates/products in reactions, inappropriate assignments of reaction directionality, nonfunctional β-oxidation pathways, and missing reactions relevant to the synthesis and degradation of triglycerides. Suggestions to amend these inconsistencies in the Yeast v6.0 model can be implemented through a MATLAB script provided in the Supplementary Materials, Supplementary Data S1 (Supplementary Data are available online at www.liebertpub.com/ind). PMID:24678285
A Global Protein Kinase and Phosphatase Interaction Network in Yeast

PubMed Central

Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

2011-01-01

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023
Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals

PubMed Central

Shi, Shuobo; Zhao, Huimin

2017-01-01

Oleaginous yeasts have been increasingly explored for production of chemicals and fuels via metabolic engineering. Particularly, there is a growing interest in using oleaginous yeasts for the synthesis of lipid-related products due to their high lipogenesis capability, robustness, and ability to utilize a variety of substrates. Most of the metabolic engineering studies in oleaginous yeasts focused on Yarrowia that already has plenty of genetic engineering tools. However, recent advances in systems biology and synthetic biology have provided new strategies and tools to engineer those oleaginous yeasts that have naturally high lipid accumulation but lack genetic tools, such as Rhodosporidium, Trichosporon, and Lipomyces. This review highlights recent accomplishments in metabolic engineering of oleaginous yeasts and recent advances in the development of genetic engineering tools in oleaginous yeasts within the last 3 years. PMID:29167664
Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals.

PubMed

Shi, Shuobo; Zhao, Huimin

2017-01-01

Oleaginous yeasts have been increasingly explored for production of chemicals and fuels via metabolic engineering. Particularly, there is a growing interest in using oleaginous yeasts for the synthesis of lipid-related products due to their high lipogenesis capability, robustness, and ability to utilize a variety of substrates. Most of the metabolic engineering studies in oleaginous yeasts focused on Yarrowia that already has plenty of genetic engineering tools. However, recent advances in systems biology and synthetic biology have provided new strategies and tools to engineer those oleaginous yeasts that have naturally high lipid accumulation but lack genetic tools, such as Rhodosporidium , Trichosporon , and Lipomyces . This review highlights recent accomplishments in metabolic engineering of oleaginous yeasts and recent advances in the development of genetic engineering tools in oleaginous yeasts within the last 3 years.

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

PubMed Central

Usaite, Renata; Jewett, Michael C; Oliveira, Ana Paula; Yates, John R; Olsson, Lisbeth; Nielsen, Jens

2009-01-01

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases. PMID:19888214
Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

DOE PAGES

Kuang, Meihua Christina; Hutchins, Paul D.; Russell, Jason D.; ...

2016-09-30

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of theGALactose sugar utilization network in two yeast species. Here, we show that theSaccharomyces uvarumnetwork is more active, even as over-induction is prevented by a second co-repressor that the model yeastSaccharomyces cerevisiaelacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systemsmore » and exacerbate this phenotype. Furthermore, we show thatS. cerevisiaeexperiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. Our results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation.« less
Metabolic engineering of yeast for lignocellulosic biofuel production.

PubMed

Jin, Yong-Su; Cate, Jamie Hd

2017-12-01

Production of biofuels from lignocellulosic biomass remains an unsolved challenge in industrial biotechnology. Efforts to use yeast for conversion face the question of which host organism to use, counterbalancing the ease of genetic manipulation with the promise of robust industrial phenotypes. Saccharomyces cerevisiae remains the premier host for metabolic engineering of biofuel pathways, due to its many genetic, systems and synthetic biology tools. Numerous engineering strategies for expanding substrate ranges and diversifying products of S. cerevisiae have been developed. Other yeasts generally lack these tools, yet harbor superior phenotypes that could be exploited in the harsh processes required for lignocellulosic biofuel production. These include thermotolerance, resistance to toxic compounds generated during plant biomass deconstruction, and wider carbon consumption capabilities. Although promising, these yeasts have yet to be widely exploited. By contrast, oleaginous yeasts such as Yarrowia lipolytica capable of producing high titers of lipids are rapidly advancing in terms of the tools available for their metabolic manipulation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Mitochondrial metabolism and stress response of yeast: Applications in fermentation technologies.

PubMed

Kitagaki, Hiroshi; Takagi, Hiroshi

2014-04-01

Mitochondria are sites of oxidative respiration. During sake brewing, sake yeasts are exposed to long periods of hypoxia; the structure, role, and metabolism of mitochondria of sake yeasts have not been studied in detail. It was first elucidated that the mitochondrial structure of sake yeast transforms from filamentous to dotted structure during sake brewing, which affects malate metabolism. Based on the information of yeast mitochondria during sake brewing, practical technologies have been developed; (i) breeding pyruvate-underproducing sake yeast by the isolation of a mutant resistant to an inhibitor of mitochondrial pyruvate transport; and (ii) modifying malate and succinate production by manipulating mitochondrial activity. During the bread-making process, baker's yeast cells are exposed to a variety of baking-associated stresses, such as freeze-thaw, air-drying, and high sucrose concentrations. These treatments induce oxidative stress generating reactive oxygen species due to mitochondrial damage. A novel metabolism of proline and arginine catalyzed by N-acetyltransferase Mpr1 in the mitochondria eventually leads to synthesis of nitric oxide, which confers oxidative stress tolerance on yeast cells. The enhancement of proline and arginine metabolism could be promising for breeding novel baker's yeast strains that are tolerant to multiple baking-associated stresses. These new and practical methods provide approaches to improve the processes in the field of industrial fermentation technologies. Copyright © 2013 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.
Homocysteine regulates fatty acid and lipid metabolism in yeast.

PubMed

Visram, Myriam; Radulovic, Maja; Steiner, Sabine; Malanovic, Nermina; Eichmann, Thomas O; Wolinski, Heimo; Rechberger, Gerald N; Tehlivets, Oksana

2018-04-13

S -Adenosyl-l-homocysteine hydrolase (AdoHcy hydrolase; Sah1 in yeast/AHCY in mammals) degrades AdoHcy, a by-product and strong product inhibitor of S -adenosyl-l-methionine (AdoMet)-dependent methylation reactions, to adenosine and homocysteine (Hcy). This reaction is reversible, so any elevation of Hcy levels, such as in hyperhomocysteinemia (HHcy), drives the formation of AdoHcy, with detrimental consequences for cellular methylation reactions. HHcy, a pathological condition linked to cardiovascular and neurological disorders, as well as fatty liver among others, is associated with a deregulation of lipid metabolism. Here, we developed a yeast model of HHcy to identify mechanisms that dysregulate lipid metabolism. Hcy supplementation to wildtype cells up-regulated cellular fatty acid and triacylglycerol content and induced a shift in fatty acid composition, similar to changes observed in mutants lacking Sah1. Expression of the irreversible bacterial pathway for AdoHcy degradation in yeast allowed us to dissect the impact of AdoHcy accumulation on lipid metabolism from the impact of elevated Hcy. Expression of this pathway fully suppressed the growth deficit of sah1 mutants as well as the deregulation of lipid metabolism in both the sah1 mutant and Hcy-exposed wildtype, showing that AdoHcy accumulation mediates the deregulation of lipid metabolism in response to elevated Hcy in yeast. Furthermore, Hcy supplementation in yeast led to increased resistance to cerulenin, an inhibitor of fatty acid synthase, as well as to a concomitant decline of condensing enzymes involved in very long-chain fatty acid synthesis, in line with the observed shift in fatty acid content and composition. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.
Homocysteine regulates fatty acid and lipid metabolism in yeast

PubMed Central

Visram, Myriam; Radulovic, Maja; Steiner, Sabine; Malanovic, Nermina; Eichmann, Thomas O.; Wolinski, Heimo; Rechberger, Gerald N.; Tehlivets, Oksana

2018-01-01

S-Adenosyl-l-homocysteine hydrolase (AdoHcy hydrolase; Sah1 in yeast/AHCY in mammals) degrades AdoHcy, a by-product and strong product inhibitor of S-adenosyl-l-methionine (AdoMet)-dependent methylation reactions, to adenosine and homocysteine (Hcy). This reaction is reversible, so any elevation of Hcy levels, such as in hyperhomocysteinemia (HHcy), drives the formation of AdoHcy, with detrimental consequences for cellular methylation reactions. HHcy, a pathological condition linked to cardiovascular and neurological disorders, as well as fatty liver among others, is associated with a deregulation of lipid metabolism. Here, we developed a yeast model of HHcy to identify mechanisms that dysregulate lipid metabolism. Hcy supplementation to wildtype cells up-regulated cellular fatty acid and triacylglycerol content and induced a shift in fatty acid composition, similar to changes observed in mutants lacking Sah1. Expression of the irreversible bacterial pathway for AdoHcy degradation in yeast allowed us to dissect the impact of AdoHcy accumulation on lipid metabolism from the impact of elevated Hcy. Expression of this pathway fully suppressed the growth deficit of sah1 mutants as well as the deregulation of lipid metabolism in both the sah1 mutant and Hcy-exposed wildtype, showing that AdoHcy accumulation mediates the deregulation of lipid metabolism in response to elevated Hcy in yeast. Furthermore, Hcy supplementation in yeast led to increased resistance to cerulenin, an inhibitor of fatty acid synthase, as well as to a concomitant decline of condensing enzymes involved in very long-chain fatty acid synthesis, in line with the observed shift in fatty acid content and composition. PMID:29414770
Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling

PubMed Central

Triana, Sergio; de Cock, Hans; Ohm, Robin A.; Danies, Giovanna; Wösten, Han A. B.; Restrepo, Silvia; González Barrios, Andrés F.; Celis, Adriana

2017-01-01

Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia. PMID:28959251
Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling.

PubMed

Triana, Sergio; de Cock, Hans; Ohm, Robin A; Danies, Giovanna; Wösten, Han A B; Restrepo, Silvia; González Barrios, Andrés F; Celis, Adriana

2017-01-01

Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa , Malassezia sympodialis , and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur , and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis . The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa , M. sympodialis , M. pachydermatis , and the atypical variant of M. furfur , but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia .
Proteins involved in flor yeast carbon metabolism under biofilm formation conditions.

PubMed

Moreno-García, Jaime; García-Martínez, Teresa; Moreno, Juan; Mauricio, Juan Carlos

2015-04-01

A lack of sugars during the production of biologically aged wines after fermentation of grape must causes flor yeasts to metabolize other carbon molecules formed during fermentation (ethanol and glycerol, mainly). In this work, a proteome analysis involving OFFGEL fractionation prior to LC/MS detection was used to elucidate the carbon metabolism of a flor yeast strain under biofilm formation conditions (BFC). The results were compared with those obtained under non-biofilm formation conditions (NBFC). Proteins associated to processes such as non-fermentable carbon uptake, the glyoxylate and TCA cycles, cellular respiration and inositol metabolism were detected at higher concentrations under BFC than under the reference conditions (NBFC). This study constitutes the first attempt at identifying the flor yeast proteins responsible for the peculiar sensory profile of biologically aged wines. A better metabolic knowledge of flor yeasts might facilitate the development of effective strategies for improved production of these special wines. Copyright © 2014 Elsevier Ltd. All rights reserved.
A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica

PubMed Central

2012-01-01

Background Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism. Results Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inserted specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions. Conclusions Y. lipolytica iNL895 represents the first well-annotated metabolic model of an oleaginous yeast, providing a base for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts. PMID:22558935
Engineering yeasts for xylose metabolism

Treesearch

Thomas W. Jeffries

2006-01-01

Technologies for the production of alternative fuels are receiving increased attention owing to concerns over the rising cost of petrol and global warming. One such technology under development is the use of yeasts for the commercial fermentation of xylose to ethanol. Several approaches have been employed to engineer xylose metabolism. These involve modeling, flux...
Phenotypic and metabolic traits of commercial Saccharomyces cerevisiae yeasts

PubMed Central

2014-01-01

Currently, pursuing yeast strains that display both a high potential fitness for alcoholic fermentation and a favorable impact on quality is a major goal in the alcoholic beverage industry. This considerable industrial interest has led to many studies characterizing the phenotypic and metabolic traits of commercial yeast populations. In this study, 20 Saccharomyces cerevisiae strains from different geographical origins exhibited high phenotypic diversity when their response to nine biotechnologically relevant conditions was examined. Next, the fermentation fitness and metabolic traits of eight selected strains with a unique phenotypic profile were evaluated in a high-sugar synthetic medium under two nitrogen regimes. Although the strains exhibited significant differences in nitrogen requirements and utilization rates, a direct relationship between nitrogen consumption, specific growth rate, cell biomass, cell viability, acetic acid and glycerol formation was only observed under high-nitrogen conditions. In contrast, the strains produced more succinic acid under the low-nitrogen regime, and a direct relationship with the final cell biomass was established. Glucose and fructose utilization patterns depended on both yeast strain and nitrogen availability. For low-nitrogen fermentation, three strains did not fully degrade the fructose. This study validates phenotypic and metabolic diversity among commercial wine yeasts and contributes new findings on the relationship between nitrogen availability, yeast cell growth and sugar utilization. We suggest that measuring nitrogen during the stationary growth phase is important because yeast cells fermentative activity is not exclusively related to population size, as previously assumed, but it is also related to the quantity of nitrogen consumed during this growth phase. PMID:24949272
Phenotypic and metabolic traits of commercial Saccharomyces cerevisiae yeasts.

PubMed

Barbosa, Catarina; Lage, Patrícia; Vilela, Alice; Mendes-Faia, Arlete; Mendes-Ferreira, Ana

2014-01-01

Currently, pursuing yeast strains that display both a high potential fitness for alcoholic fermentation and a favorable impact on quality is a major goal in the alcoholic beverage industry. This considerable industrial interest has led to many studies characterizing the phenotypic and metabolic traits of commercial yeast populations. In this study, 20 Saccharomyces cerevisiae strains from different geographical origins exhibited high phenotypic diversity when their response to nine biotechnologically relevant conditions was examined. Next, the fermentation fitness and metabolic traits of eight selected strains with a unique phenotypic profile were evaluated in a high-sugar synthetic medium under two nitrogen regimes. Although the strains exhibited significant differences in nitrogen requirements and utilization rates, a direct relationship between nitrogen consumption, specific growth rate, cell biomass, cell viability, acetic acid and glycerol formation was only observed under high-nitrogen conditions. In contrast, the strains produced more succinic acid under the low-nitrogen regime, and a direct relationship with the final cell biomass was established. Glucose and fructose utilization patterns depended on both yeast strain and nitrogen availability. For low-nitrogen fermentation, three strains did not fully degrade the fructose. This study validates phenotypic and metabolic diversity among commercial wine yeasts and contributes new findings on the relationship between nitrogen availability, yeast cell growth and sugar utilization. We suggest that measuring nitrogen during the stationary growth phase is important because yeast cells fermentative activity is not exclusively related to population size, as previously assumed, but it is also related to the quantity of nitrogen consumed during this growth phase.
Structural kinetic modeling of metabolic networks.

PubMed

Steuer, Ralf; Gross, Thilo; Selbig, Joachim; Blasius, Bernd

2006-08-08

To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.
Visible light alters yeast metabolic rhythms by inhibiting respiration.

PubMed

Robertson, James Brian; Davis, Chris R; Johnson, Carl Hirschie

2013-12-24

Exposure of cells to visible light in nature or in fluorescence microscopy often is considered to be relatively innocuous. However, using the yeast respiratory oscillation (YRO) as a sensitive measurement of metabolism, we find that non-UV visible light has a significant impact on yeast metabolism. Blue/green wavelengths of visible light shorten the period and dampen the amplitude of the YRO, which is an ultradian rhythm of cell metabolism and transcription. The wavelengths of light that have the greatest effect coincide with the peak absorption regions of cytochromes. Moreover, treating yeast with the electron transport inhibitor sodium azide has similar effects on the YRO as visible light. Because impairment of respiration by light would change several state variables believed to play vital roles in the YRO (e.g., oxygen tension and ATP levels), we tested oxygen's role in YRO stability and found that externally induced oxygen depletion can reset the phase of the oscillation, demonstrating that respiratory capacity plays a role in the oscillation's period and phase. Light-induced damage to the cytochromes also produces reactive oxygen species that up-regulate the oxidative stress response gene TRX2 that is involved in pathways that enable sustained growth in bright visible light. Therefore, visible light can modulate cellular rhythmicity and metabolism through unexpectedly photosensitive pathways.
Divergence of iron metabolism in wild Malaysian yeast.

PubMed

Lee, Hana N; Mostovoy, Yulia; Hsu, Tiffany Y; Chang, Amanda H; Brem, Rachel B

2013-12-09

Comparative genomic studies have reported widespread variation in levels of gene expression within and between species. Using these data to infer organism-level trait divergence has proven to be a key challenge in the field. We have used a wild Malaysian population of S. cerevisiae as a test bed in the search to predict and validate trait differences based on observations of regulatory variation. Malaysian yeast, when cultured in standard medium, activated regulatory programs that protect cells from the toxic effects of high iron. Malaysian yeast also showed a hyperactive regulatory response during culture in the presence of excess iron and had a unique growth defect in conditions of high iron. Molecular validation experiments pinpointed the iron metabolism factors AFT1, CCC1, and YAP5 as contributors to these molecular and cellular phenotypes; in genome-scale sequence analyses, a suite of iron toxicity response genes showed evidence for rapid protein evolution in Malaysian yeast. Our findings support a model in which iron metabolism has diverged in Malaysian yeast as a consequence of a change in selective pressure, with Malaysian alleles shifting the dynamic range of iron response to low-iron concentrations and weakening resistance to extreme iron toxicity. By dissecting the iron scarcity specialist behavior of Malaysian yeast, our work highlights the power of expression divergence as a signpost for biologically and evolutionarily relevant variation at the organismal level. Interpreting the phenotypic relevance of gene expression variation is one of the primary challenges of modern genomics.
[Detection of viable metabolically active yeast cells using a colorimetric assay].

PubMed

Růzicka, F; Holá, V

2008-02-01

The increasing concern of yeasts able to form biofilm brings about the need for susceptibility testing of both planktonic and biofilm cells. Detection of viability or metabolic activity of yeast cells after exposure to antimicrobials plays a key role in the assessment of susceptibility testing results. Colorimetric assays based on the color change of the medium in the presence of metabolically active cells proved suitable for this purpose. In this study, the usability of a colorimetric assay with the resazurin redox indicator for monitoring the effect of yeast inoculum density on the reduction rate was tested. As correlation between the color change rate and inoculum density was observed, approximate quantification of viable cells was possible. The assay would be of relevance to antifungal susceptibility testing in both planktonic and biofilm yeasts.
Global Mapping of the Yeast Genetic Interaction Network

NASA Astrophysics Data System (ADS)

Tong, Amy Hin Yan; Lesage, Guillaume; Bader, Gary D.; Ding, Huiming; Xu, Hong; Xin, Xiaofeng; Young, James; Berriz, Gabriel F.; Brost, Renee L.; Chang, Michael; Chen, YiQun; Cheng, Xin; Chua, Gordon; Friesen, Helena; Goldberg, Debra S.; Haynes, Jennifer; Humphries, Christine; He, Grace; Hussein, Shamiza; Ke, Lizhu; Krogan, Nevan; Li, Zhijian; Levinson, Joshua N.; Lu, Hong; Ménard, Patrice; Munyana, Christella; Parsons, Ainslie B.; Ryan, Owen; Tonikian, Raffi; Roberts, Tania; Sdicu, Anne-Marie; Shapiro, Jesse; Sheikh, Bilal; Suter, Bernhard; Wong, Sharyl L.; Zhang, Lan V.; Zhu, Hongwei; Burd, Christopher G.; Munro, Sean; Sander, Chris; Rine, Jasper; Greenblatt, Jack; Peter, Matthias; Bretscher, Anthony; Bell, Graham; Roth, Frederick P.; Brown, Grant W.; Andrews, Brenda; Bussey, Howard; Boone, Charles

2004-02-01

A genetic interaction network containing ~1000 genes and ~4000 interactions was mapped by crossing mutations in 132 different query genes into a set of ~4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.
Divergence of Iron Metabolism in Wild Malaysian Yeast

PubMed Central

Lee, Hana N.; Mostovoy, Yulia; Hsu, Tiffany Y.; Chang, Amanda H.; Brem, Rachel B.

2013-01-01

Comparative genomic studies have reported widespread variation in levels of gene expression within and between species. Using these data to infer organism-level trait divergence has proven to be a key challenge in the field. We have used a wild Malaysian population of S. cerevisiae as a test bed in the search to predict and validate trait differences based on observations of regulatory variation. Malaysian yeast, when cultured in standard medium, activated regulatory programs that protect cells from the toxic effects of high iron. Malaysian yeast also showed a hyperactive regulatory response during culture in the presence of excess iron and had a unique growth defect in conditions of high iron. Molecular validation experiments pinpointed the iron metabolism factors AFT1, CCC1, and YAP5 as contributors to these molecular and cellular phenotypes; in genome-scale sequence analyses, a suite of iron toxicity response genes showed evidence for rapid protein evolution in Malaysian yeast. Our findings support a model in which iron metabolism has diverged in Malaysian yeast as a consequence of a change in selective pressure, with Malaysian alleles shifting the dynamic range of iron response to low-iron concentrations and weakening resistance to extreme iron toxicity. By dissecting the iron scarcity specialist behavior of Malaysian yeast, our work highlights the power of expression divergence as a signpost for biologically and evolutionarily relevant variation at the organismal level. Interpreting the phenotypic relevance of gene expression variation is one of the primary challenges of modern genomics. PMID:24142925
An integrated approach to characterize genetic interaction networks in yeast metabolism

PubMed Central

Szappanos, Balázs; Kovács, Károly; Szamecz, Béla; Honti, Frantisek; Costanzo, Michael; Baryshnikova, Anastasia; Gelius-Dietrich, Gabriel; Lercher, Martin J.; Jelasity, Márk; Myers, Chad L.; Andrews, Brenda J.; Boone, Charles; Oliver, Stephen G.; Pál, Csaba; Papp, Balázs

2011-01-01

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. PMID:21623372

Yeast metabolic engineering for hemicellulosic ethanol production

Treesearch

Jennifer Van Vleet; Thomas W. Jeffries

2009-01-01

Efficient fermentation of hemicellulosic sugars is critical for the bioconversion of lignocellulosics to ethanol. Efficient sugar uptake through the heterologous expression of yeast and fungal xylose/glucose transporters can improve fermentation if other metabolic steps are not rate limiting. Rectification of cofactor imbalances through heterologous expression of...
Metabolic regulation and maximal reaction optimization in the central metabolism of a yeast cell

NASA Astrophysics Data System (ADS)

Kasbawati, Gunawan, A. Y.; Hertadi, R.; Sidarto, K. A.

2015-03-01

Regulation of fluxes in a metabolic system aims to enhance the production rates of biotechnologically important compounds. Regulation is held via modification the cellular activities of a metabolic system. In this study, we present a metabolic analysis of ethanol fermentation process of a yeast cell in terms of continuous culture scheme. The metabolic regulation is based on the kinetic formulation in combination with metabolic control analysis to indicate the key enzymes which can be modified to enhance ethanol production. The model is used to calculate the intracellular fluxes in the central metabolism of the yeast cell. Optimal control is then applied to the kinetic model to find the optimal regulation for the fermentation system. The sensitivity results show that there are external and internal control parameters which are adjusted in enhancing ethanol production. As an external control parameter, glucose supply should be chosen in appropriate way such that the optimal ethanol production can be achieved. For the internal control parameter, we find three enzymes as regulation targets namely acetaldehyde dehydrogenase, pyruvate decarboxylase, and alcohol dehydrogenase which reside in the acetaldehyde branch. Among the three enzymes, however, only acetaldehyde dehydrogenase has a significant effect to obtain optimal ethanol production efficiently.
Metabolic engineering of yeast for production of fuels and chemicals.

PubMed

Nielsen, Jens; Larsson, Christer; van Maris, Antonius; Pronk, Jack

2013-06-01

Microbial production of fuels and chemicals from renewable carbohydrate feedstocks offers sustainable and economically attractive alternatives to their petroleum-based production. The yeast Saccharomyces cerevisiae offers many advantages as a platform cell factory for such applications. Already applied on a huge scale for bioethanol production, this yeast is easy to genetically engineer, its physiology, metabolism and genetics have been intensively studied and its robustness enables it to handle harsh industrial conditions. Introduction of novel pathways and optimization of its native cellular processes by metabolic engineering are rapidly expanding its range of cell-factory applications. Here we review recent scientific progress in metabolic engineering of S. cerevisiae for the production of bioethanol, advanced biofuels, and chemicals. Copyright © 2013 Elsevier Ltd. All rights reserved.
Metabolic engineering for improved fermentation of pentoses by yeasts

Treesearch

T. W. Jeffries; Jin. Y.-S.

2004-01-01

The fermentation of xylose is essential for the bioconversion of lignocellulose to fuels and chemicals, but wild-type strains of Saccharomyces cerevisiae do not metabolize xylose, so researchers have engineered xylose metabolism in this yeast. Glucose transporters mediate xylose uptake, but no transporter specific for xylose has yet been identified. Over-expressing...
Reserve carbohydrates metabolism in the yeast Saccharomyces cerevisiae.

PubMed

François, J; Parrou, J L

2001-01-01

Glycogen and trehalose are the two glucose stores of yeast cells. The large variations in the cell content of these two compounds in response to different environmental changes indicate that their metabolism is controlled by complex regulatory systems. In this review we present information on the regulation of the activity of the enzymes implicated in the pathways of synthesis and degradation of glycogen and trehalose as well as on the transcriptional control of the genes encoding them. cAMP and the protein kinases Snf1 and Pho85 appear as major actors in this regulation. From a metabolic point of view, glucose-6-phosphate seems the major effector in the net synthesis of glycogen and trehalose. We discuss also the implication of the recently elucidated TOR-dependent nutrient signalling pathway in the control of the yeast glucose stores and its integration in growth and cell division. The unexpected roles of glycogen and trehalose found in the control of glycolytic flux, stress responses and energy stores for the budding process, demonstrate that their presence confers survival and reproductive advantages to the cell. The findings discussed provide for the first time a teleonomic value for the presence of two different glucose stores in the yeast cell.
Genome and metabolic engineering in non-conventional yeasts: Current advances and applications.

PubMed

Löbs, Ann-Kathrin; Schwartz, Cory; Wheeldon, Ian

2017-09-01

Microbial production of chemicals and proteins from biomass-derived and waste sugar streams is a rapidly growing area of research and development. While the model yeast Saccharomyces cerevisia e is an excellent host for the conversion of glucose to ethanol, production of other chemicals from alternative substrates often requires extensive strain engineering. To avoid complex and intensive engineering of S. cerevisiae, other yeasts are often selected as hosts for bioprocessing based on their natural capacity to produce a desired product: for example, the efficient production and secretion of proteins, lipids, and primary metabolites that have value as commodity chemicals. Even when using yeasts with beneficial native phenotypes, metabolic engineering to increase yield, titer, and production rate is essential. The non-conventional yeasts Kluyveromyces lactis, K. marxianus, Scheffersomyces stipitis, Yarrowia lipolytica, Hansenula polymorpha and Pichia pastoris have been developed as eukaryotic hosts because of their desirable phenotypes, including thermotolerance, assimilation of diverse carbon sources, and high protein secretion. However, advanced metabolic engineering in these yeasts has been limited. This review outlines the challenges of using non-conventional yeasts for strain and pathway engineering, and discusses the developed solutions to these problems and the resulting applications in industrial biotechnology.
Novel Cysteine-Centered Sulfur Metabolic Pathway in the Thermotolerant Methylotrophic Yeast Hansenula polymorpha

PubMed Central

Oh, Doo-Byoung; Kwon, Ohsuk; Lee, Sang Yup; Sibirny, Andriy A.; Kang, Hyun Ah

2014-01-01

In yeast and filamentous fungi, sulfide can be condensed either with O-acetylhomoserine to generate homocysteine, the precursor of methionine, or with O-acetylserine to directly generate cysteine. The resulting homocysteine and cysteine can be interconverted through transsulfuration pathway. Here, we systematically analyzed the sulfur metabolic pathway of the thermotolerant methylotrophic yeast Hansenula polymorpha, which has attracted much attention as an industrial yeast strain for various biotechnological applications. Quite interestingly, the detailed sulfur metabolic pathway of H. polymorpha, which was reconstructed based on combined analyses of the genome sequences and validation by systematic gene deletion experiments, revealed the absence of de novo synthesis of homocysteine from inorganic sulfur in this yeast. Thus, the direct biosynthesis of cysteine from sulfide is the only pathway of synthesizing sulfur amino acids from inorganic sulfur in H. polymorpha, despite the presence of both directions of transsulfuration pathway Moreover, only cysteine, but no other sulfur amino acid, was able to repress the expression of a subset of sulfur genes, suggesting its central and exclusive role in the control of H. polymorpha sulfur metabolism. 35S-Cys was more efficiently incorporated into intracellular sulfur compounds such as glutathione than 35S-Met in H. polymorpha, further supporting the cysteine-centered sulfur pathway. This is the first report on the novel features of H. polymorpha sulfur metabolic pathway, which are noticeably distinct from those of other yeast and filamentous fungal species. PMID:24959887
Attractor Metabolic Networks

PubMed Central

De la Fuente, Ildefonso M.; Cortes, Jesus M.; Pelta, David A.; Veguillas, Juan

2013-01-01

Background The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. Methodology/Principal Findings In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. Conclusions/Significance We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency
Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

PubMed Central

Misra, Ashish; Conway, Matthew F.; Johnnie, Joseph; Qureshi, Tabish M.; Lige, Bao; Derrick, Anne M.; Agbo, Eddy C.; Sriram, Ganesh

2013-01-01

Synthetic biology enables metabolic engineering of industrial microbes to synthesize value-added molecules. In this, a major challenge is the efficient redirection of carbon to the desired metabolic pathways. Pinpointing strategies toward this goal requires an in-depth investigation of the metabolic landscape of the organism, particularly primary metabolism, to identify precursor and cofactor availability for the target compound. The potent antimalarial therapeutic artemisinin and its precursors are promising candidate molecules for production in microbial hosts. Recent advances have demonstrated the production of artemisinin precursors in engineered yeast strains as an alternative to extraction from plants. We report the application of in silico and in vivo metabolic pathway analyses to identify metabolic engineering targets to improve the yield of the direct artemisinin precursor dihydroartemisinic acid (DHA) in yeast. First, in silico extreme pathway (ExPa) analysis identified NADPH-malic enzyme and the oxidative pentose phosphate pathway (PPP) as mechanisms to meet NADPH demand for DHA synthesis. Next, we compared key DHA-synthesizing ExPas to the metabolic flux distributions obtained from in vivo 13C metabolic flux analysis of a DHA-synthesizing strain. This comparison revealed that knocking out ethanol synthesis and overexpressing glucose-6-phosphate dehydrogenase in the oxidative PPP (gene YNL241C) or the NADPH-malic enzyme ME2 (YKL029C) are vital steps toward overproducing DHA. Finally, we employed in silico flux balance analysis and minimization of metabolic adjustment on a yeast genome-scale model to identify gene knockouts for improving DHA yields. The best strategy involved knockout of an oxaloacetate transporter (YKL120W) and an aspartate aminotransferase (YKL106W), and was predicted to improve DHA yields by 70-fold. Collectively, our work elucidates multiple non-trivial metabolic engineering strategies for improving DHA yield in yeast. PMID:23898325
Water-network percolation transitions in hydrated yeast

NASA Astrophysics Data System (ADS)

Sokołowska, Dagmara; Król-Otwinowska, Agnieszka; Mościcki, Józef K.

2004-11-01

We discovered two percolation processes in succession in dc conductivity of bulk baker’s yeast in the course of dehydration. Critical exponents characteristic for the three-dimensional network for heavily hydrated system, and two dimensions in the light hydration limit, evidenced a dramatic change of the water network dimensionality in the dehydration process.
Yeast metabolic engineering--targeting sterol metabolism and terpenoid formation.

PubMed

Wriessnegger, Tamara; Pichler, Harald

2013-07-01

Terpenoids comprise various structures conferring versatile functions to eukaryotes, for example in the form of prenyl-anchors they attach proteins to membranes. The physiology of eukaryotic membranes is fine-tuned by another terpenoid class, namely sterols. Evidence is accumulating that numerous membrane proteins require specific sterol structural features for function. Moreover, sterols are intermediates in the synthesis of steroids serving as hormones in higher eukaryotes. Like steroids many compounds of the terpenoid family do not contribute to membrane architecture, but serve as signalling, protective or attractant/repellent molecules. Particularly plants have developed a plenitude of terpenoid biosynthetic routes branching off early in the sterol biosynthesis pathway and, thereby, forming one of the largest groups of naturally occurring organic compounds. Many of these aromatic and volatile molecules are interesting for industrial application ranging from foods to pharmaceuticals. Combining the fortunate situation that sterol biosynthesis is highly conserved in eukaryotes with the amenability of yeasts to genetic and metabolic engineering, basically all naturally occurring terpenoids might be produced involving yeasts. Such engineered yeasts are useful for the study of biological functions and molecular interactions of terpenoids as well as for the large-scale production of high-value compounds, which are unavailable in sufficient amounts from natural sources due to their low abundance. Copyright © 2013 Elsevier Ltd. All rights reserved.
Advances in metabolic engineering of yeast Saccharomyces cerevisiae for production of chemicals.

PubMed

Borodina, Irina; Nielsen, Jens

2014-05-01

Yeast Saccharomyces cerevisiae is an important industrial host for production of enzymes, pharmaceutical and nutraceutical ingredients and recently also commodity chemicals and biofuels. Here, we review the advances in modeling and synthetic biology tools and how these tools can speed up the development of yeast cell factories. We also present an overview of metabolic engineering strategies for developing yeast strains for production of polymer monomers: lactic, succinic, and cis,cis-muconic acids. S. cerevisiae has already firmly established itself as a cell factory in industrial biotechnology and the advances in yeast strain engineering will stimulate development of novel yeast-based processes for chemicals production. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

PubMed

Vo, Tommy V; Das, Jishnu; Meyer, Michael J; Cordero, Nicolas A; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J; Degatano, Andrew G; Fragoza, Robert; Liu, Lisa G; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P; Pleiss, Jeffrey A; Xia, Yu; Yu, Haiyuan

2016-01-14

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution. Copyright © 2016 Elsevier Inc. All rights reserved.
Metabolic regulation of yeast

NASA Astrophysics Data System (ADS)

Fiechter, A.

1982-12-01

Metabolic regulation which is based on endogeneous and exogeneous process variables which may act constantly or time dependently on the living cell is discussed. The observed phenomena of the regulation are the result of physical, chemical, and biological parameters. These parameters are identified. Ethanol is accumulated as an intermediate product and the synthesis of biomass is reduced. This regulatory effect of glucose is used for the aerobic production of ethanol. Very high production rates are thereby obtained. Understanding of the regulation mechanism of the glucose effect has improved. In addition to catabolite repression, several other mechanisms of enzyme regulation have been described, that are mostly governed by exogeneous factors. Glucose also affects the control of respiration in a third class of yeasts which are unable to make use of ethanol as a substrate for growth. This is due to the lack of any anaplerotic activity. As a consequence, diauxic growth behavior is reduced to a one-stage growth with a drastically reduced cell yield. The pulse chemostat technique, a systematic approach for medium design is developed and medium supplements that are essential for metabolic control are identified.
Dynamical analysis of yeast protein interaction network during the sake brewing process.

PubMed

Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

2011-12-01

Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.
Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies.

PubMed

Vanrolleghem, P A; de Jong-Gubbels, P; van Gulik, W M; Pronk, J T; van Dijken, J P; Heijnen, S

1996-01-01

Setting up a metabolic network model for respiratory growth of Saccharomyces cerevisiae requires the estimation of only two (energetic) stoichiometric parameters: (1) the operational PO ratio and (2) a growth-related maintenance factor k. It is shown, both theoretically and practically, how chemostat cultivations with different mixtures of two substrates allow unique values to be given to these unknowns of the proposed metabolic model. For the yeast and model considered, an effective PO ratio of 1.09 mol of ATP/mol of O (95% confidence interval 1.07-1.11) and a k factor of 0.415 mol of ATP/C-mol of biomass (0.385-0.445) were obtained from biomass substrate yield data on glucose/ethanol mixtures. Symbolic manipulation software proved very valuable in this study as it supported the proof of theoretical identifiability and significantly reduced the necessary computations for parameter estimation. In the transition from 100% glucose to 100% ethanol in the feed, four metabolic regimes occur. Switching between these regimes is determined by cessation of an irreversible reaction and initiation of an alternative reaction. Metabolic network predictions of these metabolic switches compared well with activity measurements of key enzymes. As a second validation of the network, the biomass yield of S. cerevisiae on acetate was also compared to the network prediction. An excellent agreement was found for a network in which acetate transport was modeled with a proton symport, while passive diffusion of acetate gave significantly higher yield predictions.
Yeast aconitase binds and provides metabolically coupled protection to mitochondrial DNA.

PubMed

Chen, Xin Jie; Wang, Xiaowen; Butow, Ronald A

2007-08-21

Aconitase (Aco1p) is a multifunctional protein: It is an enzyme of the tricarboxylic acid cycle. In animal cells, Aco1p also is a cytosolic protein binding to mRNAs to regulate iron metabolism. In yeast, Aco1p was identified as a component of mtDNA nucleoids. Here we show that yeast Aco1p protects mtDNA from excessive accumulation of point mutations and ssDNA breaks and suppresses reductive recombination of mtDNA. Aconitase binds to both ds- and ssDNA, with a preference for GC-containing sequences. Therefore, mitochondria are opportunistic organelles that seize proteins, such as metabolic enzymes, for construction of the nucleoid, an mtDNA maintenance/segregation apparatus.
Integrative network alignment reveals large regions of global network similarity in yeast and human.

PubMed

Kuchaiev, Oleksii; Przulj, Natasa

2011-05-15

High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similarity measures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein-protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7% of proteins in the baker's yeast high-confidence PPI network participate in such a subnetwork that is fully contained in the human high-confidence PPI network. This is the first demonstration that species as diverse as yeast and human contain so large, continuous regions of global network similarity. We apply MI-GRAAL's alignments to predict functions of un-annotated proteins in yeast, human and bacteria validating our predictions in the literature. Furthermore, using network alignment scores for PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship. This is the first time that phylogeny is exactly reconstructed from purely topological alignments of PPI networks. Supplementary files and MI-GRAAL executables: http://bio-nets.doc.ic.ac.uk/MI-GRAAL/.
Engineering yeast metabolism for production of terpenoids for use as perfume ingredients, pharmaceuticals and biofuels.

PubMed

Zhang, Yueping; Nielsen, Jens; Liu, Zihe

2017-12-01

Terpenoids represent a large class of natural products with significant commercial applications. These chemicals are currently mainly obtained through extraction from plants and microbes or through chemical synthesis. However, these sources often face challenges of unsustainability and low productivity. In order to address these issues, Escherichia coli and yeast have been metabolic engineered to produce non-native terpenoids. With recent reports of engineering yeast metabolism to produce several terpenoids at high yields, it has become possible to establish commercial yeast production of terpenoids that find applications as perfume ingredients, pharmaceuticals and advanced biofuels. In this review, we describe the strategies to rewire the yeast pathway for terpenoid biosynthesis. Recent advances will be discussed together with challenges and perspectives of yeast as a cell factory to produce different terpenoids. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Natural and modified promoters for tailored metabolic engineering of the yeast Saccharomyces cerevisiae.

PubMed

Hubmann, Georg; Thevelein, Johan M; Nevoigt, Elke

2014-01-01

The ease of highly sophisticated genetic manipulations in the yeast Saccharomyces cerevisiae has initiated numerous initiatives towards development of metabolically engineered strains for novel applications beyond its traditional use in brewing, baking, and wine making. In fact, baker's yeast has become a key cell factory for the production of various bulk and fine chemicals. Successful metabolic engineering requires fine-tuned adjustments of metabolic fluxes and coordination of multiple pathways within the cell. This has mostly been achieved by controlling gene expression at the transcriptional level, i.e., by using promoters with appropriate strengths and regulatory properties. Here we present an overview of natural and modified promoters, which have been used in metabolic pathway engineering of S. cerevisiae. Recent developments in creating promoters with tailor-made properties are also discussed.

Flor yeasts of Saccharomyces cerevisiae--their ecology, genetics and metabolism.

PubMed

Alexandre, Hervé

2013-10-15

The aging of certain white wines is dependent on the presence of yeast strains that develop a biofilm on the wine surface after the alcoholic fermentation. These strains belong to the genus Saccharomyces and are called flor yeasts. These strains possess distinctive characteristics compared with Saccharomyces cerevisiae fermenting strain. The most important one is their capacity to form a biofilm on the air-liquid interface of the wine. The major gene involved in this phenotype is FLO11, however other genes are also involved in velum formation by these yeast and will be detailed. Other striking features presented in this review are their aneuploidy, and their mitochondrial DNA polymorphism which seems to reflect adaptive evolution of the yeast to a stressful environment where acetaldehyde and ethanol are present at elevated concentration. The biofilm assures access to oxygen and therefore permits continued growth on non-fermentable ethanol. This specific metabolism explains the peculiar organoleptic profile of these wines, especially their content in acetaldehyde and sotolon. This review deals with these different specificities of flor yeasts and will also underline the existing gaps regarding these astonishing yeasts. © 2013.
The response to inositol: regulation of glycerolipid metabolism and stress response signaling in yeast

PubMed Central

Henry, Susan A.; Gaspar, Maria L.; Jesch, Stephen A.

2014-01-01

This article focuses on discoveries of the mechanisms governing the regulation of glycerolipid metabolism and stress response signaling in response to the phospholipid precursor, inositol. The regulation of glycerolipid lipid metabolism in yeast in response to inositol is highly complex, but increasingly well understood, and the roles of individual lipids in stress response are also increasingly well characterized. Discoveries that have emerged over several decades of genetic, molecular and biochemical analyses of metabolic, regulatory and signaling responses of yeast cells, both mutant and wild type, to the availability of the phospholipid precursor, inositol are discussed. PMID:24418527
Bioactive Compounds Derived from the Yeast Metabolism of Aromatic Amino Acids during Alcoholic Fermentation

PubMed Central

Guillamon, Jose Manuel; Torija, Maria Jesus; Beltran, Gemma; Troncoso, Ana M.; Garcia-Parrilla, M. Carmen

2014-01-01

Metabolites resulting from nitrogen metabolism in yeast are currently found in some fermented beverages such as wine and beer. Their study has recently attracted the attention of researchers. Some metabolites derived from aromatic amino acids are bioactive compounds that can behave as hormones or even mimic their role in humans and may also act as regulators in yeast. Although the metabolic pathways for their formation are well known, the physiological significance is still far from being understood. The understanding of this relevance will be a key element in managing the production of these compounds under controlled conditions, to offer fermented food with specific enrichment in these compounds or even to use the yeast as nutritional complements. PMID:24895623
Yeast vitality during cider fermentation: assessment by energy metabolism.

PubMed

Dinsdale, M G; Lloyd, D; McIntyre, P; Jarvis, B

1999-03-15

In an apple juice-based medium, an ethanol-tolerant Australian wine-yeast used for cider manufacture produced more than 10% ethanol over a 5 week period. Growth of the inoculum (10(6) organisms ml(-1)) occurred to a population of 3.1 x 10(7) ml(-1) during the first few days; at the end of the fermentation only 5 x 10(5) yeasts ml(-1) could be recovered as colony-forming units on plates. Respiratory and fermentative activities were measured by mass spectrometric measurements (O2 consumption and CO2 and ethanol production) of washed yeast suspensions taken from the cider fermentation at intervals. Both endogenous and glucose-supported energy-yielding metabolism declined, especially during the first 20 days. Levels of adenine nucleotides also showed decreases after day 1, as did adenylate energy charge, although in a prolonged (16.5 week) fermentation the lowest value calculated was 0.55. AMP was released into the medium. 31P-NMR spectra showed that by comparison with aerobically grown yeast, that from the later stages of the cider fermentation showed little polyphosphate. However, as previously concluded from studies of 'acidification power' and fluorescent oxonol dye exclusion (Dinsdale et al., 1995), repitching of yeast indicated little loss of viability despite considerable loss of vitality.
Stoichiometric network constraints on xylose metabolism by recombinant Saccharomyces cerevisiae

Treesearch

Yong-Su Jin; Thomas W. Jeffries

2004-01-01

Metabolic pathway engineering is constrained by the thermodynamic and stoichiometric feasibility of enzymatic activities of introduced genes. Engineering of xylose metabolism in Saccharomyces cerevisiae has focused on introducing genes for the initial xylose assimilation steps from Pichia stipitis, a xylose-fermenting yeast, into S. cerevisiae, a yeast raditionally...
Dietary live yeast alters metabolic profiles, protein biosynthesis and thermal stress tolerance of Drosophila melanogaster.

PubMed

Colinet, Hervé; Renault, David

2014-04-01

The impact of nutritional factors on insect's life-history traits such as reproduction and lifespan has been excessively examined; however, nutritional determinant of insect's thermal tolerance has not received a lot of attention. Dietary live yeast represents a prominent source of proteins and amino acids for laboratory-reared drosophilids. In this study, Drosophila melanogaster adults were fed on diets supplemented or not with live yeast. We hypothesized that manipulating nutritional conditions through live yeast supplementation would translate into altered physiology and stress tolerance. We verified how live yeast supplementation affected body mass characteristics, total lipids and proteins, metabolic profiles and cold tolerance (acute and chronic stress). Females fed with live yeast had increased body mass and contained more lipids and proteins. Using GC/MS profiling, we found distinct metabolic fingerprints according to nutritional conditions. Metabolite pathway enrichment analysis corroborated that live yeast supplementation was associated with amino acid and protein biosyntheses. The cold assays revealed that the presence of dietary live yeast greatly promoted cold tolerance. Hence, this study conclusively demonstrates a significant interaction between nutritional conditions and thermal tolerance. Copyright © 2014 Elsevier Inc. All rights reserved.
Yeast diversity of sourdoughs and associated metabolic properties and functionalities.

PubMed

De Vuyst, Luc; Harth, Henning; Van Kerrebroeck, Simon; Leroy, Frédéric

2016-12-19

Together with acidifying lactic acid bacteria, yeasts play a key role in the production process of sourdough, where they are either naturally present or added as a starter culture. Worldwide, a diversity of yeast species is encountered, with Saccharomyces cerevisiae, Candida humilis, Kazachstania exigua, Pichia kudriavzevii, Wickerhamomyces anomalus, and Torulaspora delbrueckii among the most common ones. Sourdough-adapted yeasts are able to withstand the stress conditions encountered during their growth, including nutrient starvation as well as the effects of acidic, oxidative, thermal, and osmotic stresses. From a technological point of view, their metabolism primarily contributes to the leavening and flavour of sourdough products. Besides ethanol and carbon dioxide, yeasts can produce metabolites that specifically affect flavour, such as organic acids, diacetyl, higher alcohols from branched-chain amino acids, and esters derived thereof. Additionally, several yeast strains possess functional properties that can potentially lead to nutritional and safety advantages. These properties encompass the production of vitamins, an improvement of the bioavailability of phenolic compounds, the dephosphorylation of phytic acid, the presence of probiotic potential, and the inhibition of fungi and their mycotoxin production. Strains of diverse species are new candidate functional starter cultures, offering opportunities beyond the conventional use of baker's yeast. Copyright © 2016 Elsevier B.V. All rights reserved.
An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

PubMed Central

Lee, Insuk; Li, Zhihua; Marcotte, Edward M.

2007-01-01

Background Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org. PMID:17912365
New Insights into Sulfur Metabolism in Yeasts as Revealed by Studies of Yarrowia lipolytica

PubMed Central

Hébert, Agnès; Forquin-Gomez, Marie-Pierre; Roux, Aurélie; Aubert, Julie; Junot, Christophe; Heilier, Jean-François; Landaud, Sophie; Bonnarme, Pascal

2013-01-01

Yarrowia lipolytica, located at the frontier of hemiascomycetous yeasts and fungi, is an excellent candidate for studies of metabolism evolution. This yeast, widely recognized for its technological applications, in particular produces volatile sulfur compounds (VSCs) that fully contribute to the flavor of smear cheese. We report here a relevant global vision of sulfur metabolism in Y. lipolytica based on a comparison between high- and low-sulfur source supplies (sulfate, methionine, or cystine) by combined approaches (transcriptomics, metabolite profiling, and VSC analysis). The strongest repression of the sulfate assimilation pathway was observed in the case of high methionine supply, together with a large accumulation of sulfur intermediates. A high sulfate supply seems to provoke considerable cellular stress via sulfite production, resulting in a decrease of the availability of the glutathione pathway's sulfur intermediates. The most limited effect was observed for the cystine supply, suggesting that the intracellular cysteine level is more controlled than that of methionine and sulfate. Using a combination of metabolomic profiling and genetic experiments, we revealed taurine and hypotaurine metabolism in yeast for the first time. On the basis of a phylogenetic study, we then demonstrated that this pathway was lost by some of the hemiascomycetous yeasts during evolution. PMID:23220962
Enzymes of yeast polyphosphate metabolism: structure, enzymology and biological roles.

PubMed

Gerasimaitė, Rūta; Mayer, Andreas

2016-02-01

Inorganic polyphosphate (polyP) is found in all living organisms. The known polyP functions in eukaryotes range from osmoregulation and virulence in parasitic protozoa to modulating blood coagulation, inflammation, bone mineralization and cellular signalling in mammals. However mechanisms of regulation and even the identity of involved proteins in many cases remain obscure. Most of the insights obtained so far stem from studies in the yeast Saccharomyces cerevisiae. Here, we provide a short overview of the properties and functions of known yeast polyP metabolism enzymes and discuss future directions for polyP research. © 2016 Authors; published by Portland Press Limited.
Genome-wide expression analyses of the stationary phase model of ageing in yeast.

PubMed

Wanichthanarak, Kwanjeera; Wongtosrad, Nutvadee; Petranovic, Dina

2015-07-01

Ageing processes involved in replicative lifespan (RLS) and chronological lifespan (CLS) have been found to be conserved among many organisms, including in unicellular Eukarya such as yeast Saccharomyces cerevisiae. Here we performed an integrated approach of genome wide expression profiles of yeast at different time points, during growth and starvation. The aim of the study was to identify transcriptional changes in those conditions by using several different computational analyses in order to propose transcription factors, biological networks and metabolic pathways that seem to be relevant during the process of chronological ageing in yeast. Specifically, we performed differential gene expression analysis, gene-set enrichment analysis and network-based analysis, and we identified pathways affected in the stationary phase and specific transcription factors driving transcriptional adaptations. The results indicate signal propagation from G protein-coupled receptors through signaling pathway components and other stress and nutrient-induced transcription factors resulting in adaptation of yeast cells to the lack of nutrients by activating metabolism associated with aerobic metabolism of carbon sources such as ethanol, glycerol and fatty acids. In addition, we found STE12, XBP1 and TOS8 as highly connected nodes in the subnetworks of ageing yeast. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Enhanced leavening ability of baker's yeast by overexpression of SNR84 with PGM2 deletion.

PubMed

Lin, Xue; Zhang, Cui-Ying; Bai, Xiao-Wen; Xiao, Dong-Guang

2015-06-01

Dough-leavening ability is one of the main aspects considered when selecting a baker's yeast strain for baking industry. Generally, modification of maltose metabolic pathway and known regulatory networks of maltose metabolism were used to increase maltose metabolism to improve leavening ability in lean dough. In this study, we focus on the effects of PGM2 (encoding for the phosphoglucomutase) and SNR84 (encoding for the H/ACA snoRNA) that are not directly related to both the maltose metabolic pathway and known regulatory networks of maltose metabolism on the leavening ability of baker's yeast in lean dough. The results show that the modifications on PGM2 and/or SNR84 are effective ways in improving leavening ability of baker's yeast in lean dough. Deletion of PGM2 decreased cellular glucose-1-phosphate and overexpression of SNR84 increased the maltose permease activity. These changes resulted in 11, 19 and 21% increases of the leavening ability for PGM2 deletion, SNR84 overexpression and SNR84 overexpression combining deleted PGM2, respectively.
Integration of Posttranscriptional Gene Networks into Metabolic Adaptation and Biofilm Maturation in Candida albicans

PubMed Central

Harrison, Paul F.; Lo, Tricia L.; Quenault, Tara; Dagley, Michael J.; Bellousoff, Matthew; Powell, David R.; Beilharz, Traude H.; Traven, Ana

2015-01-01

The yeast Candida albicans is a human commensal and opportunistic pathogen. Although both commensalism and pathogenesis depend on metabolic adaptation, the regulatory pathways that mediate metabolic processes in C. albicans are incompletely defined. For example, metabolic change is a major feature that distinguishes community growth of C. albicans in biofilms compared to suspension cultures, but how metabolic adaptation is functionally interfaced with the structural and gene regulatory changes that drive biofilm maturation remains to be fully understood. We show here that the RNA binding protein Puf3 regulates a posttranscriptional mRNA network in C. albicans that impacts on mitochondrial biogenesis, and provide the first functional data suggesting evolutionary rewiring of posttranscriptional gene regulation between the model yeast Saccharomyces cerevisiae and C. albicans. A proportion of the Puf3 mRNA network is differentially expressed in biofilms, and by using a mutant in the mRNA deadenylase CCR4 (the enzyme recruited to mRNAs by Puf3 to control transcript stability) we show that posttranscriptional regulation is important for mitochondrial regulation in biofilms. Inactivation of CCR4 or dis-regulation of mitochondrial activity led to altered biofilm structure and over-production of extracellular matrix material. The extracellular matrix is critical for antifungal resistance and immune evasion, and yet of all biofilm maturation pathways extracellular matrix biogenesis is the least understood. We propose a model in which the hypoxic biofilm environment is sensed by regulators such as Ccr4 to orchestrate metabolic adaptation, as well as the regulation of extracellular matrix production by impacting on the expression of matrix-related cell wall genes. Therefore metabolic changes in biofilms might be intimately linked to a key biofilm maturation mechanism that ultimately results in untreatable fungal disease. PMID:26474309
A protein interaction network analysis for yeast integral membrane protein.

PubMed

Shi, Ming-Guang; Huang, De-Shuang; Li, Xue-Ling

2008-01-01

Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.
Yeast diversity and native vigor for flavor phenotypes.

PubMed

Carrau, Francisco; Gaggero, Carina; Aguilar, Pablo S

2015-03-01

Saccharomyces cerevisiae, the yeast used widely for beer, bread, cider, and wine production, is the most resourceful eukaryotic model used for genetic engineering. A typical concern about using engineered yeasts for food production might be negative consumer perception of genetically modified organisms. However, we believe the true pitfall of using genetically modified yeasts is their limited capacity to either refine or improve the sensory properties of fermented foods under real production conditions. Alternatively, yeast diversity screening to improve the aroma and flavors could offer groundbreaking opportunities in food biotechnology. We propose a 'Yeast Flavor Diversity Screening' strategy which integrates knowledge from sensory analysis and natural whole-genome evolution with information about flavor metabolic networks and their regulation. Copyright © 2015 Elsevier Ltd. All rights reserved.
Randomizing Genome-Scale Metabolic Networks

PubMed Central

Samal, Areejit; Martin, Olivier C.

2011-01-01

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409
The proline metabolism intermediate Δ1-pyrroline-5-carboxylate directly inhibits the mitochondrial respiration in budding yeast.

PubMed

Nishimura, Akira; Nasuno, Ryo; Takagi, Hiroshi

2012-07-30

The proline metabolism intermediate Δ(1)-pyrroline-5-carboxylate (P5C) induces cell death in animals, plants and yeasts. To elucidate how P5C triggers cell death, we analyzed P5C metabolism, mitochondrial respiration and superoxide anion generation in the yeast Saccharomyces cerevisiae. Gene disruption analysis revealed that P5C-mediated cell death was not due to P5C metabolism. Interestingly, deficiency in mitochondrial respiration suppressed the sensitivity of yeast cells to P5C. In addition, we found that P5C inhibits the mitochondrial respiration and induces a burst of superoxide anions from the mitochondria. We propose that P5C regulates cell death via the inhibition of mitochondrial respiration. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Insights on the evolution of metabolic networks of unicellular translationally biased organisms from transcriptomic data and sequence analysis.

PubMed

Carbone, Alessandra; Madden, Richard

2005-10-01

Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.
Effect of Bulk MoS₂ on the Metabolic Profile of Yeast.

PubMed

Yu, Yadong; Yang, Qi; Wu, Na; Tang, Hanlin; Yi, Yanliang; Wang, Gaihong; Ge, Yilin; Zong, Jiajun; Madzak, Catherine; Zhao, Ye; Jiang, Ling; Huang, He

2018-06-01

MoS2, a kind of two-dimensional material with unique performances, has been widely used in many fields. However, an in-depth understanding of its toxicity is still needed, let alone its effects on the environmental microorganism. Herein, we used different methods, including metabolomics technology, to investigate the influence of bulk MoS2 (BMS) on yeast cells. The results indicated that high concentrations (1 mg/L and more) of BMS could destroy cell membrane and induce ROS accumulation. When exposed to a low concentration of BMS (0.1 mg/L), the intracellular concentrations of many metabolites (e.g., fumaric acid, lysine) increased. However, most of their concentrations descended significantly as the yeast cells were treated with BMS of high concentrations (1 mg/L and more). Metabolomics analysis further revealed that exposure to high concentrations of BMS could significantly affect some metabolic pathways such as amino acid and citrate cycle related metabolism. These findings will be beneficial for MoS2 toxicity assessment and further applications.
Thermodynamic Constraints Improve Metabolic Networks.

PubMed

Krumholz, Elias W; Libourel, Igor G L

2017-08-08

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Optimization of a yeast RNA interference system for controlling gene expression and enabling rapid metabolic engineering.

PubMed

Crook, Nathan C; Schmitz, Alexander C; Alper, Hal S

2014-05-16

Reduction of endogenous gene expression is a fundamental operation of metabolic engineering, yet current methods for gene knockdown (i.e., genome editing) remain laborious and slow, especially in yeast. In contrast, RNA interference allows facile and tunable gene knockdown via a simple plasmid transformation step, enabling metabolic engineers to rapidly prototype knockdown strategies in multiple strains before expending significant cost to undertake genome editing. Although RNAi is naturally present in a myriad of eukaryotes, it has only been recently implemented in Saccharomyces cerevisiae as a heterologous pathway and so has not yet been optimized as a metabolic engineering tool. In this study, we elucidate a set of design principles for the construction of hairpin RNA expression cassettes in yeast and implement RNA interference to quickly identify routes for improvement of itaconic acid production in this organism. The approach developed here enables rapid prototyping of knockdown strategies and thus accelerates and reduces the cost of the design-build-test cycle in yeast.
The rate of metabolism as a factor determining longevity of the Saccharomyces cerevisiae yeast.

PubMed

Molon, Mateusz; Szajwaj, Monika; Tchorzewski, Marek; Skoczowski, Andrzej; Niewiadomska, Ewa; Zadrag-Tecza, Renata

2016-02-01

Despite many controversies, the yeast Saccharomyces cerevisiae continues to be used as a model organism for the study of aging. Numerous theories and hypotheses have been created for several decades, yet basic mechanisms of aging have remained unclear. Therefore, the principal aim of this work is to propose a possible mechanism leading to increased longevity in yeast. In this paper, we suggest for the first time that there is a link between decreased metabolic activity, fertility and longevity expressed as time of life in yeast. Determination of reproductive potential and total lifespan with the use of fob1Δ and sfp1Δ mutants allows us to compare the "longevity" presented as the number of produced daughters with the longevity expressed as the time of life. The results of analyses presented in this paper suggest the need for a change in the definition of longevity of yeast by taking into consideration the time parameter. The mutants that have been described as "long-lived" in the literature, such as the fob1Δ mutant, have an increased reproductive potential but live no longer than their standard counterparts. On the other hand, the sfp1Δ mutant and the wild-type strain produce a similar number of daughter cells, but the former lives much longer. Our results demonstrate a correlation between the decreased efficiency of the translational apparatus and the longevity of the sfp1Δ mutant. We suggest that a possible factor regulating the lifespan is the rate of cell metabolism. To measure the basic metabolism of the yeast cells, we used the isothermal microcalorimetry method. In the case of sfp1Δ, the flow of energy, ATP concentration, polysome profile and translational fitness are significantly lower in comparison with the wild-type strain and the fob1Δ mutant.
A Kinetic Modelling of Enzyme Inhibitions in the Central Metabolism of Yeast Cells

NASA Astrophysics Data System (ADS)

Kasbawati; Kalondeng, A.; Aris, N.; Erawaty, N.; Azis, M. I.

2018-03-01

Metabolic regulation plays an important role in the metabolic engineering of a cellular process. It is conducted to improve the productivity of a microbial process by identifying the important regulatory nodes of a metabolic pathway such as fermentation pathway. Regulation of enzymes involved in a particular pathway can be held to improve the productivity of the system. In the central metabolism of yeast cell, some enzymes are known as regulating enzymes that can be inhibited to increase the production of ethanol. In this research we study the kinetic modelling of the enzymes in the central pathway of yeast metabolism by taking into consideration the enzyme inhibition effects to the ethanol production. The existence of positive steady state solution and the stability of the system are also analysed to study the property and dynamical behaviour of the system. One stable steady state of the system is produced if some conditions are fulfilled. The conditions concern to the restriction of the maximum reactions of the enzymes in the pyruvate and acetaldehyde branch points. There exists a certain time of fermentation reaction at which a maximum and a minimum ethanol productions are attained after regulating the system. Optimal ethanol concentration is also produced for a certain initial concentration of inhibitor.
Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

PubMed Central

Hartman, John L.; Stisher, Chandler; Outlaw, Darryl A.; Guo, Jingyu; Shah, Najaf A.; Tian, Dehua; Santos, Sean M.; Rodgers, John W.; White, Richard A.

2015-01-01

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. PMID:25668739
Functional Alignment of Metabolic Networks.

PubMed

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

2016-05-01

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.
Computational architecture of the yeast regulatory network

NASA Astrophysics Data System (ADS)

Maslov, Sergei; Sneppen, Kim

2005-12-01

The topology of regulatory networks contains clues to their overall design principles and evolutionary history. We find that while in- and out-degrees of a given protein in the regulatory network are not correlated with each other, there exists a strong negative correlation between the out-degree of a regulatory protein and in-degrees of its targets. Such correlation positions large regulatory modules on the periphery of the network and makes them rather well separated from each other. We also address the question of relative importance of different classes of proteins quantified by the lethality of null-mutants lacking one of them as well as by the level of their evolutionary conservation. It was found that in the yeast regulatory network highly connected proteins are in fact less important than their low-connected counterparts.
Effects of SNF1 on Maltose Metabolism and Leavening Ability of Baker's Yeast in Lean Dough.

PubMed

Zhang, Cui-Ying; Bai, Xiao-Wen; Lin, Xue; Liu, Xiao-Er; Xiao, Dong-Guang

2015-12-01

Maltose metabolism of baker's yeast (Saccharomyces cerevisiae) in lean dough is negatively influenced by glucose repression, thereby delaying the dough fermentation. To improve maltose metabolism and leavening ability, it is necessary to alleviate glucose repression. The Snf1 protein kinase is well known to be essential for the response to glucose repression and required for transcription of glucose-repressed genes including the maltose-utilization genes (MAL). In this study, the SNF1 overexpression and deletion industrial baker's yeast strains were constructed and characterized in terms of maltose utilization, growth and fermentation characteristics, mRNA levels of MAL genes (MAL62 encoding the maltase and MAL61 encoding the maltose permease) and maltase and maltose permease activities. Our results suggest that overexpression of SNF1 was effective to glucose derepression for enhancing MAL expression levels and enzymes (maltase and maltose permease) activities. These enhancements could result in an 18% increase in maltose metabolism of industrial baker's yeast in LSMLD medium (the low sugar model liquid dough fermentation medium) containing glucose and maltose and a 15% increase in leavening ability in lean dough. These findings provide a valuable insight of breeding industrial baker's yeast for rapid fermentation. © 2015 Institute of Food Technologists®
VRML metabolic network visualizer.

PubMed

Rojdestvenski, Igor

2003-03-01

A successful date collection visualization should satisfy a set of many requirements: unification of diverse data formats, support for serendipity research, support of hierarchical structures, algorithmizability, vast information density, Internet-readiness, and other. Recently, virtual reality has made significant progress in engineering, architectural design, entertainment and communication. We experiment with the possibility of using the immersive abstract three-dimensional visualizations of the metabolic networks. We present the trial Metabolic Network Visualizer software, which produces graphical representation of a metabolic network as a VRML world from a formal description written in a simple SGML-type scripting language.
Real-time measurement of the intracellular pH of yeast cells during glucose metabolism using ratiometric fluorescent nanosensors.

PubMed

Elsutohy, Mohamed M; Chauhan, Veeren M; Markus, Robert; Kyyaly, Mohammed Aref; Tendler, Saul J B; Aylott, Jonathan W

2017-05-11

Intracellular pH is a key parameter that influences many biochemical and metabolic pathways that can also be used as an indirect marker to monitor metabolic and intracellular processes. Herein, we utilise ratiometric fluorescent pH-sensitive nanosensors with an extended dynamic pH range to measure the intracellular pH of yeast (Saccharomyces cerevisiae) during glucose metabolism in real-time. Ratiometric fluorescent pH-sensitive nanosensors consisting of a polyacrylamide nanoparticle matrix covalently linked to two pH-sensitive fluorophores, Oregon green (OG) and 5(6)carboxyfluorescein (FAM), and a reference pH-insensitive fluorophore, 5(6)carboxytetramethylrhodamine (TAMRA), were synthesised. Nanosensors were functionalised with acrylamidopropyltrimethyl ammonium hydrochloride (ACTA) to confer a positive charge to the nanoparticle surfaces that facilitated nanosensor delivery to yeast cells, negating the need to use stress inducing techniques. The results showed that under glucose-starved conditions the intracellular pH of yeast population (n ≈ 200) was 4.67 ± 0.15. Upon addition of d-(+)-glucose (10 mM), this pH value decreased to pH 3.86 ± 0.13 over a period of 10 minutes followed by a gradual rise to a maximal pH of 5.21 ± 0.26, 25 minutes after glucose addition. 45 minutes after the addition of glucose, the intracellular pH of yeast cells returned to that of the glucose starved conditions. This study advances our understanding of the interplay between glucose metabolism and pH regulation in yeast cells, and indicates that the intracellular pH homestasis in yeast is highly regulated and demonstrates the utility of nanosensors for real-time intracellular pH measurements.
Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

PubMed Central

Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

2013-01-01

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546
The Role of Target of Rapamycin Signaling Networks in Plant Growth and Metabolism1

PubMed Central

Sheen, Jen

2014-01-01

The target of rapamycin (TOR) kinase, a master regulator that is evolutionarily conserved among yeasts (Saccharomyces cerevisiae), plants, animals, and humans, integrates nutrient and energy signaling to promote cell proliferation and growth. Recent breakthroughs made possible by integrating chemical, genetic, and genomic analyses have greatly increased our understanding of the molecular functions and dynamic regulation of the TOR kinase in photosynthetic plants. TOR signaling plays fundamental roles in embryogenesis, meristem activation, root and leaf growth, flowering, senescence, and life span determination. The molecular mechanisms underlying TOR-mediated ribosomal biogenesis, translation promotion, readjustment of metabolism, and autophagy inhibition are now being uncovered. Moreover, monitoring photosynthesis-derived Glc and bioenergetics relays has revealed that TOR orchestrates unprecedented transcriptional networks that wire central metabolism and biosynthesis for energy and biomass production. In addition, these networks integrate localized stem/progenitor cell proliferation through interorgan nutrient coordination to control developmental transitions and growth. PMID:24385567
Switching the mode of metabolism in the yeast Saccharomyces cerevisiae

PubMed Central

Otterstedt, Karin; Larsson, Christer; Bill, Roslyn M; Ståhlberg, Anders; Boles, Eckhard; Hohmann, Stefan; Gustafsson, Lena

2004-01-01

The biochemistry of most metabolic pathways is conserved from bacteria to humans, although the control mechanisms are adapted to the needs of each cell type. Oxygen depletion commonly controls the switch from respiration to fermentation. However, Saccharomyces cerevisiae also controls that switch in response to the external glucose level. We have generated an S. cerevisiae strain in which glucose uptake is dependent on a chimeric hexose transporter mediating reduced sugar uptake. This strain shows a fully respiratory metabolism also at high glucose levels as seen for aerobic organisms, and switches to fermentation only when oxygen is lacking. These observations illustrate that manipulating a single step can alter the mode of metabolism. The novel yeast strain is an excellent tool to study the mechanisms underlying glucose-induced signal transduction. PMID:15071495
Expression profiling reveals an unexpected growth-stimulating effect of surplus iron on the yeast Saccharomyces cerevisiae.

PubMed

Du, Yang; Cheng, Wang; Li, Wei-Fang

2012-08-01

Iron homeostasis plays a crucial role in growth and division of cells in all kingdoms of life. Although yeast iron metabolism has been extensively studied, little is known about the molecular mechanism of response to surplus iron. In this study, expression profiling of Saccharomyces cerevisiae in the presence of surplus iron revealed a dual effect at 1 and 4 h. A cluster of stress-responsive genes was upregulated via activation of the stress-resistance transcription factor Msn4, which indicated the stress effect of surplus iron on yeast metabolism. Genes involved in aerobic metabolism and several anabolic pathways are also upregulated in iron-surplus conditions, which could significantly accelerate yeast growth. This dual effect suggested that surplus iron might participate in a more complex metabolic network, in addition to serving as a stress inducer. These findings contribute to our understanding of the global response of yeast to the fluctuating availability of iron in the environment.
Understanding bistability in yeast glycolysis using general properties of metabolic pathways.

PubMed

Planqué, Robert; Bruggeman, Frank J; Teusink, Bas; Hulshof, Josephus

2014-09-01

Glycolysis is the central pathway in energy metabolism in the majority of organisms. In a recent paper, van Heerden et al. showed experimentally and computationally that glycolysis can exist in two states, a global steady state and a so-called imbalanced state. In the imbalanced state, intermediary metabolites accumulate at low levels of ATP and inorganic phosphate. It was shown that Baker's yeast uses a peculiar regulatory mechanism--via trehalose metabolism--to ensure that most yeast cells reach the steady state and not the imbalanced state. Here we explore the apparent bistable behaviour in a core model of glycolysis that is based on a well-established detailed model, and study in great detail the bifurcation behaviour of solutions, without using any numerical information on parameter values. We uncover a rich suite of solutions, including so-called imbalanced states, bistability, and oscillatory behaviour. The techniques employed are generic, directly suitable for a wide class of biochemical pathways, and could lead to better analytical treatments of more detailed models. Copyright © 2014 Elsevier Inc. All rights reserved.
Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571
Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.
Sugar utilization patterns and respiro-fermentative metabolism in the baker's yeast Torulaspora delbrueckii.

PubMed

Alves-Araújo, C; Pacheco, A; Almeida, M J; Spencer-Martins, I; Leão, C; Sousa, M J

2007-03-01

The highly osmo- and cryotolerant yeast species Torulaspora delbrueckii is an important case study among the non-Saccharomyces yeast species. The strain T. delbrueckii PYCC 5321, isolated from traditional corn and rye bread dough in northern Portugal, is considered particularly interesting for the baking industry. This paper reports the sugar utilization patterns of this strain, using media with glucose, maltose and sucrose, alone or in mixtures. Kinetics of growth, biomass and ethanol yields, fermentation and respiration rates, hydrolase activities and sugar uptake rates were used to infer the potential applied relevance of this yeast in comparison to a conventional baker's strain of Saccharomyces cerevisiae. The results showed that both maltase and maltose transport in T. delbrueckii were subject to glucose repression and maltose induction, whereas invertase was subject to glucose control but not dependent on sucrose induction. A comparative analysis of specific sugar consumption rates and transport capacities suggests that the transport step limits both glucose and maltose metabolism. Specific rates of CO(2) production and O(2) consumption showed a significantly higher contribution of respiration to the overall metabolism in T. delbrueckii than in S. cerevisiae. This was reflected in the biomass yields from batch cultures and could represent an asset for the large-scale production of the former species. This work contributes to a better understanding of the physiology of a non-conventional yeast species, with a view to the full exploitation of T. delbrueckii by the baking industry.
A discrete mathematical model applied to genetic regulation and metabolic networks.

PubMed

Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

2007-03-01

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under
Quantitative analysis of chaperone network throughput in budding yeast

PubMed Central

Brownridge, Philip; Lawless, Craig; Payapilly, Aishwarya B; Lanthaler, Karin; Holman, Stephen W; Harman, Victoria M; Grant, Christopher M; Beynon, Robert J; Hubbard, Simon J

2013-01-01

The network of molecular chaperones mediates the folding and translocation of the many proteins encoded in the genome of eukaryotic organisms, as well as a response to stress. It has been particularly well characterised in the budding yeast, Saccharomyces cerevisiae, where 63 known chaperones have been annotated and recent affinity purification and MS/MS experiments have helped characterise the attendant network of chaperone targets to a high degree. In this study, we apply our QconCAT methodology to directly quantify the set of yeast chaperones in absolute terms (copies per cell) via SRM MS. Firstly, we compare these to existing quantitative estimates of these yeast proteins, highlighting differences between approaches. Secondly, we cast the results into the context of the chaperone target network and show a distinct relationship between abundance of individual chaperones and their targets. This allows us to characterise the ‘throughput’ of protein molecules passing through individual chaperones and their groups on a proteome-wide scale in an unstressed model eukaryote for the first time. The results demonstrate specialisations of the chaperone classes, which display different overall workloads, efficiencies and preference for the sub-cellular localisation of their targets. The novel integration of the interactome data with quantification supports re-estimates of the level of protein throughout going through molecular chaperones. Additionally, although chaperones target fewer than 40% of annotated proteins we show that they mediate the folding of the majority of protein molecules (∼62% of the total protein flux in the cell), highlighting their importance. PMID:23420633
Metabolic engineering of a tyrosine-overproducing yeast platform using targeted metabolomics.

PubMed

Gold, Nicholas D; Gowen, Christopher M; Lussier, Francois-Xavier; Cautha, Sarat C; Mahadevan, Radhakrishnan; Martin, Vincent J J

2015-05-28

L-tyrosine is a common precursor for a wide range of valuable secondary metabolites, including benzylisoquinoline alkaloids (BIAs) and many polyketides. An industrially tractable yeast strain optimized for production of L-tyrosine could serve as a platform for the development of BIA and polyketide cell factories. This study applied a targeted metabolomics approach to evaluate metabolic engineering strategies to increase the availability of intracellular L-tyrosine in the yeast Saccharomyces cerevisiae CEN.PK. Our engineering strategies combined localized pathway engineering with global engineering of central metabolism, facilitated by genome-scale steady-state modelling. Addition of a tyrosine feedback resistant version of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase Aro4 from S. cerevisiae was combined with overexpression of either a tyrosine feedback resistant yeast chorismate mutase Aro7, the native pentafunctional arom protein Aro1, native prephenate dehydrogenase Tyr1 or cyclohexadienyl dehydrogenase TyrC from Zymomonas mobilis. Loss of aromatic carbon was limited by eliminating phenylpyruvate decarboxylase Aro10. The TAL gene from Rhodobacter sphaeroides was used to produce coumarate as a simple test case of a heterologous by-product of tyrosine. Additionally, multiple strategies for engineering global metabolism to promote tyrosine production were evaluated using metabolic modelling. The T21E mutant of pyruvate kinase Cdc19 was hypothesized to slow the conversion of phosphoenolpyruvate to pyruvate and accumulate the former as precursor to the shikimate pathway. The ZWF1 gene coding for glucose-6-phosphate dehydrogenase was deleted to create an NADPH deficiency designed to force the cell to couple its growth to tyrosine production via overexpressed NADP(+)-dependent prephenate dehydrogenase Tyr1. Our engineered Zwf1(-) strain expressing TYRC ARO4(FBR) and grown in the presence of methionine achieved an intracellular L-tyrosine accumulation up to 520 �

Metabolic networks are almost nonfractal: a comprehensive evaluation.

PubMed

Takemoto, Kazuhiro

2014-08-01

Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.
Effect of Selenium on Lipid and Amino Acid Metabolism in Yeast Cells.

PubMed

Kieliszek, Marek; Błażejak, Stanisław; Bzducha-Wróbel, Anna; Kot, Anna M

2018-04-19

This article discusses the effect of selenium in aqueous solutions on aspects of lipid and amino acid metabolism in the cell biomass of Saccharomyces cerevisiae MYA-2200 and Candida utilis ATCC 9950 yeasts. The yeast biomass was obtained by using waste products (potato wastewater and glycerol). Selenium, at a dose of 20 mg/L of aqueous solution, affected the differentiation of cellular morphology. Yeast enriched with selenium was characterized by a large functional diversity in terms of protein and amino acid content. The protein content in the biomass of S. cerevisiae enriched with selenium (42.6%) decreased slightly as compared to that in the control sample without additional selenium supplementation (48.4%). Moreover, yeasts of both strains enriched with selenium contained a large amount of glutamic acid, aspartic acid, lysine, and leucine. Analysis of fatty acid profiles in S. cerevisiae yeast supplemented with selenium showed an increase in the unsaturated fatty acid content (e.g., C18:1). The presence of margaric acid (C17:0) and hexadecanoic acid (C17:1) was found in the C. utilis biomass enriched with selenium, in contrast to that of S. cerevisiae. These results indicate that selenium may induce lipid peroxidation, which consequently affects the loss of integrity of the cytoplasmic membrane. Yeast enriched with selenium with optimal amino acid and lipid composition can be used to prepare a novel formula of dietary supplements, which can be applied directly to various diets for both humans and animals.
Network motif frequency vectors reveal evolving metabolic network organisation.

PubMed

Pearcy, Nicole; Crofts, Jonathan J; Chuzhanova, Nadia

2015-01-01

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.
Expression level, cellular compartment and metabolic network position all influence the average selective constraint on mammalian enzymes

PubMed Central

2011-01-01

Background A gene's position in regulatory, protein interaction or metabolic networks can be predictive of the strength of purifying selection acting on it, but these relationships are neither universal nor invariably strong. Following work in bacteria, fungi and invertebrate animals, we explore the relationship between selective constraint and metabolic function in mammals. Results We measure the association between selective constraint, estimated by the ratio of nonsynonymous (Ka) to synonymous (Ks) substitutions, and several, primarily metabolic, measures of gene function. We find significant differences between the selective constraints acting on enzyme-coding genes from different cellular compartments, with the nucleus showing higher constraint than genes from either the cytoplasm or the mitochondria. Among metabolic genes, the centrality of an enzyme in the metabolic network is significantly correlated with Ka/Ks. In contrast to yeasts, gene expression magnitude does not appear to be the primary predictor of selective constraint in these organisms. Conclusions Our results imply that the relationship between selective constraint and enzyme centrality is complex: the strength of selective constraint acting on mammalian genes is quite variable and does not appear to exclusively follow patterns seen in other organisms. PMID:21470417
Metabolism of the Fusarium mycotoxins zearalenone and deoxynivalenol by yeast strains of technological relevance.

PubMed

Böswald, C; Engelhardt, G; Vogel, H; Wallnöfer, P R

1995-01-01

The Fusarium mycotoxin zearalenone (ZEA), added at a level of 2 micrograms/ml, was reduced stereoselectively by cultures of Candida tropicalis, Torulaspora delbrückii, Zygosaccharomyces rouxii, and 7 Saccharomyces strains to both alpha- and beta-zearalenol. In contrast, only alpha-zearalenol was produced from ZEA by Pichia fermentans and several yeast strains of the genera Candida, Hansenula, Brettanomyces, Schizosaccharomyces, and Saccharomycopsis. No glucose conjugates of ZEA (zearalenone-4-beta-D-glucopyranoside) were detected. The trichothecene mycotoxin deoxynivalenol (DON) was not metabolized by any of the yeast strains that were used for analysis.
Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the networkmore » reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.« less
Glycerol metabolism and transport in yeast and fungi: established knowledge and ambiguities.

PubMed

Klein, Mathias; Swinnen, Steve; Thevelein, Johan M; Nevoigt, Elke

2017-03-01

There is huge variability among yeasts with regard to their efficiency in utilizing glycerol as the sole source of carbon and energy. Certain species show growth rates with glycerol comparable to those reached with glucose as carbon source; others are virtually unable to utilize glycerol, especially in synthetic medium. Most of our current knowledge regarding glycerol uptake and catabolic pathways has been gained from studying laboratory strains of the model yeast Saccharomyces cerevisiae. The growth of these strains on glycerol is dependent on the presence of medium supplements such as amino acids and nucleobases. In contrast, there is only fragmentary knowledge about S. cerevisiae isolates able to grow in synthetic glycerol medium without such supplements as well as about growth of non-Saccharomyces yeast species on glycerol. Thus, more research is required to understand why certain strains and species show superior growth performance on glycerol compared with common S. cerevisiae laboratory strains. This mini-review summarizes what is known so far about the gene products and pathways involved in glycerol metabolism and transport in yeast and fungi as well as the regulation of these processes. © 2016 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.
Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks.

PubMed

Chang, Xiao; Wang, Zhuo; Hao, Pei; Li, Yuan-Yuan; Li, Yi-Xue

2010-06-01

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. Copyright 2010 Elsevier Inc. All rights reserved.
Insight into yeast: A study model of lipid metabolism and terpenoid biosynthesis.

PubMed

Hu, Cheng; Lu, Wenyu

2015-01-01

With the development of transcriptomics, metabolomics, proteomics, and mathematical modeling, yeast Saccharomyces cerevisiae is recently considered as a model studying strain by biologists who try to reveal the mystery of microorganic metabolism or develop heterologous pharmaceutical and economic products. Among S. cerevisiae metabolic research, lipid metabolism always attracts great interest because of its dominant role in cell physiology. Related researchers have developed multiple functions from cell membrane component such as adjustment to changing environment and impact on protein folding. Nowadays, many common human diseases such as diabetes mellitus, Alzheimer's disease, obesity, and atherosclerosis are related to lipid metabolism, which makes the study of lipids a desperate need. In addition to lipid metabolism, the study of the native mevalonic acid (MVA) pathway in S. cerevisiae has increased exponentially because of its huge potential to produce economically important products terpenoids. With the progress of technology in gene engineering and metabolic engineering, more and more biosynthetic pathways will be developed and put into industrial application. © 2014 International Union of Biochemistry and Molecular Biology, Inc.
Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong

2014-12-10

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverifiedmore » reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.« less
Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

PubMed Central

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

2014-01-01

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates. PMID:25540776
Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

PubMed

Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R; Jijakli, Kenan; Salehi-Ashtiani, Kourosh

2014-01-01

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
Caloric restriction delays yeast chronological aging by remodeling carbohydrate and lipid metabolism, altering peroxisomal and mitochondrial functionalities, and postponing the onsets of apoptotic and liponecrotic modes of regulated cell death

PubMed Central

Arlia-Ciommo, Anthony; Leonov, Anna; Beach, Adam; Richard, Vincent R.; Bourque, Simon D.; Burstein, Michelle T.; Kyryakov, Pavlo; Gomez-Perez, Alejandra; Koupaki, Olivia; Feldman, Rachel; Titorenko, Vladimir I.

2018-01-01

A dietary regimen of caloric restriction delays aging in evolutionarily distant eukaryotes, including the budding yeast Saccharomyces cerevisiae. Here, we assessed how caloric restriction influences morphological, biochemical and cell biological properties of chronologically aging yeast advancing through different stages of the aging process. Our findings revealed that this low-calorie diet slows yeast chronological aging by mechanisms that coordinate the spatiotemporal dynamics of various cellular processes before entry into a non-proliferative state and after such entry. Caloric restriction causes a stepwise establishment of an aging-delaying cellular pattern by tuning a network that assimilates the following: 1) pathways of carbohydrate and lipid metabolism; 2) communications between the endoplasmic reticulum, lipid droplets, peroxisomes, mitochondria and the cytosol; and 3) a balance between the processes of mitochondrial fusion and fission. Through different phases of the aging process, the caloric restriction-dependent remodeling of this intricate network 1) postpones the age-related onsets of apoptotic and liponecrotic modes of regulated cell death; and 2) actively increases the chance of cell survival by supporting the maintenance of cellular proteostasis. Because caloric restriction decreases the risk of cell death and actively increases the chance of cell survival throughout chronological lifespan, this dietary intervention extends longevity of chronologically aging yeast. PMID:29662634
Triggering Respirofermentative Metabolism in the Crabtree-Negative Yeast Pichia guilliermondii by Disrupting the CAT8 Gene

PubMed Central

Qi, Kai

2014-01-01

Pichia guilliermondii is a Crabtree-negative yeast that does not normally exhibit respirofermentative metabolism under aerobic conditions, and methods to trigger this metabolism may have applications for physiological study and industrial applications. In the present study, CAT8, which encodes a putative global transcriptional activator, was disrupted in P. guilliermondii. This yeast's ethanol titer increased by >20-fold compared to the wild type (WT) during aerobic fermentation using glucose. A comparative transcriptional analysis indicated that the expression of genes in the tricarboxylic acid cycle and respiratory chain was repressed in the CAT8-disrupted (ΔCAT8) strain, while the fermentative pathway genes were significantly upregulated. The respiratory activities in the ΔCAT8 strain, indicated by the specific oxygen uptake rate and respiratory state value, decreased to one-half and one-third of the WT values, respectively. In addition, the expression of HAP4, a transcriptional respiratory activator, was significantly repressed in the ΔCAT8 strain. Through disruption of HAP4, the ethanol production of P. guilliermondii was also increased, but the yield and titer were lower than that in the ΔCAT8 strain. A further transcriptional comparison between ΔCAT8 and ΔHAP4 strains suggested a more comprehensive reprogramming function of Cat8 in the central metabolic pathways. These results indicated the important role of CAT8 in regulating the glucose metabolism of P. guilliermondii and that the regulation was partially mediated by repressing HAP4. The strategy proposed here might be applicable to improve the aerobic fermentation capacity of other Crabtree-negative yeasts. PMID:24747899
Big data mining powers fungal research: recent advances in fission yeast systems biology approaches.

PubMed

Wang, Zhe

2017-06-01

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.
Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

PubMed Central

Chang, Roger L; Ghamsari, Lila; Manichaikul, Ani; Hom, Erik F Y; Balaji, Santhanam; Fu, Weiqi; Shen, Yun; Hao, Tong; Palsson, Bernhard Ø; Salehi-Ashtiani, Kourosh; Papin, Jason A

2011-01-01

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. PMID:21811229
Cell-autonomous mechanisms of chronological aging in the yeast Saccharomyces cerevisiae.

PubMed

Arlia-Ciommo, Anthony; Leonov, Anna; Piano, Amanda; Svistkova, Veronika; Titorenko, Vladimir I

2014-05-27

A body of evidence supports the view that the signaling pathways governing cellular aging - as well as mechanisms of their modulation by longevity-extending genetic, dietary and pharmacological interventions - are conserved across species. The scope of this review is to critically analyze recent advances in our understanding of cell-autonomous mechanisms of chronological aging in the budding yeast Saccharomyces cerevisiae . Based on our analysis, we propose a concept of a biomolecular network underlying the chronology of cellular aging in yeast. The concept posits that such network progresses through a series of lifespan checkpoints. At each of these checkpoints, the intracellular concentrations of some key intermediates and products of certain metabolic pathways - as well as the rates of coordinated flow of such metabolites within an intricate network of intercompartmental communications - are monitored by some checkpoint-specific "master regulator" proteins. The concept envisions that a synergistic action of these master regulator proteins at certain early-life and late-life checkpoints modulates the rates and efficiencies of progression of such processes as cell metabolism, growth, proliferation, stress resistance, macromolecular homeostasis, survival and death. The concept predicts that, by modulating these vital cellular processes throughout lifespan (i.e., prior to an arrest of cell growth and division, and following such arrest), the checkpoint-specific master regulator proteins orchestrate the development and maintenance of a pro- or anti-aging cellular pattern and, thus, define longevity of chronologically aging yeast.
Metabolic Engineering of Oleaginous Yeasts for Fatty Alcohol Production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Wei; Wei, Hui; Knoshaug, Eric

To develop pathways for advanced biological upgrading of sugars to hydrocarbons, we are seeking biological approaches to produce high carbon efficiency intermediates amenable to separations and catalytic upgrading to hydrocarbon fuels. In this study, we successfully demonstrated fatty alcohol production by oleaginous yeasts Yarrowia lipolytica and Lipomyces starkeyi by expressing a bacteria-derived fatty acyl-CoA reductase (FAR). Moreover, we find higher extracellular distribution of fatty alcohols produced by FAR-expressing L. starkeyi strain as compared to Y. lipolytica strain, which would benefit the downstream product recovery process. In both oleaginous yeasts, long chain length saturated fatty alcohols were predominant, accounting for moremore » than 85% of the total fatty alcohols produced. To the best of our knowledge, this is the first report of fatty alcohol production in L. starkeyi. Taken together, our work demonstrates that in addition to Y. lipolytica, L. starkeyi can also serve as a platform organism for production of fatty acid-derived biofuels and bioproducts via metabolic engineering. We believe strain and process development both will significantly contribute to our goal of producing scalable and cost-effective fatty alcohols from renewable biomass.« less
Metabolism of 4-Chloronitrobenzene by the Yeast Rhodosporidium sp

PubMed Central

Corbett, Michael D.; Corbett, Bernadette R.

1981-01-01

The yeast Rhodosporidium sp. metabolized 4-chloronitrobenzene by a reductive pathway to give 4-chloroacetanilide and 4-chloro-2-hydroxyacetanilide as the major final metabolites. The intermediate production of 4-chloronitrosobenzene, 4-chlorophenylhydroxylamine, and 4-chloroaniline was demonstrated by high-pressure liquid chromatography. Additional studies with selected metabolites established that the metabolite 4-chloro-2-hydroxyacetanilide was produced by an initial Bamberger rearrangement of the hydroxylamine metabolite, followed by acetylation. Direct C hydroxylation of the aromatic ring was not observed in this species. No hydroxamic acid production was detected, even though significant concentrations of the nitroso and hydroxylamine precursors to this functional group were observed. PMID:16345757
Inference and Prediction of Metabolic Network Fluxes

PubMed Central

Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

2015-01-01

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

Environmental versatility promotes modularity in genome-scale metabolic networks.

PubMed

Samal, Areejit; Wagner, Andreas; Martin, Olivier C

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple
Fermentation and aerobic metabolism of cellodextrins by yeasts. [Candida wickerhamii; C. guiliermondii; C. molischiana; Debaryomyces polymorphus; Pichia guilliermondii; Clavispora lusitaniae; Kluyveromyces lactis; Brettanomyces claussenii; Rhodotorula minuta; Dekkera intermedia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freer, S.N.

1991-03-01

The fermentation and aerobic metabolism of cellodextrins by 14 yeast species or strains was monitored. When grown aerobically, Candida wickerhamii, C. guilliermondii, and C. molischiana metabolized cellodextrins of degree of polymerization 3 to 6. C. wicherhamii and C. molischiana also fermented these substrates, while C. guilliermondii fermented only cellodextrins of degree of polymerization {<=} 3. Debaryomyces polymorphus, Pichia guilliermondii, Clavispora lusitaniae, and one of two strains of Kluyveromyces lactis metabolized glucose, cellobiose, and cellotriose when grown aerobically. These yeasts also fermented these substrates, except for K. lactis, which fermented only glucose and cellobiose. The remaining species/strains tested, K. lactis, Brettanomycesmore » claussenii, Brettanomyces anomalus, Kluyveromyces dobzhanskii, Rhodotorula minuta, and Dekkera intermedia, both fermented and aerobically metabolized glucose and cellobiose. Crude enzyme preparations from all 14 yeast species or strains were tested for ability to hydrolyze cellotriose and cellotretose. Most of the yeasts produced an enzyme(s) capable of hydrolyzing cellotriose. However, with two exceptions, R. minuta and P. guilliermondii, only the yeasts that metabolized cellodextrins of degree of polymerization >3 produced an enzyme(s) that hydrolyzed cellotretose.« less
Yeast Genetics and Biotechnological Applications

NASA Astrophysics Data System (ADS)

Mishra, Saroj; Baranwal, Richa

Yeast can be recognized as one of the very important groups of microorganisms on account of its extensive use in the fermentation industry and as a basic eukaryotic model cellular system. The yeast Saccharomyces cerevisiae has been extensively used to elucidate the genetics and regulation of several key functions in the cell such as cell mating, electron transport chain, protein trafficking, cell cycle events and others. Even before the genome sequence of the yeast was out, the structural organization and function of several of its genes was known. With the availability of the origin of replication from the 2 μm plasmid and the development of transformation system, it became the host of choice for expression of a number of important proteins. A large number of episomal and integrative shuttle vectors are available for expression of mammalian proteins. The latest developments in genomics and micro-array technology have allowed investigations of individual gene function by site-specific deletion method. The application of metabolic profiling has also assisted in understanding the cellular network operating in this yeast. This chapter is aimed at reviewing the use of this system as an experimental tool for conducting classical genetics. Various vector systems available, foreign genes expressed and the limitations as a host will be discussed. Finally, the use of various yeast enzymes in biotechnology sector will be reviewed.
Metabolic network modeling with model organisms.

PubMed

Yilmaz, L Safak; Walhout, Albertha Jm

2017-02-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. Published by Elsevier Ltd.
Metabolic network modeling with model organisms

PubMed Central

Yilmaz, L. Safak; Walhout, Albertha J.M.

2017-01-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. PMID:28088694
Metabolic brain networks in aging and preclinical Alzheimer's disease.

PubMed

Arnemann, Katelyn L; Stöber, Franziska; Narayan, Sharada; Rabinovici, Gil D; Jagust, William J

2018-01-01

Metabolic brain networks can provide insight into the network processes underlying progression from healthy aging to Alzheimer's disease. We explore the effect of two Alzheimer's disease risk factors, amyloid-β and ApoE ε4 genotype, on metabolic brain networks in cognitively normal older adults (N = 64, ages 69-89) compared to young adults (N = 17, ages 20-30) and patients with Alzheimer's disease (N = 22, ages 69-89). Subjects underwent MRI and PET imaging of metabolism (FDG) and amyloid-β (PIB). Normal older adults were divided into four subgroups based on amyloid-β and ApoE genotype. Metabolic brain networks were constructed cross-sectionally by computing pairwise correlations of metabolism across subjects within each group for 80 regions of interest. We found widespread elevated metabolic correlations and desegregation of metabolic brain networks in normal aging compared to youth and Alzheimer's disease, suggesting that normal aging leads to widespread loss of independent metabolic function across the brain. Amyloid-β and the combination of ApoE ε4 led to less extensive elevated metabolic correlations compared to other normal older adults, as well as a metabolic brain network more similar to youth and Alzheimer's disease. This could reflect early progression towards Alzheimer's disease in these individuals. Altered metabolic brain networks of older adults and those at the highest risk for progression to Alzheimer's disease open up novel lines of inquiry into the metabolic and network processes that underlie normal aging and Alzheimer's disease.
Detecting breakdown points in metabolic networks.

PubMed

Tagore, Somnath; De, Rajat K

2011-12-14

A complex network of biochemical reactions present in an organism generates various biological moieties necessary for its survival. It is seen that biological systems are robust to genetic and environmental changes at all levels of organization. Functions of various organisms are sustained against mutational changes by using alternative pathways. It is also seen that if any one of the paths for production of the same metabolite is hampered, an alternate path tries to overcome this defect and helps in combating the damage. Certain physical, chemical or genetic change in any of the precursor substrate of a biochemical reaction may damage the production of the ultimate product. We employ a quantitative approach for simulating this phenomena of causing a physical change in the biochemical reactions by performing external perturbations to 12 metabolic pathways under carbohydrate metabolism in Saccharomyces cerevisae as well as 14 metabolic pathways under carbohydrate metabolism in Homo sapiens. Here, we investigate the relationship between structure and degree of compatibility of metabolites against external perturbations, i.e., robustness. Robustness can also be further used to identify the extent to which a metabolic pathway can resist a mutation event. Biological networks with a certain connectivity distribution may be very resilient to a particular attack but not to another. The goal of this work is to determine the exact boundary of network breakdown due to both random and targeted attack, thereby analyzing its robustness. We also find that compared to various non-standard models, metabolic networks are exceptionally robust. Here, we report the use of a 'Resilience-based' score for enumerating the concept of 'network-breakdown'. We also use this approach for analyzing metabolite essentiality providing insight into cellular robustness that can be further used for future drug development. We have investigated the behavior of metabolic pathways under carbohydrate
Deciphering transcriptional and metabolic networks associated with lysine metabolism during Arabidopsis seed development.

PubMed

Angelovici, Ruthie; Fait, Aaron; Zhu, Xiaohong; Szymanski, Jedrzej; Feldmesser, Ester; Fernie, Alisdair R; Galili, Gad

2009-12-01

In order to elucidate transcriptional and metabolic networks associated with lysine (Lys) metabolism, we utilized developing Arabidopsis (Arabidopsis thaliana) seeds as a system in which Lys synthesis could be stimulated developmentally without application of chemicals and coupled this to a T-DNA insertion knockout mutation impaired in Lys catabolism. This seed-specific metabolic perturbation stimulated Lys accumulation starting from the initiation of storage reserve accumulation. Our results revealed that the response of seed metabolism to the inducible alteration of Lys metabolism was relatively minor; however, that which was observable operated in a modular manner. They also demonstrated that Lys metabolism is strongly associated with the operation of the tricarboxylic acid cycle while largely disconnected from other metabolic networks. In contrast, the inducible alteration of Lys metabolism was strongly associated with gene networks, stimulating the expression of hundreds of genes controlling anabolic processes that are associated with plant performance and vigor while suppressing a small number of genes associated with plant stress interactions. The most pronounced effect of the developmentally inducible alteration of Lys metabolism was an induction of expression of a large set of genes encoding ribosomal proteins as well as genes encoding translation initiation and elongation factors, all of which are associated with protein synthesis. With respect to metabolic regulation, the inducible alteration of Lys metabolism was primarily associated with altered expression of genes belonging to networks of amino acids and sugar metabolism. The combined data are discussed within the context of network interactions both between and within metabolic and transcriptional control systems.
A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks.

PubMed

Filho, Humberto A; Machicao, Jeaneth; Bruno, Odemir M

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.
A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks

PubMed Central

Filho, Humberto A.; Machicao, Jeaneth

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology. PMID:29734359
Neural network analysis of electrodynamic activity of yeast cells around 1 kHz

NASA Astrophysics Data System (ADS)

Janca, R.

2011-12-01

This paper deals with data analysis of electrodynamic activity of two mutants of yeast cells, cell cycle of which is synchronized and non-synchronized, respectively. We used data already published by Jelinek et al. and treat them with data mining method based on the multilayer neural network. Intersection of data mining and statistical distribution of the noise shows significant difference between synchronized and non-synchronized yeasts not only in total power, but also discrete frequencies.
"Omics" of Selenium Biology: A Prospective Study of Plasma Proteome Network Before and After Selenized-Yeast Supplementation in Healthy Men.

PubMed

Sinha, Indu; Karagoz, Kubra; Fogle, Rachel L; Hollenbeak, Christopher S; Zea, Arnold H; Arga, Kazim Y; Stanley, Anne E; Hawkes, Wayne C; Sinha, Raghu

2016-04-01

Low selenium levels have been linked to a higher incidence of cancer and other diseases, including Keshan, Chagas, and Kashin-Beck, and insulin resistance. Additionally, muscle and cardiovascular disorders, immune dysfunction, cancer, neurological disorders, and endocrine function have been associated with mutations in genes encoding for selenoproteins. Selenium biology is complex, and a systems biology approach to study global metabolomics, genomics, and/or proteomics may provide important clues to examining selenium-responsive markers in circulation. In the current investigation, we applied a global proteomics approach on plasma samples collected from a previously conducted, double-blinded placebo controlled clinical study, where men were supplemented with selenized-yeast (Se-Yeast; 300 μg/day, 3.8 μmol/day) or placebo-yeast for 48 weeks. Proteomic analysis was performed by iTRAQ on 8 plasma samples from each arm at baseline and 48 weeks. A total of 161 plasma proteins were identified in both arms. Twenty-two proteins were significantly altered following Se-Yeast supplementation and thirteen proteins were significantly changed after placebo-yeast supplementation in healthy men. The differentially expressed proteins were involved in complement and coagulation pathways, immune functions, lipid metabolism, and insulin resistance. Reconstruction and analysis of protein-protein interaction network around selected proteins revealed several hub proteins. One of the interactions suggested by our analysis, PHLD-APOA4, which is involved in insulin resistance, was subsequently validated by Western blot analysis. Our systems approach illustrates a viable platform for investigating responsive proteomic profile in 'before and after' condition following Se-Yeast supplementation. The nature of proteins identified suggests that selenium may play an important role in complement and coagulation pathways, and insulin resistance.
Yeast supplementation altered the metabolic response to a combined viral-bacterial challenge in feedlot heifers

USDA-ARS?s Scientific Manuscript database

Two treatments were evaluated in feedlot heifers to determine the effects of feeding a yeast supplement on metabolic responses to a combined viral-bacterial respiratory disease challenge. Thirty-two beef heifers (325 +/- 19.2 kg) were selected and randomly assigned to one of two treatments: 1) Contr...
Multi-scale modularity and motif distributional effect in metabolic networks.

PubMed

Gao, Shang; Chen, Alan; Rahmani, Ali; Zeng, Jia; Tan, Mehmet; Alhajj, Reda; Rokne, Jon; Demetrick, Douglas; Wei, Xiaohui

2016-01-01

Metabolism is a set of fundamental processes that play important roles in a plethora of biological and medical contexts. It is understood that the topological information of reconstructed metabolic networks, such as modular organization, has crucial implications on biological functions. Recent interpretations of modularity in network settings provide a view of multiple network partitions induced by different resolution parameters. Here we ask the question: How do multiple network partitions affect the organization of metabolic networks? Since network motifs are often interpreted as the super families of evolved units, we further investigate their impact under multiple network partitions and investigate how the distribution of network motifs influences the organization of metabolic networks. We studied Homo sapiens, Saccharomyces cerevisiae and Escherichia coli metabolic networks; we analyzed the relationship between different community structures and motif distribution patterns. Further, we quantified the degree to which motifs participate in the modular organization of metabolic networks.
Single cell assessment of yeast metabolic engineering for enhanced lipid production using Raman and AFM-IR imaging.

PubMed

Kochan, Kamila; Peng, Huadong; Wood, Bayden R; Haritos, Victoria S

2018-01-01

Biodiesel is a valuable renewable fuel made from derivatized fatty acids produced in plants, animals, and oleaginous microbes. Of the latter, yeasts are of special interest due to their wide use in biotechnology, ability to synthesize fatty acids and store large amounts of triacylglycerols while utilizing non-food carbon sources. While yeast efficiently produce lipids, genetic modification and indeed, lipid pathway metabolic engineering, is usually required for cost-effective production. Traditionally, gas chromatography (GC) is used to measure fatty acid production and to track the success of a metabolic engineering strategy in a microbial culture; here we have employed vibrational spectroscopy approaches at population and single cell level of engineered yeast while simultaneously investigating metabolite levels in subcellular structures. Firstly, a strong correlation ( r 2 > 0.99) was established between Fourier transform infrared (FTIR) lipid in intact cells and GC analysis of fatty acid methyl esters in the differently engineered strains. Confocal Raman spectroscopy of individual cells carrying genetic modifications to enhance fatty acid synthesis and lipid accumulation revealed changes to the lipid body (LB), the storage organelle for lipids in yeast, with their number increasing markedly (up to tenfold higher); LB size was almost double in the strain that also expressed a LB stabilizing gene but considerable variation was also noted between cells. Raman spectroscopy revealed a clear trend toward reduced unsaturated fatty acid content in lipids of cells carrying more complex metabolic engineering. Atomic force microscopy-infrared spectroscopy (AFM-IR) analysis of individual cells indicated large differences in subcellular constituents between strains: cells of the most highly engineered strain had elevated lipid and much reduced carbohydrate in their cytoplasm compared with unmodified cells. Vibrational spectroscopy analysis allowed the simultaneous
Steady states and stability in metabolic networks without regulation.

PubMed

Ivanov, Oleksandr; van der Schaft, Arjan; Weissing, Franz J

2016-07-21

Metabolic networks are often extremely complex. Despite intensive efforts many details of these networks, e.g., exact kinetic rates and parameters of metabolic reactions, are not known, making it difficult to derive their properties. Considerable effort has been made to develop theory about properties of steady states in metabolic networks that are valid for any values of parameters. General results on uniqueness of steady states and their stability have been derived with specific assumptions on reaction kinetics, stoichiometry and network topology. For example, deep results have been obtained under the assumptions of mass-action reaction kinetics, continuous flow stirred tank reactors (CFSTR), concordant reaction networks and others. Nevertheless, a general theory about properties of steady states in metabolic networks is still missing. Here we make a step further in the quest for such a theory. Specifically, we study properties of steady states in metabolic networks with monotonic kinetics in relation to their stoichiometry (simple and general) and the number of metabolites participating in every reaction (single or many). Our approach is based on the investigation of properties of the Jacobian matrix. We show that stoichiometry, network topology, and the number of metabolites that participate in every reaction have a large influence on the number of steady states and their stability in metabolic networks. Specifically, metabolic networks with single-substrate-single-product reactions have disconnected steady states, whereas in metabolic networks with multiple-substrates-multiple-product reactions manifolds of steady states arise. Metabolic networks with simple stoichiometry have either a unique globally asymptotically stable steady state or asymptotically stable manifolds of steady states. In metabolic networks with general stoichiometry the steady states are not always stable and we provide conditions for their stability. In order to demonstrate the biological
Metabolism and evolution: A comparative study of reconstructed genome-level metabolic networks

NASA Astrophysics Data System (ADS)

Almaas, Eivind

2008-03-01

The availability of high-quality annotations of sequenced genomes has made it possible to generate organism-specific comprehensive maps of cellular metabolism. Currently, more than twenty such metabolic reconstructions are publicly available, with the majority focused on bacteria. A typical metabolic reconstruction for a bacterium results in a complex network containing hundreds of metabolites (nodes) and reactions (links), while some even contain more than a thousand. The constrain-based optimization approach of flux-balance analysis (FBA) is used to investigate the functional characteristics of such large-scale metabolic networks, making it possible to estimate an organism's growth behavior in a wide variety of nutrient environments, as well as its robustness to gene loss. We have recently completed the genome-level metabolic reconstruction of Yersinia pseudotuberculosis, as well as the three Yersinia pestis biovars Antiqua, Mediaevalis, and Orientalis. While Y. pseudotuberculosis typically only causes fever and abdominal pain that can mimic appendicitis, the evolutionary closely related Y. pestis strains are the aetiological agents of the bubonic plague. In this presentation, I will discuss our results and conclusions from a comparative study on the evolution of metabolic function in the four Yersiniae networks using FBA and related techniques, and I will give particular focus to the interplay between metabolic network topology and evolutionary flexibility.
L-arabinose fermenting yeast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Min; Singh, Arjun; Suominen, Pirkko

An L-arabinose utilizing yeast strain is provided for the production of ethanol by introducing and expressing bacterial araA, araB and araD genes. L-arabinose transporters are also introduced into the yeast to enhance the uptake of arabinose. The yeast carries additional genomic mutations enabling it to consume L-arabinose, even as the only carbon source, and to produce ethanol. A yeast strain engineered to metabolize arabinose through a novel pathway is also disclosed. Methods of producing ethanol include utilizing these modified yeast strains.
L-arabinose fermenting yeast

DOEpatents

Zhang, Min; Singh, Arjun; Suominen, Pirkko; Knoshaug, Eric; Franden, Mary Ann; Jarvis, Eric

2014-09-23

An L-arabinose utilizing yeast strain is provided for the production of ethanol by introducing and expressing bacterial araA, araB and araD genes. L-arabinose transporters are also introduced into the yeast to enhance the uptake of arabinose. The yeast carries additional genomic mutations enabling it to consume L-arabinose, even as the only carbon source, and to produce ethanol. A yeast strain engineered to metabolize arabinose through a novel pathway is also disclosed. Methods of producing ethanol include utilizing these modified yeast strains.
Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways

PubMed Central

Kawakami, Eiryo; Singh, Vivek K; Matsubara, Kazuko; Ishii, Takashi; Matsuoka, Yukiko; Hase, Takeshi; Kulkarni, Priya; Siddiqui, Kenaz; Kodilkar, Janhavi; Danve, Nitisha; Subramanian, Indhupriya; Katoh, Manami; Shimizu-Yoshida, Yuki; Ghosh, Samik; Jere, Abhay; Kitano, Hiroaki

2016-01-01

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker’s or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow–tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow–tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems. PMID:28725465

High temporal-resolution view of transcription and chromatin states across distinct metabolic states in budding yeast

PubMed Central

Kuang, Zheng; Cai, Ling; Zhang, Xuekui; Ji, Hongkai; Tu, Benjamin P.; Boeke, Jef D.

2014-01-01

Under continuous, glucose-limited conditions, budding yeast exhibit robust metabolic cycles associated with major oscillations of gene expression. How such fluctuations are linked to changes in chromatin status is not well understood. Here we examine the correlated genome-wide transcription and chromatin states across the yeast metabolic cycle at unprecedented temporal resolution, revealing a “just-in-time supply chain” by which components from specific cellular processes such as ribosome biogenesis become available in a highly coordinated manner. We identify distinct chromatin and splicing patterns associated with different gene categories and determine the relative timing of chromatin modifications to maximal transcription. There is unexpected variation in the chromatin modification and expression relationship, with histone acetylation peaks occurring with varying timing and “sharpness” relative to RNA expression both within and between cycle phases. Chromatin modifier occupancy reveals subtly distinct spatial and temporal patterns compared to the modifications themselves. PMID:25173176
Yeast cell differentiation: Lessons from pathogenic and non-pathogenic yeasts.

PubMed

Palková, Zdena; Váchová, Libuše

2016-09-01

Yeasts, historically considered to be single-cell organisms, are able to activate different differentiation processes. Individual yeast cells can change their life-styles by processes of phenotypic switching such as the switch from yeast-shaped cells to filamentous cells (pseudohyphae or true hyphae) and the transition among opaque, white and gray cell-types. Yeasts can also create organized multicellular structures such as colonies and biofilms, and the latter are often observed as contaminants on surfaces in industry and medical care and are formed during infections of the human body. Multicellular structures are formed mostly of stationary-phase or slow-growing cells that diversify into specific cell subpopulations that have unique metabolic properties and can fulfill specific tasks. In addition to the development of multiple protective mechanisms, processes of metabolic reprogramming that reflect a changed environment help differentiated individual cells and/or community cell constituents to survive harmful environmental attacks and/or to escape the host immune system. This review aims to provide an overview of differentiation processes so far identified in individual yeast cells as well as in multicellular communities of yeast pathogens of the Candida and Cryptococcus spp. and the Candida albicans close relative, Saccharomyces cerevisiae. Molecular mechanisms and extracellular signals potentially involved in differentiation processes are also briefly mentioned. Copyright © 2016 Elsevier Ltd. All rights reserved.
Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

PubMed

Ruppin, Eytan; Papin, Jason A; de Figueiredo, Luis F; Schuster, Stefan

2010-08-01

With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well. Copyright © 2010 Elsevier Ltd. All rights reserved.
Chemical Approaches to Probe Metabolic Networks

PubMed Central

Medina-Cleghorn, Daniel; Nomura, Daniel K.

2013-01-01

One of the more provocative realizations that have come out of the genome sequencing projects is that organisms possess a large number of uncharacterized or poorly characterized enzymes. This finding belies the commonly held notion that our knowledge of cell metabolism is nearly complete, underscoring the vast landscape of unannotated metabolic and signaling networks that operate under normal physiological conditions, let alone in disease states where metabolic networks may be rewired, dysregulated, or altered to drive disease progression. Consequently, the functional annotation of enzymatic pathways represents a grand challenge for researchers in the post-genomic era. This review will highlight the chemical technologies that have been successfully used to characterize metabolism, and put forth some of the challenges we face as we expand our map of metabolic pathways. PMID:23296751
Systematic inference of functional phosphorylation events in yeast metabolism.

PubMed

Chen, Yu; Wang, Yonghong; Nielsen, Jens

2017-07-01

Protein phosphorylation is a post-translational modification that affects proteins by changing their structure and conformation in a rapid and reversible way, and it is an important mechanism for metabolic regulation in cells. Phosphoproteomics enables high-throughput identification of phosphorylation events on metabolic enzymes, but identifying functional phosphorylation events still requires more detailed biochemical characterization. Therefore, development of computational methods for investigating unknown functions of a large number of phosphorylation events identified by phosphoproteomics has received increased attention. We developed a mathematical framework that describes the relationship between phosphorylation level of a metabolic enzyme and the corresponding flux through the enzyme. Using this framework, it is possible to quantitatively estimate contribution of phosphorylation events to flux changes. We showed that phosphorylation regulation analysis, combined with a systematic workflow and correlation analysis, can be used for inference of functional phosphorylation events in steady and dynamic conditions, respectively. Using this analysis, we assigned functionality to phosphorylation events of 17 metabolic enzymes in the yeast Saccharomyces cerevisiae , among which 10 are novel. Phosphorylation regulation analysis cannot only be extended for inference of other functional post-translational modifications but also be a promising scaffold for multi-omics data integration in systems biology. Matlab codes for flux balance analysis in this study are available in Supplementary material. yhwang@ecust.edu.cn or nielsenj@chalmers.se. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
Control of fluxes in metabolic networks

PubMed Central

Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

2016-01-01

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218
Control of fluxes in metabolic networks.

PubMed

Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

2016-07-01

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. © 2016 Basler et al.; Published by Cold Spring Harbor Laboratory Press.
Beyond topology: coevolution of structure and flux in metabolic networks.

PubMed

Morrison, E S; Badyaev, A V

2017-10-01

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals' metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage-bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among-individual variation in flux occurred in networks with the strongest among-compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.
Metabolic flux and nodes control analysis of brewer's yeasts under different fermentation temperature during beer brewing.

PubMed

Yu, Zhimin; Zhao, Haifeng; Zhao, Mouming; Lei, Hongjie; Li, Huiping

2012-12-01

The aim of this work was to further investigate the glycolysis performance of lager and ale brewer's yeasts under different fermentation temperature using a combined analysis of metabolic flux, glycolytic enzyme activities, and flux control. The results indicated that the fluxes through glycolytic pathway decreased with the change of the fermentation temperature from 15 °C to 10 °C, which resulted in the prolonged fermentation times. The maximum activities (V (max)) of hexokinase (HK), phosphofructokinase (PFK), and pyruvate kinase (PK) at key nodes of glycolytic pathway decreased with decreasing fermentation temperature, which was estimated to have different control extent (22-84 %) on the glycolytic fluxes in exponential or flocculent phase. Moreover, the decrease of V (max) of PFK or PK displayed the crucial role in down-regulation of flux in flocculent phase. In addition, the metabolic state of ale strain was more sensitive to the variation of temperature than that of lager strain. The results of the metabolic flux and nodes control analysis in brewer's yeasts under different fermentation temperature may provide an alternative approach to regulate glycolytic flux by changing V (max) and improve the production efficiency and beer quality.
Transcriptional Regulatory Networks in Saccharomyces cerevisiae

NASA Astrophysics Data System (ADS)

Lee, Tong Ihn; Rinaldi, Nicola J.; Robert, François; Odom, Duncan T.; Bar-Joseph, Ziv; Gerber, Georg K.; Hannett, Nancy M.; Harbison, Christopher T.; Thompson, Craig M.; Simon, Itamar; Zeitlinger, Julia; Jennings, Ezra G.; Murray, Heather L.; Gordon, D. Benjamin; Ren, Bing; Wyrick, John J.; Tagne, Jean-Bosco; Volkert, Thomas L.; Fraenkel, Ernest; Gifford, David K.; Young, Richard A.

2002-10-01

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
The m6A methyltransferase Ime4 epitranscriptionally regulates triacylglycerol metabolism and vacuolar morphology in haploid yeast cells.

PubMed

Yadav, Pradeep Kumar; Rajasekharan, Ram

2017-08-18

N 6 -Methyladenosine (m 6 A) is among the most common modifications in eukaryotic mRNA. The role of yeast m 6 A methyltransferase, Ime4, in meiosis and sporulation in diploid strains is very well studied, but its role in haploid strains has remained unknown. Here, with the help of an immunoblotting strategy and Ime4-GFP protein localization studies, we establish the physiological role of Ime4 in haploid cells. Our data showed that Ime4 epitranscriptionally regulates triacylglycerol metabolism and vacuolar morphology through the long-chain fatty acyl-CoA synthetase Faa1, independently of the RNA methylation complex (MIS complex). The MIS complex consists of the Ime4, Mum2, and Slz1 proteins. Our affinity enrichment strategy (methylated RNA immunoprecipitation assays) using m 6 A polyclonal antibodies coupled with mRNA isolation, quantitative real-time PCR, and standard PCR analyses confirmed the presence of m 6 A-modified FAA1 transcripts in haploid yeast cells. The term "epitranscriptional regulation" encompasses the RNA modification-mediated regulation of genes. Moreover, we demonstrate that the Aft2 transcription factor up-regulates FAA1 expression. Because the m 6 A methylation machinery is fundamentally conserved throughout eukaryotes, our findings will help advance the rapidly emerging field of RNA epitranscriptomics. The metabolic link identified here between m 6 A methylation and triacylglycerol metabolism via the Ime4 protein provides new insights into lipid metabolism and the pathophysiology of lipid-related metabolic disorders, such as obesity. Because the yeast vacuole is an analogue of the mammalian lysosome, our findings pave the way to better understand the role of m 6 A methylation in lysosome-related functions and diseases. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.
An insight into the complex prion-prion interaction network in the budding yeast Saccharomyces cerevisiae.

PubMed

Du, Zhiqiang; Valtierra, Stephanie; Li, Liming

2014-01-01

The budding yeast Saccharomyces cerevisiae is a valuable model system for studying prion-prion interactions as it contains multiple prion proteins. A recent study from our laboratory showed that the existence of Swi1 prion ([SWI(+)]) and overproduction of Swi1 can have strong impacts on the formation of 2 other extensively studied yeast prions, [PSI(+)] and [PIN(+)] ([RNQ(+)]) (Genetics, Vol. 197, 685-700). We showed that a single yeast cell is capable of harboring at least 3 heterologous prion elements and these prions can influence each other's appearance positively and/or negatively. We also showed that during the de novo [PSI(+)] formation process upon Sup35 overproduction, the aggregation patterns of a preexisting inducer ([RNQ(+)] or [SWI(+)]) can undergo significant remodeling from stably transmitted dot-shaped aggregates to aggregates that co-localize with the newly formed Sup35 aggregates that are ring/ribbon/rod- shaped. Such co-localization disappears once the newly formed [PSI(+)] prion stabilizes. Our finding provides strong evidence supporting the "cross-seeding" model for prion-prion interactions and confirms earlier reports that the interactions among different prions and their prion proteins mostly occur at the initiation stages of prionogenesis. Our results also highlight a complex prion interaction network in yeast. We believe that elucidating the mechanism underlying the yeast prion-prion interaction network will not only provide insight into the process of prion de novo generation and propagation in yeast but also shed light on the mechanisms that govern protein misfolding, aggregation, and amyloidogenesis in higher eukaryotes.
Storage lipids of yeasts: a survey of nonpolar lipid metabolism in Saccharomyces cerevisiae, Pichia pastoris, and Yarrowia lipolytica.

PubMed

Koch, Barbara; Schmidt, Claudia; Daum, Günther

2014-09-01

Biosynthesis and storage of nonpolar lipids, such as triacylglycerols (TG) and steryl esters (SE), have gained much interest during the last decades because defects in these processes are related to severe human diseases. The baker's yeast Saccharomyces cerevisiae has become a valuable tool to study eukaryotic lipid metabolism because this single-cell microorganism harbors many enzymes and pathways with counterparts in mammalian cells. In this article, we will review aspects of TG and SE metabolism and turnover in the yeast that have been known for a long time and combine them with new perceptions of nonpolar lipid research. We will provide a detailed insight into the mechanisms of nonpolar lipid synthesis, storage, mobilization, and degradation in the yeast S. cerevisiae. The central role of lipid droplets (LD) in these processes will be addressed with emphasis on the prevailing view that this compartment is more than only a depot for TG and SE. Dynamic and interactive aspects of LD with other organelles will be discussed. Results obtained with S. cerevisiae will be complemented by recent investigations of nonpolar lipid research with Yarrowia lipolytica and Pichia pastoris. Altogether, this review article provides a comprehensive view of nonpolar lipid research in yeast. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.
The control of translational accuracy is a determinant of healthy ageing in yeast.

PubMed

von der Haar, Tobias; Leadsham, Jane E; Sauvadet, Aimie; Tarrant, Daniel; Adam, Ilectra S; Saromi, Kofo; Laun, Peter; Rinnerthaler, Mark; Breitenbach-Koller, Hannelore; Breitenbach, Michael; Tuite, Mick F; Gourlay, Campbell W

2017-01-01

Life requires the maintenance of molecular function in the face of stochastic processes that tend to adversely affect macromolecular integrity. This is particularly relevant during ageing, as many cellular functions decline with age, including growth, mitochondrial function and energy metabolism. Protein synthesis must deliver functional proteins at all times, implying that the effects of protein synthesis errors like amino acid misincorporation and stop-codon read-through must be minimized during ageing. Here we show that loss of translational accuracy accelerates the loss of viability in stationary phase yeast. Since reduced translational accuracy also reduces the folding competence of at least some proteins, we hypothesize that negative interactions between translational errors and age-related protein damage together overwhelm the cellular chaperone network. We further show that multiple cellular signalling networks control basal error rates in yeast cells, including a ROS signal controlled by mitochondrial activity, and the Ras pathway. Together, our findings indicate that signalling pathways regulating growth, protein homeostasis and energy metabolism may jointly safeguard accurate protein synthesis during healthy ageing. © 2017 The Authors.
The control of translational accuracy is a determinant of healthy ageing in yeast

PubMed Central

Leadsham, Jane E.; Sauvadet, Aimie; Tarrant, Daniel; Adam, Ilectra S.; Saromi, Kofo; Laun, Peter; Rinnerthaler, Mark; Breitenbach-Koller, Hannelore; Breitenbach, Michael; Tuite, Mick F.; Gourlay, Campbell W.

2017-01-01

Life requires the maintenance of molecular function in the face of stochastic processes that tend to adversely affect macromolecular integrity. This is particularly relevant during ageing, as many cellular functions decline with age, including growth, mitochondrial function and energy metabolism. Protein synthesis must deliver functional proteins at all times, implying that the effects of protein synthesis errors like amino acid misincorporation and stop-codon read-through must be minimized during ageing. Here we show that loss of translational accuracy accelerates the loss of viability in stationary phase yeast. Since reduced translational accuracy also reduces the folding competence of at least some proteins, we hypothesize that negative interactions between translational errors and age-related protein damage together overwhelm the cellular chaperone network. We further show that multiple cellular signalling networks control basal error rates in yeast cells, including a ROS signal controlled by mitochondrial activity, and the Ras pathway. Together, our findings indicate that signalling pathways regulating growth, protein homeostasis and energy metabolism may jointly safeguard accurate protein synthesis during healthy ageing. PMID:28100667
In Situ Analysis of Metabolic Characteristics Reveals the Key Yeast in the Spontaneous and Solid-State Fermentation Process of Chinese Light-Style Liquor

PubMed Central

Kong, Yu; Wu, Qun; Zhang, Yan

2014-01-01

The in situ metabolic characteristics of the yeasts involved in spontaneous fermentation process of Chinese light-style liquor are poorly understood. The covariation between metabolic profiles and yeast communities in Chinese light-style liquor was modeled using the partial least square (PLS) regression method. The diversity of yeast species was evaluated by sequence analysis of the 26S ribosomal DNA (rDNA) D1/D2 domains of cultivable yeasts, and the volatile compounds in fermented grains were analyzed by gas chromatography (GC)-mass spectrometry (MS). Eight yeast species and 58 volatile compounds were identified, respectively. The modulation of 16 of these volatile compounds was associated with variations in the yeast population (goodness of prediction [Q2] > 20%). The results showed that Pichia anomala was responsible for the characteristic aroma of Chinese liquor, through the regulation of several important volatile compounds, such as ethyl lactate, octanoic acid, and ethyl tetradecanoate. Correspondingly, almost all of the compounds associated with P. anomala were detected in a pure culture of this yeast. In contrast to the PLS regression results, however, ethyl lactate and ethyl isobutyrate were not detected in the same pure culture, which indicated that some metabolites could be generated by P. anomala only when it existed in a community with other yeast species. Furthermore, different yeast communities provided different volatile patterns in the fermented grains, which resulted in distinct flavor profiles in the resulting liquors. This study could help identify the key yeast species involved in spontaneous fermentation and provide a deeper understanding of the role of individual yeast species in the community. PMID:24727269
The large-scale organization of metabolic networks

NASA Astrophysics Data System (ADS)

Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L.

2000-10-01

In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.
Yeast cell wall supplementation alters the metabolic responses of crossbred heifers to an endotoxin challenge

USDA-ARS?s Scientific Manuscript database

This study examined the effect of feeding yeast cell wall (YCW) products on the metabolic responses of newly-received heifers to endotoxin challenge. Heifers (n = 24; 219 ± 2.4 kg) were separated into treatment groups receiving a Control diet (n = 8), YCW-A (2.5 grams/heifer/d; n = 8) or YCW-C (2.5 ...
Yeast cell metabolism investigated by CO{_2} production and soft X-ray irradiation

NASA Astrophysics Data System (ADS)

Masini, A.; Batani, D.; Previdi, F.; Milani, M.; Pozzi, A.; Turcu, E.; Huntington, S.; Takeyasu, H.

1999-01-01

Results obtained using a new technique for studying cell metabolism are presented. The technique, consisting in CO2 production monitoring, has been applied to Saccharomyces cerevisiae yeast cells. Also the cells were irradiated using the soft X-ray laser-plasma source at Rutherford Appleton Laboratory with the aim of producing a damage of metabolic processes at the wall level, responsible for fermentation, without great interference with respiration, taking place in mitochondria, and DNA activity. The source was calibrated with PIN diodes and X-ray spectrometers and used Teflon stripes as target, emitting X-rays at about 0.9 keV, with a very low penetration in biological material. X-ray doses delivered to the different cell compartments were calculated following a Lambert-Bouguet-Beer law. Immediately after irradiation, the damage to metabolic activity was measured again by monitoring CO2 production. Results showed a general reduction in gas production by irradiated samples, together with non-linear and non-monotone response to dose. There was also evidence of oscillations in cell metabolic activity and of X-ray induced changes in oscillation frequency.
Integrated analysis, transcriptome-lipidome, reveals the effects of INO-level (INO2 and INO4) on lipid metabolism in yeast.

PubMed

Chumnanpuen, Pramote; Nookaew, Intawat; Nielsen, Jens

2013-10-16

In the yeast Saccharomyces cerevisiae, genes containing UASINO sequences are regulated by the Ino2/Ino4 and Opi1 transcription factors, and this regulation controls lipid biosynthesis. The expression level of INO2 and INO4 genes (INO-level) at different nutrient limited conditions might lead to various responses in yeast lipid metabolism. In this study, we undertook a global study on how INO-levels (transcription level of INO2 and INO4) affect lipid metabolism in yeast and we also studied the effects of single and double deletions of the two INO-genes (deficient effect). Using 2 types of nutrient limitations (carbon and nitrogen) in chemostat cultures operated at a fixed specific growth rate of 0.1 h-1 and strains having different INO-level, we were able to see the effect on expression level of the genes involved in lipid biosynthesis and the fluxes towards the different lipid components. Through combined measurements of the transcriptome, metabolome, and lipidome it was possible to obtain a large dataset that could be used to identify how the INO-level controls lipid metabolism and also establish correlations between the different components. In this study, we undertook a global study on how INO-levels (transcription level of INO2 and INO4) affect lipid metabolism in yeast and we also studied the effects of single and double deletions of the two INO-genes (deficient effect). Using 2 types of nutrient limitations (carbon and nitrogen) in chemostat cultures operated at a fixed specific growth rate of 0.1 h-1 and strains having different INO-level, we were able to see the effect on expression level of the genes involved in lipid biosynthesis and the fluxes towards the different lipid components. Through combined measurements of the transcriptome, metabolome, and lipidome it was possible to obtain a large dataset that could be used to identify how the INO-level controls lipid metabolism and also establish correlations between the different components. Our analysis

Analysis of Growth Inhibition and Metabolism of Hydroxycinnamic Acids by Brewing and Spoilage Strains of Brettanomyces Yeast.

PubMed

Lentz, Michael; Harris, Chad

2015-10-15

Brettanomyces yeasts are well-known as spoilage organisms in both the wine and beer industries, but also contribute important desirable characters to certain beer styles. These properties are mediated in large part by Brettanomyces ' metabolism of hydroxycinnamic acids (HCAs) present in beverage raw materials. Here we compare growth inhibition by, and metabolism of, HCAs among commercial brewing strains and spoilage strains of B. bruxellensis and B. anomalus . These properties vary widely among the different strains tested and between the HCAs analyzed. Brewing strains showed more efficient metabolism of ferulic acid over p -coumaric acid, a trait not shared among the spoilage strains.
Multi-equilibrium property of metabolic networks: SSI module.

PubMed

Lei, Hong-Bo; Zhang, Ji-Feng; Chen, Luonan

2011-06-20

Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.
Scheffersomyces stipitis: a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae

PubMed Central

2012-01-01

Background Scheffersomyces stipitis is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as Saccharomyces cerevisiae, the onset of fermentation in S. stipitis is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though S. stipitis has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of S. stipitis during aerobic growth on glucose under batch and chemostat cultivations. Results Starting from the analysis of physiological data, we confirmed through 13C-based flux analysis the fully respiratory metabolism of S. stipitis when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for S. cerevisiae under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with S. cerevisiae. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through in silico transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways. Conclusions The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree
Developing a set of strong intronic promoters for robust metabolic engineering in oleaginous Rhodotorula (Rhodosporidium) yeast species.

PubMed

Liu, Yanbin; Yap, Sihui Amy; Koh, Chong Mei John; Ji, Lianghui

2016-11-25

Red yeast species in the Rhodotorula/Rhodosporidium genus are outstanding producers of triacylglyceride and cell biomass. Metabolic engineering is expected to further enhance the productivity and versatility of these hosts for the production of biobased chemicals and fuels. Promoters with strong activity during oil-accumulation stage are critical tools for metabolic engineering of these oleaginous yeasts. The upstream DNA sequences of 6 genes involved in lipid biosynthesis or accumulation in Rhodotorula toruloides were studied by luciferase reporter assay. The promoter of perilipin/lipid droplet protein 1 gene (LDP1) displayed much stronger activity (4-11 folds) than that of glyceraldehyde-3-phosphate dehydrogenase gene (GPD1), one of the strongest promoters known in yeasts. Depending on the stage of cultivation, promoter of acetyl-CoA carboxylase gene (ACC1) and fatty acid synthase β subunit gene (FAS1) exhibited intermediate strength, displaying 50-160 and 20-90% levels of GPD1 promoter, respectively. Interestingly, introns significantly modulated promoter strength at high frequency. The incorporation of intron 1 and 2 of LDP1 (LDP1in promoter) enhanced its promoter activity by 1.6-3.0 folds. Similarly, the strength of ACC1 promoter was enhanced by 1.5-3.2 folds if containing intron 1. The intron 1 sequences of ACL1 and FAS1 also played significant regulatory roles. When driven by the intronic promoters of ACC1 and LDP1 (ACC1in and LDP1in promoter, respectively), the reporter gene expression were up-regulated by nitrogen starvation, independent of de novo oil biosynthesis and accumulation. As a proof of principle, overexpression of the endogenous acyl-CoA-dependent diacylglycerol acyltransferase 1 gene (DGA1) by LDP1in promoter was significantly more efficient than GPD1 promoter in enhancing lipid accumulation. Intronic sequences play an important role in regulating gene expression in R. toruloides. Three intronic promoters, LDP1in, ACC1in and FAS1in, are
Promiscuous activities of heterologous enzymes lead to unintended metabolic rerouting in Saccharomyces cerevisiae engineered to assimilate various sugars from renewable biomass.

PubMed

Yun, Eun Ju; Oh, Eun Joong; Liu, Jing-Jing; Yu, Sora; Kim, Dong Hyun; Kwak, Suryang; Kim, Kyoung Heon; Jin, Yong-Su

2018-01-01

Understanding the global metabolic network, significantly perturbed upon promiscuous activities of foreign enzymes and different carbon sources, is crucial for systematic optimization of metabolic engineering of yeast Saccharomyces cerevisiae . Here, we studied the effects of promiscuous activities of overexpressed enzymes encoded by foreign genes on rerouting of metabolic fluxes of an engineered yeast capable of assimilating sugars from renewable biomass by profiling intracellular and extracellular metabolites. Unbiased metabolite profiling of the engineered S. cerevisiae strain EJ4 revealed promiscuous enzymatic activities of xylose reductase and xylitol dehydrogenase on galactose and galactitol, respectively, resulting in accumulation of galactitol and tagatose during galactose fermentation. Moreover, during glucose fermentation, a trisaccharide consisting of glucose accumulated outside of the cells probably owing to the promiscuous and transglycosylation activity of β-glucosidase expressed for hydrolyzing cellobiose. Meanwhile, higher accumulation of fatty acids and secondary metabolites was observed during xylose and cellobiose fermentations, respectively. The heterologous enzymes functionally expressed in S. cerevisiae showed promiscuous activities that led to unintended metabolic rerouting in strain EJ4. Such metabolic rerouting could result in a low yield and productivity of a final product due to the formation of unexpected metabolites. Furthermore, the global metabolic network can be significantly regulated by carbon sources, thus yielding different patterns of metabolite production. This metabolomic study can provide useful information for yeast strain improvement and systematic optimization of yeast metabolism to manufacture bio-based products.
Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moietymore » with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. Finally, we also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.« less
Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

PubMed Central

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-01-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845
Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

DOE PAGES

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-11-21

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moietymore » with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. Finally, we also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.« less
Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks.

PubMed

Haraldsdóttir, Hulda S; Fleming, Ronan M T

2016-11-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.
The connectivity structure, giant strong component and centrality of metabolic networks.

PubMed

Ma, Hong-Wu; Zeng, An-Ping

2003-07-22

Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. http://genome.gbf.de/bioinformatics/
Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

NASA Astrophysics Data System (ADS)

Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

2014-01-01

Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.
Decreased Metabolism in the Posterior Medial Network with Concomitantly Increased Metabolism in the Anterior Temporal Network During Transient Global Amnesia.

PubMed

Yi, SangHak; Park, Young Ho; Jang, Jae-Won; Lim, Jae-Sung; Chun, In Kook; Kim, SangYun

2018-05-01

Perturbation of corticohippocampal circuits is a key step in the pathogenesis of transient global amnesia. We evaluated the spatial distribution of altered cerebral metabolism to determine the location of the corticohippocampal circuits perturbed during the acute stage of transient global amnesia. A consecutive series of 12 patients with transient global amnesia who underwent 18 F-fluorodeoxyglucose positron emission tomography within 3 days after symptom onset was identified. We used statistical parametric mapping with two contrasts to identify regions of decreased and increased brain metabolism in transient global amnesia patients compared with 25 age-matched controls. Transient global amnesia patients showed hypometabolic clusters in the left temporal and bilateral parieto-occipital regions that belong to the posterior medial network as well as, hypermetabolic clusters in the bilateral inferior frontal regions that belong to the anterior temporal network. The posterior medial and anterior temporal networks are the two main corticohippocampal circuits involved in memory-guided behavior. Decreased metabolism in the posterior medial network might explain the impairment of episodic memory observed during the acute stage of transient global amnesia. Concomitant increased metabolism within the anterior temporal network might occur as a compensatory mechanism.
Functional analysis of lipid metabolism genes in wine yeasts during alcoholic fermentation at low temperature

PubMed Central

López-Malo, María; García-Ríos, Estéfani; Chiva, Rosana; Guillamon, José M.

2014-01-01

Wine produced by low-temperature fermentation is mostly considered to have improved sensory qualities. However few commercial wine strains available on the market are well-adapted to ferment at low temperature (10 - 15°C). The lipid metabolism of Saccharomyces cerevisiae plays a central role in low temperature adaptation. One strategy to modify lipid composition is to alter transcriptional activity by deleting or overexpressing the key genes of lipid metabolism. In a previous study, we identified the genes of the phospholipid, sterol and sphingolipid pathways, which impacted on growth capacity at low temperature. In the present study, we aimed to determine the influence of these genes on fermentation performance and growth during low-temperature wine fermentations. We analyzed the phenotype during fermentation at the low and optimal temperature of the lipid mutant and overexpressing strains in the background of a derivative commercial wine strain. The increase in the gene dosage of some of these lipid genes, e.g., PSD1, LCB3, DPL1 and OLE1, improved fermentation activity during low-temperature fermentations, thus confirming their positive role during wine yeast adaptation to cold. Genes whose overexpression improved fermentation activity at 12°C were overexpressed by chromosomal integration into commercial wine yeast QA23. Fermentations in synthetic and natural grape must were carried out by this new set of overexpressing strains. The strains overexpressing OLE1 and DPL1 were able to finish fermentation before commercial wine yeast QA23. Only the OLE1 gene overexpression produced a specific aroma profile in the wines produced with natural grape must. PMID:28357215
Functional analysis of lipid metabolism genes in wine yeasts during alcoholic fermentation at low temperature.

PubMed

López-Malo, María; García-Ríos, Estéfani; Chiva, Rosana; Guillamon, José M

2014-10-29

Wine produced by low-temperature fermentation is mostly considered to have improved sensory qualities. However few commercial wine strains available on the market are well-adapted to ferment at low temperature (10 - 15°C). The lipid metabolism of Saccharomyces cerevisiae plays a central role in low temperature adaptation. One strategy to modify lipid composition is to alter transcriptional activity by deleting or overexpressing the key genes of lipid metabolism. In a previous study, we identified the genes of the phospholipid, sterol and sphingolipid pathways, which impacted on growth capacity at low temperature. In the present study, we aimed to determine the influence of these genes on fermentation performance and growth during low-temperature wine fermentations. We analyzed the phenotype during fermentation at the low and optimal temperature of the lipid mutant and overexpressing strains in the background of a derivative commercial wine strain. The increase in the gene dosage of some of these lipid genes, e.g., PSD1 , LCB3, DPL1 and OLE1, improved fermentation activity during low-temperature fermentations, thus confirming their positive role during wine yeast adaptation to cold. Genes whose overexpression improved fermentation activity at 12°C were overexpressed by chromosomal integration into commercial wine yeast QA23. Fermentations in synthetic and natural grape must were carried out by this new set of overexpressing strains. The strains overexpressing OLE1 and DPL1 were able to finish fermentation before commercial wine yeast QA23. Only the OLE1 gene overexpression produced a specific aroma profile in the wines produced with natural grape must.
Multi-equilibrium property of metabolic networks: SSI module

PubMed Central

2011-01-01

Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module. PMID:21689474
The effect of yeast cell wall supplementation on the metabolic responses of crossbred heifers to endotoxin challenge

USDA-ARS?s Scientific Manuscript database

This study examined the effect of feeding yeast cell wall (YCW) products on the metabolic responses of newly-received heifers to endotoxin (lipopolysaccharide; LPS) challenge. Heifers (n=24; 218.9±2.4 kg) were obtained from commercial sale barns and transported to the Texas Tech University Beef Cent...
The Rewiring of Ubiquitination Targets in a Pathogenic Yeast Promotes Metabolic Flexibility, Host Colonization and Virulence

PubMed Central

Childers, Delma S.; Raziunaite, Ingrida; Mol Avelar, Gabriela; Mackie, Joanna; Budge, Susan; Stead, David; Gow, Neil A. R.; Lenardon, Megan D.; Ballou, Elizabeth R.; MacCallum, Donna M.; Brown, Alistair J. P.

2016-01-01

Efficient carbon assimilation is critical for microbial growth and pathogenesis. The environmental yeast Saccharomyces cerevisiae is “Crabtree positive”, displaying a rapid metabolic switch from the assimilation of alternative carbon sources to sugars. Following exposure to sugars, this switch is mediated by the transcriptional repression of genes (carbon catabolite repression) and the turnover (catabolite inactivation) of enzymes involved in the assimilation of alternative carbon sources. The pathogenic yeast Candida albicans is Crabtree negative. It has retained carbon catabolite repression mechanisms, but has undergone posttranscriptional rewiring such that gluconeogenic and glyoxylate cycle enzymes are not subject to ubiquitin-mediated catabolite inactivation. Consequently, when glucose becomes available, C. albicans can continue to assimilate alternative carbon sources alongside the glucose. We show that this metabolic flexibility promotes host colonization and virulence. The glyoxylate cycle enzyme isocitrate lyase (CaIcl1) was rendered sensitive to ubiquitin-mediated catabolite inactivation in C. albicans by addition of a ubiquitination site. This mutation, which inhibits lactate assimilation in the presence of glucose, reduces the ability of C. albicans cells to withstand macrophage killing, colonize the gastrointestinal tract and cause systemic infections in mice. Interestingly, most S. cerevisiae clinical isolates we examined (67%) have acquired the ability to assimilate lactate in the presence of glucose (i.e. they have become Crabtree negative). These S. cerevisiae strains are more resistant to macrophage killing than Crabtree positive clinical isolates. Moreover, Crabtree negative S. cerevisiae mutants that lack Gid8, a key component of the Glucose-Induced Degradation complex, are more resistant to macrophage killing and display increased virulence in immunocompromised mice. Thus, while Crabtree positivity might impart a fitness advantage for
Metabolic network visualization eliminating node redundance and preserving metabolic pathways

PubMed Central

Bourqui, Romain; Cottret, Ludovic; Lacroix, Vincent; Auber, David; Mary, Patrick; Sagot, Marie-France; Jourdan, Fabien

2007-01-01

Background The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult. Results We propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster. Conclusion The method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism. PMID:17608928
Genomic Evolution of the Ascomycete Yeasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riley, Robert; Haridas, Sajeet; Salamov, Asaf

2015-03-16

Yeasts are important for industrial and biotechnological processes and show remarkable metabolic and phylogenetic diversity despite morphological similarities. We have sequenced the genomes of 16 ascomycete yeasts of taxonomic and industrial importance including members of Saccharomycotina and Taphrinomycotina. Phylogenetic analysis of these and previously published yeast genomes helped resolve the placement of species including Saitoella complicata, Babjeviella inositovora, Hyphopichia burtonii, and Metschnikowia bicuspidata. Moreover, we find that alternative nuclear codon usage, where CUG encodes serine instead of leucine, are monophyletic within the Saccharomycotina. Most of the yeasts have compact genomes with a large fraction of single exon genes, and amore » tendency towards more introns in early-diverging species. Analysis of enzyme phylogeny gives insights into the evolution of metabolic capabilities such as methanol utilization and assimilation of alternative carbon sources.« less
Increased heme synthesis in yeast induces a metabolic switch from fermentation to respiration even under conditions of glucose repression.

PubMed

Zhang, Tiantian; Bu, Pengli; Zeng, Joey; Vancura, Ales

2017-10-13

Regulation of mitochondrial biogenesis and respiration is a complex process that involves several signaling pathways and transcription factors as well as communication between the nuclear and mitochondrial genomes. Under aerobic conditions, the budding yeast Saccharomyces cerevisiae metabolizes glucose predominantly by glycolysis and fermentation. We have recently shown that altered chromatin structure in yeast induces respiration by a mechanism that requires transport and metabolism of pyruvate in mitochondria. However, how pyruvate controls the transcriptional responses underlying the metabolic switch from fermentation to respiration is unknown. Here, we report that this pyruvate effect involves heme. We found that heme induces transcription of HAP4 , the transcriptional activation subunit of the Hap2/3/4/5p complex, required for growth on nonfermentable carbon sources, in a Hap1p- and Hap2/3/4/5p-dependent manner. Increasing cellular heme levels by inactivating ROX1 , which encodes a repressor of many hypoxic genes, or by overexpressing HEM3 or HEM12 induced respiration and elevated ATP levels. Increased heme synthesis, even under conditions of glucose repression, activated Hap1p and the Hap2/3/4/5p complex and induced transcription of HAP4 and genes required for the tricarboxylic acid (TCA) cycle, electron transport chain, and oxidative phosphorylation, leading to a switch from fermentation to respiration. Conversely, inhibiting metabolic flux into the TCA cycle reduced cellular heme levels and HAP4 transcription. Together, our results indicate that the glucose-mediated repression of respiration in budding yeast is at least partly due to the low cellular heme level. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

PubMed

Kruger, Nicholas J; Ratcliffe, R George

2015-01-01

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.
On Functional Module Detection in Metabolic Networks

PubMed Central

Koch, Ina; Ackermann, Jörg

2013-01-01

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145
Analysis of Growth Inhibition and Metabolism of Hydroxycinnamic Acids by Brewing and Spoilage Strains of Brettanomyces Yeast

PubMed Central

Lentz, Michael; Harris, Chad

2015-01-01

Brettanomyces yeasts are well-known as spoilage organisms in both the wine and beer industries, but also contribute important desirable characters to certain beer styles. These properties are mediated in large part by Brettanomyces’ metabolism of hydroxycinnamic acids (HCAs) present in beverage raw materials. Here we compare growth inhibition by, and metabolism of, HCAs among commercial brewing strains and spoilage strains of B. bruxellensis and B. anomalus. These properties vary widely among the different strains tested and between the HCAs analyzed. Brewing strains showed more efficient metabolism of ferulic acid over p-coumaric acid, a trait not shared among the spoilage strains. PMID:28231223
CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism.

PubMed

Karlstädt, Anja; Fliegner, Daniela; Kararigas, Georgios; Ruderisch, Hugo Sanchez; Regitz-Zagrosek, Vera; Holzhütter, Hermann-Georg

2012-08-29

Availability of oxygen and nutrients in the coronary circulation is a crucial determinant of cardiac performance. Nutrient composition of coronary blood may significantly vary in specific physiological and pathological conditions, for example, administration of special diets, long-term starvation, physical exercise or diabetes. Quantitative analysis of cardiac metabolism from a systems biology perspective may help to a better understanding of the relationship between nutrient supply and efficiency of metabolic processes required for an adequate cardiac output. Here we present CardioNet, the first large-scale reconstruction of the metabolic network of the human cardiomyocyte comprising 1793 metabolic reactions, including 560 transport processes in six compartments. We use flux-balance analysis to demonstrate the capability of the network to accomplish a set of 368 metabolic functions required for maintaining the structural and functional integrity of the cell. Taking the maintenance of ATP, biosynthesis of ceramide, cardiolipin and further important phospholipids as examples, we analyse how a changed supply of glucose, lactate, fatty acids and ketone bodies may influence the efficiency of these essential processes. CardioNet is a functionally validated metabolic network of the human cardiomyocyte that enables theorectical studies of cellular metabolic processes crucial for the accomplishment of an adequate cardiac output.
Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.
Genomics and the making of yeast biodiversity.

PubMed

Hittinger, Chris Todd; Rokas, Antonis; Bai, Feng-Yan; Boekhout, Teun; Gonçalves, Paula; Jeffries, Thomas W; Kominek, Jacek; Lachance, Marc-André; Libkind, Diego; Rosa, Carlos A; Sampaio, José Paulo; Kurtzman, Cletus P

2015-12-01

Yeasts are unicellular fungi that do not form fruiting bodies. Although the yeast lifestyle has evolved multiple times, most known species belong to the subphylum Saccharomycotina (syn. Hemiascomycota, hereafter yeasts). This diverse group includes the premier eukaryotic model system, Saccharomyces cerevisiae; the common human commensal and opportunistic pathogen, Candida albicans; and over 1000 other known species (with more continuing to be discovered). Yeasts are found in every biome and continent and are more genetically diverse than angiosperms or chordates. Ease of culture, simple life cycles, and small genomes (∼10-20Mbp) have made yeasts exceptional models for molecular genetics, biotechnology, and evolutionary genomics. Here we discuss recent developments in understanding the genomic underpinnings of the making of yeast biodiversity, comparing and contrasting natural and human-associated evolutionary processes. Only a tiny fraction of yeast biodiversity and metabolic capabilities has been tapped by industry and science. Expanding the taxonomic breadth of deep genomic investigations will further illuminate how genome function evolves to encode their diverse metabolisms and ecologies. Copyright © 2015 Elsevier Ltd. All rights reserved.
Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks.

PubMed

Bardoscia, Marco; Marsili, Matteo; Samal, Areejit

2015-07-01

System-level properties of metabolic networks may be the direct product of natural selection or arise as a by-product of selection on other properties. Here we study the effect of direct selective pressure for growth or viability in particular environments on two properties of metabolic networks: latent versatility to function in additional environments and carbon usage efficiency. Using a Markov chain Monte Carlo (MCMC) sampling based on flux balance analysis (FBA), we sample from a known biochemical universe random viable metabolic networks that differ in the number of directly constrained environments. We find that the latent versatility of sampled metabolic networks increases with the number of directly constrained environments and with the size of the networks. We then show that the average carbon wastage of sampled metabolic networks across the constrained environments decreases with the number of directly constrained environments and with the size of the networks. Our work expands the growing body of evidence about nonadaptive origins of key functional properties of biological networks.
Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure.

PubMed

Milreu, Paulo Vieira; Klein, Cecilia Coimbra; Cottret, Ludovic; Acuña, Vicente; Birmelé, Etienne; Borassi, Michele; Junot, Christophe; Marchetti-Spaccamela, Alberto; Marino, Andrea; Stougie, Leen; Jourdan, Fabien; Crescenzi, Pierluigi; Lacroix, Vincent; Sagot, Marie-France

2014-01-01

The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected. We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications. The code for the method presented in this article is available at http://gobbolino.gforge.inria.fr.
Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

PubMed

Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

2007-12-01

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.
Genomic evolution of the ascomycetous yeasts

USDA-ARS?s Scientific Manuscript database

Yeasts are important for industrial and biotechnological processes and show remarkable metabolic and phylogenetic diversity despite morphological similarities. We have sequenced the genomes of 16 ascomycete yeasts of taxonomic and industrial importance including members of Saccharomycotina and Taphr...
Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

PubMed Central

Martín-Jiménez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; González, Janneth

2017-01-01

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states. PMID:28243200
Metabolic Network Modeling of Microbial Communities

PubMed Central

Biggs, Matthew B.; Medlock, Gregory L.; Kolling, Glynis L.

2015-01-01

Genome-scale metabolic network reconstructions and constraint-based analysis are powerful methods that have the potential to make functional predictions about microbial communities. Current use of genome-scale metabolic networks to characterize the metabolic functions of microbial communities includes species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the “enzyme-soup” approach, multi-scale modeling, and others. There are many challenges inherent to the field, including a need for tools that accurately assign high-level omics signals to individual community members, new automated reconstruction methods that rival manual curation, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be proportional advances in the fields of ecology, health science, and microbial community engineering. PMID:26109480
Metabolism and Regulation of Glycerolipids in the Yeast Saccharomyces cerevisiae

PubMed Central

Henry, Susan A.; Kohlwein, Sepp D.; Carman, George M.

2012-01-01

Due to its genetic tractability and increasing wealth of accessible data, the yeast Saccharomyces cerevisiae is a model system of choice for the study of the genetics, biochemistry, and cell biology of eukaryotic lipid metabolism. Glycerolipids (e.g., phospholipids and triacylglycerol) and their precursors are synthesized and metabolized by enzymes associated with the cytosol and membranous organelles, including endoplasmic reticulum, mitochondria, and lipid droplets. Genetic and biochemical analyses have revealed that glycerolipids play important roles in cell signaling, membrane trafficking, and anchoring of membrane proteins in addition to membrane structure. The expression of glycerolipid enzymes is controlled by a variety of conditions including growth stage and nutrient availability. Much of this regulation occurs at the transcriptional level and involves the Ino2–Ino4 activation complex and the Opi1 repressor, which interacts with Ino2 to attenuate transcriptional activation of UASINO-containing glycerolipid biosynthetic genes. Cellular levels of phosphatidic acid, precursor to all membrane phospholipids and the storage lipid triacylglycerol, regulates transcription of UASINO-containing genes by tethering Opi1 to the nuclear/endoplasmic reticulum membrane and controlling its translocation into the nucleus, a mechanism largely controlled by inositol availability. The transcriptional activator Zap1 controls the expression of some phospholipid synthesis genes in response to zinc availability. Regulatory mechanisms also include control of catalytic activity of glycerolipid enzymes by water-soluble precursors, products and lipids, and covalent modification of phosphorylation, while in vivo function of some enzymes is governed by their subcellular location. Genome-wide genetic analysis indicates coordinate regulation between glycerolipid metabolism and a broad spectrum of metabolic pathways. PMID:22345606
Regulation of NAD+ metabolism, signaling and compartmentalization in the yeast Saccharomyces cerevisiae

PubMed Central

Kato, Michiko; Lin, Su-Ju

2014-01-01

Pyridine nucleotides are essential coenzymes in many cellular redox reactions in all living systems. In addition to functioning as a redox carrier, NAD+ is also a required co-substrate for the conserved sirtuin deacetylases. Sirtuins regulate transcription, genome maintenance and metabolism and function as molecular links between cells and their environment. Maintaining NAD+ homeostasis is essential for proper cellular function and aberrant NAD+ metabolism has been implicated in a number of metabolic- and age-associated diseases. Recently, NAD+ metabolism has been linked to the phosphate-responsive signaling pathway (PHO pathway) in the budding yeast Saccharomyces cerevisiae. Activation of the PHO pathway is associated with the production and mobilization of the NAD+ metabolite nicotinamide riboside (NR), which is mediated in part by PHO-regulated nucleotidases. Cross-regulation between NAD+ metabolism and the PHO pathway has also been reported; however, detailed mechanisms remain to be elucidated. The PHO pathway also appears to modulate the activities of common downstream effectors of multiple nutrient-sensing pathways (Ras-PKA, TOR, Sch9/AKT). These signaling pathways were suggested to play a role in calorie restriction-mediated beneficial effects, which have also been linked to Sir2 function and NAD+ metabolism. Here, we discuss the interactions of these pathways and their potential roles in regulating NAD+ metabolism. In eukaryotic cells, intracellular compartmentalization facilitates the regulation of enzymatic functions and also concentrates or sequesters specific metabolites. Various NAD+-mediated cellular functions such as mitochondrial oxidative phosphorylation are compartmentalized. Therefore, we also discuss several key players functioning in mitochondrial, cytosolic and vacuolar compartmentalization of NAD+ intermediates, and their potential roles in NAD+ homeostasis. To date, it remains unclear how NAD+ and NAD+ intermediates shuttle between different
Can Yeast (S. cerevisiae) Metabolic Volatiles Provide Polymorphic Signaling?

PubMed Central

Arguello, J. Roman; Sellanes, Carolina; Lou, Yann Ru; Raguso, Robert A.

2013-01-01

Chemical signaling between organisms is a ubiquitous and evolutionarily dynamic process that helps to ensure mate recognition, location of nutrients, avoidance of toxins, and social cooperation. Evolutionary changes in chemical communication systems progress through natural variation within the organism generating the signal as well as the responding individuals. A promising yet poorly understood system with which to probe the importance of this variation exists between D. melanogaster and S. cerevisiae. D. melanogaster relies on yeast for nutrients, while also serving as a vector for yeast cell dispersal. Both are outstanding genetic and genomic models, with Drosophila also serving as a preeminent model for sensory neurobiology. To help develop these two genetic models as an ecological model, we have tested if - and to what extent - S. cerevisiae is capable of producing polymorphic signaling through variation in metabolic volatiles. We have carried out a chemical phenotyping experiment for 14 diverse accessions within a common garden random block design. Leveraging genomic sequences for 11 of the accessions, we ensured a genetically broad sample and tested for phylogenetic signal arising from phenotypic dataset. Our results demonstrate that significant quantitative differences for volatile blends do exist among S. cerevisiae accessions. Of particular ecological relevance, the compounds driving the blend differences (acetoin, 2-phenyl ethanol and 3-methyl-1-butanol) are known ligands for D. melanogasters chemosensory receptors, and are related to sensory behaviors. Though unable to correlate the genetic and volatile measurements, our data point clear ways forward for behavioral assays aimed at understanding the implications of this variation. PMID:23990899
Combined zebrafish-yeast chemical-genetic screens reveal gene-copper-nutrition interactions that modulate melanocyte pigmentation.

PubMed

Ishizaki, Hironori; Spitzer, Michaela; Wildenhain, Jan; Anastasaki, Corina; Zeng, Zhiqiang; Dolma, Sonam; Shaw, Michael; Madsen, Erik; Gitlin, Jonathan; Marais, Richard; Tyers, Mike; Patton, E Elizabeth

2010-01-01

Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu(2+)) transporter ATP7A in Menkes disease, or an inability to absorb copper after gastric surgery. However, many causes of copper deficiency are unknown, and genetic polymorphisms might underlie sensitivity to suboptimal environmental copper conditions. Here, we combined phenotypic screens in zebrafish for compounds that affect copper metabolism with yeast chemical-genetic profiles to identify pathways that are sensitive to copper depletion. Yeast chemical-genetic interactions revealed that defects in intracellular trafficking pathways cause sensitivity to low-copper conditions; partial knockdown of the analogous Ap3s1 and Ap1s1 trafficking components in zebrafish sensitized developing melanocytes to hypopigmentation in low-copper environmental conditions. Because trafficking pathways are essential for copper loading into cuproproteins, our results suggest that hypomorphic alleles of trafficking components might underlie sensitivity to reduced-copper nutrient conditions. In addition, we used zebrafish-yeast screening to identify a novel target pathway in copper metabolism for the small-molecule MEK kinase inhibitor U0126. The zebrafish-yeast screening method combines the power of zebrafish as a disease model with facile genome-scale identification of chemical-genetic interactions in yeast to enable the discovery and dissection of complex multigenic interactions in disease-gene networks.
Comparative genomics of biotechnologically important yeasts

USDA-ARS?s Scientific Manuscript database

Ascomycete yeasts are metabolically diverse, with great potential for biotechnology. Here, we report the comparative genome analysis of 29 taxonomically and biotechnologically important yeasts, including 16 newly sequenced. We identify a genetic code change, CUG-Ala, in Pachysolen tannophilus in the...
Carbohydrate and energy-yielding metabolism in non-conventional yeasts.

PubMed

Flores, C L; Rodríguez, C; Petit, T; Gancedo, C

2000-10-01

Sugars are excellent carbon sources for all yeasts. Since a vast amount of information is available on the components of the pathways of sugar utilization in Saccharomyces cerevisiae it has been tacitly assumed that other yeasts use sugars in the same way. However, although the pathways of sugar utilization follow the same theme in all yeasts, important biochemical and genetic variations on it exist. Basically, in most non-conventional yeasts, in contrast to S. cerevisiae, respiration in the presence of oxygen is prominent for the use of sugars. This review provides comparative information on the different steps of the fundamental pathways of sugar utilization in non-conventional yeasts: glycolysis, fermentation, tricarboxylic acid cycle, pentose phosphate pathway and respiration. We consider also gluconeogenesis and, briefly, catabolite repression. We have centered our attention in the genera Kluyveromyces, Candida, Pichia, Yarrowia and Schizosaccharomyces, although occasional reference to other genera is made. The review shows that basic knowledge is missing on many components of these pathways and also that studies on regulation of critical steps are scarce. Information on these points would be important to generate genetically engineered yeast strains for certain industrial uses.
Yeast biotechnology: teaching the old dog new tricks.

PubMed

Mattanovich, Diethard; Sauer, Michael; Gasser, Brigitte

2014-03-06

Yeasts are regarded as the first microorganisms used by humans to process food and alcoholic beverages. The technology developed out of these ancient processes has been the basis for modern industrial biotechnology. Yeast biotechnology has gained great interest again in the last decades. Joining the potentials of genomics, metabolic engineering, systems and synthetic biology enables the production of numerous valuable products of primary and secondary metabolism, technical enzymes and biopharmaceutical proteins. An overview of emerging and established substrates and products of yeast biotechnology is provided and discussed in the light of the recent literature.
Phylogeny of metabolic networks: a spectral graph theoretical approach.

PubMed

Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

2015-10-01

Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

Primary Metabolism and Medium-Chain Fatty Acid Alterations Precede Long-Chain Fatty Acid Changes Impacting Neutral Lipid Metabolism in Response to an Anticancer Lysophosphatidylcholine Analogue in Yeast.

PubMed

Tambellini, Nicolas P; Zaremberg, Vanina; Krishnaiah, Saikumari; Turner, Raymond J; Weljie, Aalim M

2017-10-06

The nonmetabolizable lysophosphatidylcholine (LysoPC) analogue edelfosine is the prototype of a class of compounds being investigated for their potential as selective chemotherapeutic agents. Edelfosine targets membranes, disturbing cellular homeostasis. Is not clear at this point how membrane alterations are communicated between intracellular compartments leading to growth inhibition and eventual cell death. In the present study, a combined metabolomics/lipidomics approach for the unbiased identification of metabolic pathways altered in yeast treated with sublethal concentrations of the LysoPC analogue was employed. Mass spectrometry of polar metabolites, fatty acids, and lipidomic profiling was used to study the effects of edelfosine on yeast metabolism. Amino acid and sugar metabolism, the Krebs cycle, and fatty acid profiles were most disrupted, with polar metabolites and short-medium chain fatty acid changes preceding long and very long-chain fatty acid variations. Initial increases in metabolites such as trehalose, proline, and γ-amino butyric acid with a concomitant decrease in metabolites of the Krebs cycle, citrate and fumarate, are interpreted as a cellular attempt to offset oxidative stress in response to mitochondrial dysfunction induced by the treatment. Notably, alanine, inositol, and myristoleic acid showed a steady increase during the period analyzed (2, 4, and 6 h after treatment). Of importance was the finding that edelfosine induced significant alterations in neutral glycerolipid metabolism resulting in a significant increase in the signaling lipid diacylglycerol.
Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

DOE PAGES

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant; ...

2017-03-08

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We appliedmore » GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.« less
Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We appliedmore » GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.« less
Stabilizing Motifs in Autonomous Boolean Networks and the Yeast Cell Cycle Oscillator

NASA Astrophysics Data System (ADS)

Sevim, Volkan; Gong, Xinwei; Socolar, Joshua

2009-03-01

Synchronously updated Boolean networks are widely used to model gene regulation. Some properties of these model networks are known to be artifacts of the clocking in the update scheme. Autonomous updating is a less artificial scheme that allows one to introduce small timing perturbations and study stability of the attractors. We argue that the stabilization of a limit cycle in an autonomous Boolean network requires a combination of motifs such as feed-forward loops and auto-repressive links that can correct small fluctuations in the timing of switching events. A recently published model of the transcriptional cell-cycle oscillator in yeast contains the motifs necessary for stability under autonomous updating [1]. [1] D. A. Orlando, et al. Nature (London), 4530 (7197):0 944--947, 2008.
Metabolic networks in epilepsy by MR spectroscopic imaging.

PubMed

Pan, J W; Spencer, D D; Kuzniecky, R; Duckrow, R B; Hetherington, H; Spencer, S S

2012-12-01

The concept of an epileptic network has long been suggested from both animal and human studies of epilepsy. Based on the common observation that the MR spectroscopic imaging measure of NAA/Cr is sensitive to neuronal function and injury, we use this parameter to assess for the presence of a metabolic network in mesial temporal lobe epilepsy (MTLE) patients. A multivariate factor analysis is performed with controls and MTLE patients, using NAA/Cr measures from 12 loci: the bilateral hippocampi, thalami, basal ganglia, and insula. The factor analysis determines which and to what extent these loci are metabolically covarying. We extract two independent factors that explain the data's variability in control and MTLE patients. In controls, these factors characterize a 'thalamic' and 'dominant subcortical' function. The MTLE patients also exhibit a 'thalamic' factor, in addition to a second factor involving the ipsilateral insula and bilateral basal ganglia. These data suggest that MTLE patients demonstrate a metabolic network that involves the thalami, also seen in controls. The MTLE patients also display a second set of metabolically covarying regions that may be a manifestation of the epileptic network that characterizes limbic seizure propagation. © 2012 John Wiley & Sons A/S.
Dynamic optimization of metabolic networks coupled with gene expression.

PubMed

Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

2015-01-21

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright �
MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

PubMed Central

2010-01-01

Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly
Phenotypic Landscape of Saccharomyces cerevisiae during Wine Fermentation: Evidence for Origin-Dependent Metabolic Traits

PubMed Central

Camarasa, Carole; Sanchez, Isabelle; Brial, Pascale; Bigey, Frédéric; Dequin, Sylvie

2011-01-01

The species Saccharomyces cerevisiae includes natural strains, clinical isolates, and a large number of strains used in human activities. The aim of this work was to investigate how the adaptation to a broad range of ecological niches may have selectively shaped the yeast metabolic network to generate specific phenotypes. Using 72 S. cerevisiae strains collected from various sources, we provide, for the first time, a population-scale picture of the fermentative metabolic traits found in the S. cerevisiae species under wine making conditions. Considerable phenotypic variation was found suggesting that this yeast employs diverse metabolic strategies to face environmental constraints. Several groups of strains can be distinguished from the entire population on the basis of specific traits. Strains accustomed to growing in the presence of high sugar concentrations, such as wine yeasts and strains obtained from fruits, were able to achieve fermentation, whereas natural yeasts isolated from “poor-sugar” environments, such as oak trees or plants, were not. Commercial wine yeasts clearly appeared as a subset of vineyard isolates, and were mainly differentiated by their fermentative performances as well as their low acetate production. Overall, the emergence of the origin-dependent properties of the strains provides evidence for a phenotypic evolution driven by environmental constraints and/or human selection within S. cerevisiae. PMID:21949874
Advances in yeast genome engineering.

PubMed

David, Florian; Siewers, Verena

2015-02-01

Genome engineering based on homologous recombination has been applied to yeast for many years. However, the growing importance of yeast as a cell factory in metabolic engineering and chassis in synthetic biology demands methods for fast and efficient introduction of multiple targeted changes such as gene knockouts and introduction of multistep metabolic pathways. In this review, we summarize recent improvements of existing genome engineering methods, the development of novel techniques, for example for advanced genome redesign and evolution, and the importance of endonucleases as genome engineering tools. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.
Combinatorial complexity of pathway analysis in metabolic networks.

PubMed

Klamt, Steffen; Stelling, Jörg

2002-01-01

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.
Yeast biotechnology: teaching the old dog new tricks

PubMed Central

2014-01-01

Yeasts are regarded as the first microorganisms used by humans to process food and alcoholic beverages. The technology developed out of these ancient processes has been the basis for modern industrial biotechnology. Yeast biotechnology has gained great interest again in the last decades. Joining the potentials of genomics, metabolic engineering, systems and synthetic biology enables the production of numerous valuable products of primary and secondary metabolism, technical enzymes and biopharmaceutical proteins. An overview of emerging and established substrates and products of yeast biotechnology is provided and discussed in the light of the recent literature. PMID:24602262
Structuring evolution: biochemical networks and metabolic diversification in birds.

PubMed

Morrison, Erin S; Badyaev, Alexander V

2016-08-25

Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.
Rewiring monocyte glucose metabolism via C-type lectin signaling protects against disseminated candidiasis.

PubMed

Domínguez-Andrés, Jorge; Arts, Rob J W; Ter Horst, Rob; Gresnigt, Mark S; Smeekens, Sanne P; Ratter, Jacqueline M; Lachmandas, Ekta; Boutens, Lily; van de Veerdonk, Frank L; Joosten, Leo A B; Notebaart, Richard A; Ardavín, Carlos; Netea, Mihai G

2017-09-01

Monocytes are innate immune cells that play a pivotal role in antifungal immunity, but little is known regarding the cellular metabolic events that regulate their function during infection. Using complementary transcriptomic and immunological studies in human primary monocytes, we show that activation of monocytes by Candida albicans yeast and hyphae was accompanied by metabolic rewiring induced through C-type lectin-signaling pathways. We describe that the innate immune responses against Candida yeast are energy-demanding processes that lead to the mobilization of intracellular metabolite pools and require induction of glucose metabolism, oxidative phosphorylation and glutaminolysis, while responses to hyphae primarily rely on glycolysis. Experimental models of systemic candidiasis models validated a central role for glucose metabolism in anti-Candida immunity, as the impairment of glycolysis led to increased susceptibility in mice. Collectively, these data highlight the importance of understanding the complex network of metabolic responses triggered during infections, and unveil new potential targets for therapeutic approaches against fungal diseases.
Rewiring monocyte glucose metabolism via C-type lectin signaling protects against disseminated candidiasis

PubMed Central

Smeekens, Sanne P.; Lachmandas, Ekta; Boutens, Lily; van de Veerdonk, Frank L.; Joosten, Leo A. B.; Ardavín, Carlos; Netea, Mihai G.

2017-01-01

Monocytes are innate immune cells that play a pivotal role in antifungal immunity, but little is known regarding the cellular metabolic events that regulate their function during infection. Using complementary transcriptomic and immunological studies in human primary monocytes, we show that activation of monocytes by Candida albicans yeast and hyphae was accompanied by metabolic rewiring induced through C-type lectin-signaling pathways. We describe that the innate immune responses against Candida yeast are energy-demanding processes that lead to the mobilization of intracellular metabolite pools and require induction of glucose metabolism, oxidative phosphorylation and glutaminolysis, while responses to hyphae primarily rely on glycolysis. Experimental models of systemic candidiasis models validated a central role for glucose metabolism in anti-Candida immunity, as the impairment of glycolysis led to increased susceptibility in mice. Collectively, these data highlight the importance of understanding the complex network of metabolic responses triggered during infections, and unveil new potential targets for therapeutic approaches against fungal diseases. PMID:28922415
In silico modeling of the yeast protein and protein family interaction network

NASA Astrophysics Data System (ADS)

Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.
Metabolic link between phosphatidylethanolamine and triacylglycerol metabolism in the yeast Saccharomyces cerevisiae.

PubMed

Horvath, Susanne E; Wagner, Andrea; Steyrer, Ernst; Daum, Günther

2011-12-01

In the yeast Saccharomyces cerevisiae triacylglycerols (TAG) are synthesized by the acyl-CoA dependent acyltransferases Dga1p, Are1p, Are2p and the acyl-CoA independent phospholipid:diacylglycerol acyltransferase (PDAT) Lro1p which uses phosphatidylethanolamine (PE) as a preferred acyl donor. In the present study we investigated a possible link between TAG and PE metabolism by analyzing the contribution of the four different PE biosynthetic pathways to TAG formation, namely de novo PE synthesis via Psd1p and Psd2p, the CDP-ethanolamine (CDP-Etn) pathway and lyso-PE acylation by Ale1p. In cells grown on the non-fermentable carbon source lactate supplemented with 5mM ethanolamine (Etn) the CDP-Etn pathway contributed most to the cellular TAG level, whereas mutations in the other pathways displayed only minor effects. In cki1∆dpl1∆eki1∆ mutants bearing defects in the CDP-Etn pathway both the cellular and the microsomal levels of PE were markedly decreased, whereas in other mutants of PE biosynthetic routes depletion of this aminoglycerophospholipid was less pronounced in microsomes. This observation is important because Lro1p similar to the enzymes of the CDP-Etn pathway is a component of the ER. We conclude from these results that in cki1∆dpl1∆eki1∆ insufficient supply of PE to the PDAT Lro1p was a major reason for the strongly reduced TAG level. Moreover, we found that Lro1p activity was markedly decreased in cki1∆dpl1∆eki1∆, although transcription of LRO1 was not affected. Our findings imply that (i) TAG and PE syntheses in the yeast are tightly linked; and (ii) TAG formation by the PDAT Lro1p strongly depends on PE synthesis through the CDP-Etn pathway. Moreover, it is very likely that local availability of PE in microsomes is crucial for TAG synthesis through the Lro1p reaction. Copyright © 2011 Elsevier B.V. All rights reserved.
Yeast supplementation reduced the immune and metabolic responses to a combined viral-bacterial respiratory disease challenge in feedlot heifers

USDA-ARS?s Scientific Manuscript database

Two treatments were evaluated in commercial feedlot heifers to determine the effects of a yeast supplement on immune and metabolic responses to a combined viral-bacterial respiratory disease challenge. Thirty-two beef heifers (324 ± 19.2 kg BW) were selected and randomly assigned to one of two treat...
A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.
Carbon metabolism influenced for promoters and temperature used in the heterologous protein production using Pichia pastoris yeast.

PubMed

Zepeda, Andrea B; Pessoa, Adalberto; Farías, Jorge G

2018-05-19

Nowadays, it is necessary to search for different high-scale production strategies to produce recombinant proteins of economic interest. Only a few microorganisms are industrially relevant for recombinant protein production: methylotrophic yeasts are known to use methanol efficiently as the sole carbon and energy source. Pichia pastoris is a methylotrophic yeast characterized as being an economical, fast and effective system for heterologous protein expression. Many factors can affect both the product and the production, including the promoter, carbon source, pH, production volume, temperature, and many others; but to control all of them most of the time is difficult and this depends on the initial selection of each variable. Therefore, this review focuses on the selection of the best promoter in the recombination process, considering different inductors, and the temperature as a culture medium variable in methylotrophic Pichia pastoris yeast. The goal is to understand the effects associated with different factors that influence its cell metabolism and to reach the construction of an expression system that fulfills the requirements of the yeast, presenting an optimal growth and development in batch, fed-batch or continuous cultures, and at the same time improve its yield in heterologous protein production. Copyright © 2018 Sociedade Brasileira de Microbiologia. Published by Elsevier Editora Ltda. All rights reserved.
Genome dynamics and evolution in yeasts: A long-term yeast-bacteria competition experiment

PubMed Central

Katz, Michael; Knecht, Wolfgang; Compagno, Concetta; Piškur, Jure

2018-01-01

There is an enormous genetic diversity evident in modern yeasts, but our understanding of the ecological basis of such diversifications in nature remains at best fragmented so far. Here we report a long-term experiment mimicking a primordial competitive environment, in which yeast and bacteria co-exist and compete against each other. Eighteen yeasts covering a wide phylogenetic background spanning approximately 250 million years of evolutionary history were used to establish independent evolution lines for at most 130 passages. Our collection of hundreds of modified strains generated through such a rare two-species cross-kingdom competition experiment re-created the appearance of large-scale genomic rearrangements and altered phenotypes important in the diversification history of yeasts. At the same time, the methodology employed in this evolutionary study would also be a non-gene-technological method of reprogramming yeast genomes and then selecting yeast strains with desired traits. Cross-kingdom competition may therefore be a method of significant value to generate industrially useful yeast strains with new metabolic traits. PMID:29624585

Wine yeasts for the future.

PubMed

Fleet, Graham H

2008-11-01

International competition within the wine market, consumer demands for newer styles of wines and increasing concerns about the environmental sustainability of wine production are providing new challenges for innovation in wine fermentation. Within the total production chain, the alcoholic fermentation of grape juice by yeasts is a key process where winemakers can creatively engineer wine character and value through better yeast management and, thereby, strategically tailor wines to a changing market. This review considers the importance of yeast ecology and yeast metabolic reactions in determining wine quality, and then discusses new directions for exploiting yeasts in wine fermentation. It covers criteria for selecting and developing new commercial strains, the possibilities of using yeasts other than those in the genus of Saccharomyces, the prospects for mixed culture fermentations and explores the possibilities for high cell density, continuous fermentations.
Saccharomyces cerevisiae metabolism in ecological context.

PubMed

Jouhten, Paula; Ponomarova, Olga; Gonzalez, Ramon; Patil, Kiran R

2016-11-01

The architecture and regulation of Saccharomyces cerevisiae metabolic network are among the best studied owing to its widespread use in both basic research and industry. Yet, several recent studies have revealed notable limitations in explaining genotype-metabolic phenotype relations in this yeast, especially when concerning multiple genetic/environmental perturbations. Apparently unexpected genotype-phenotype relations may originate in the evolutionarily shaped cellular operating principles being hidden in common laboratory conditions. Predecessors of laboratory S. cerevisiae strains, the wild and the domesticated yeasts, have been evolutionarily shaped by highly variable environments, very distinct from laboratory conditions, and most interestingly by social life within microbial communities. Here we present a brief review of the genotypic and phenotypic peculiarities of S. cerevisiae in the context of its social lifestyle beyond laboratory environments. Accounting for this ecological context and the origin of the laboratory strains in experimental design and data analysis would be essential in improving the understanding of genotype-environment-phenotype relationships. © FEMS 2016.
Saccharomyces cerevisiae metabolism in ecological context

PubMed Central

Jouhten, Paula; Ponomarova, Olga; Gonzalez, Ramon

2016-01-01

The architecture and regulation of Saccharomyces cerevisiae metabolic network are among the best studied owing to its widespread use in both basic research and industry. Yet, several recent studies have revealed notable limitations in explaining genotype–metabolic phenotype relations in this yeast, especially when concerning multiple genetic/environmental perturbations. Apparently unexpected genotype–phenotype relations may originate in the evolutionarily shaped cellular operating principles being hidden in common laboratory conditions. Predecessors of laboratory S. cerevisiae strains, the wild and the domesticated yeasts, have been evolutionarily shaped by highly variable environments, very distinct from laboratory conditions, and most interestingly by social life within microbial communities. Here we present a brief review of the genotypic and phenotypic peculiarities of S. cerevisiae in the context of its social lifestyle beyond laboratory environments. Accounting for this ecological context and the origin of the laboratory strains in experimental design and data analysis would be essential in improving the understanding of genotype–environment–phenotype relationships. PMID:27634775
Regulation of NAD+ metabolism, signaling and compartmentalization in the yeast Saccharomyces cerevisiae.

PubMed

Kato, Michiko; Lin, Su-Ju

2014-11-01

Pyridine nucleotides are essential coenzymes in many cellular redox reactions in all living systems. In addition to functioning as a redox carrier, NAD(+) is also a required co-substrate for the conserved sirtuin deacetylases. Sirtuins regulate transcription, genome maintenance and metabolism and function as molecular links between cells and their environment. Maintaining NAD(+) homeostasis is essential for proper cellular function and aberrant NAD(+) metabolism has been implicated in a number of metabolic- and age-associated diseases. Recently, NAD(+) metabolism has been linked to the phosphate-responsive signaling pathway (PHO pathway) in the budding yeast Saccharomyces cerevisiae. Activation of the PHO pathway is associated with the production and mobilization of the NAD(+) metabolite nicotinamide riboside (NR), which is mediated in part by PHO-regulated nucleotidases. Cross-regulation between NAD(+) metabolism and the PHO pathway has also been reported; however, detailed mechanisms remain to be elucidated. The PHO pathway also appears to modulate the activities of common downstream effectors of multiple nutrient-sensing pathways (Ras-PKA, TOR, Sch9/AKT). These signaling pathways were suggested to play a role in calorie restriction-mediated beneficial effects, which have also been linked to Sir2 function and NAD(+) metabolism. Here, we discuss the interactions of these pathways and their potential roles in regulating NAD(+) metabolism. In eukaryotic cells, intracellular compartmentalization facilitates the regulation of enzymatic functions and also concentrates or sequesters specific metabolites. Various NAD(+)-mediated cellular functions such as mitochondrial oxidative phosphorylation are compartmentalized. Therefore, we also discuss several key players functioning in mitochondrial, cytosolic and vacuolar compartmentalization of NAD(+) intermediates, and their potential roles in NAD(+) homeostasis. To date, it remains unclear how NAD(+) and NAD(+) intermediates
Estrogen receptors and the metabolic network.

PubMed

Barros, Rodrigo P A; Gustafsson, Jan-Åke

2011-09-07

The metabolic syndrome has reached pandemic level worldwide, and evidence is that estradiol plays a key role in its development. The discovery of the second estrogen receptor, ERβ, in tissues previously not considered targets of estradiol was a breakthrough in endocrinology. In the present review, we discuss how the presence of ERβ and the previously described ERα in tissues involved in glucose and lipid homeostasis (brain, skeletal muscle, adipose tissue, pancreas, liver, and heart) may have important implications to risk factors associated with the metabolic syndrome. Imbalance of ERα/ERβ ratio in this "metabolic network" may lead to the metabolic syndrome. Copyright © 2011 Elsevier Inc. All rights reserved.
Metabolic networks in motion: 13C-based flux analysis

PubMed Central

Sauer, Uwe

2006-01-01

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism. PMID:17102807
Path finding methods accounting for stoichiometry in metabolic networks

PubMed Central

2011-01-01

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. PMID:21619601
Heat Shock Partially Dissociates the Overlapping Modules of the Yeast Protein-Protein Interaction Network: A Systems Level Model of Adaptation

PubMed Central

Mihalik, Ágoston; Csermely, Peter

2011-01-01

Network analysis became a powerful tool giving new insights to the understanding of cellular behavior. Heat shock, the archetype of stress responses, is a well-characterized and simple model of cellular dynamics. S. cerevisiae is an appropriate model organism, since both its protein-protein interaction network (interactome) and stress response at the gene expression level have been well characterized. However, the analysis of the reorganization of the yeast interactome during stress has not been investigated yet. We calculated the changes of the interaction-weights of the yeast interactome from the changes of mRNA expression levels upon heat shock. The major finding of our study is that heat shock induced a significant decrease in both the overlaps and connections of yeast interactome modules. In agreement with this the weighted diameter of the yeast interactome had a 4.9-fold increase in heat shock. Several key proteins of the heat shock response became centers of heat shock-induced local communities, as well as bridges providing a residual connection of modules after heat shock. The observed changes resemble to a ‘stratus-cumulus’ type transition of the interactome structure, since the unstressed yeast interactome had a globally connected organization, similar to that of stratus clouds, whereas the heat shocked interactome had a multifocal organization, similar to that of cumulus clouds. Our results showed that heat shock induces a partial disintegration of the global organization of the yeast interactome. This change may be rather general occurring in many types of stresses. Moreover, other complex systems, such as single proteins, social networks and ecosystems may also decrease their inter-modular links, thus develop more compact modules, and display a partial disintegration of their global structure in the initial phase of crisis. Thus, our work may provide a model of a general, system-level adaptation mechanism to environmental changes. PMID:22022244
Metabolic networks evolve towards states of maximum entropy production.

PubMed

Unrean, Pornkamol; Srienc, Friedrich

2011-11-01

A metabolic network can be described by a set of elementary modes or pathways representing discrete metabolic states that support cell function. We have recently shown that in the most likely metabolic state the usage probability of individual elementary modes is distributed according to the Boltzmann distribution law while complying with the principle of maximum entropy production. To demonstrate that a metabolic network evolves towards such state we have carried out adaptive evolution experiments with Thermoanaerobacterium saccharolyticum operating with a reduced metabolic functionality based on a reduced set of elementary modes. In such reduced metabolic network metabolic fluxes can be conveniently computed from the measured metabolite secretion pattern. Over a time span of 300 generations the specific growth rate of the strain continuously increased together with a continuous increase in the rate of entropy production. We show that the rate of entropy production asymptotically approaches the maximum entropy production rate predicted from the state when the usage probability of individual elementary modes is distributed according to the Boltzmann distribution. Therefore, the outcome of evolution of a complex biological system can be predicted in highly quantitative terms using basic statistical mechanical principles. Copyright Â© 2011 Elsevier Inc. All rights reserved.
Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

PubMed

Hadadi, Noushin; Hafner, Jasmin; Soh, Keng Cher; Hatzimanikatis, Vassily

2017-01-01

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.

PubMed

Chen, Xueying; Zhao, Min; Qu, Hong

2015-01-01

T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis.
Toward the automated generation of genome-scale metabolic networks in the SEED.

PubMed

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the
Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

PubMed Central

Chasman, Deborah; Ho, Yi-Hsuan; Berry, David B; Nemec, Corey M; MacGilvray, Matthew E; Hose, James; Merrill, Anna E; Lee, M Violet; Will, Jessica L; Coon, Joshua J; Ansari, Aseem Z; Craven, Mark; Gasch, Audrey P

2014-01-01

Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology. PMID:25411400
Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure

PubMed Central

Milreu, Paulo Vieira; Klein, Cecilia Coimbra; Cottret, Ludovic; Acuña, Vicente; Birmelé, Etienne; Borassi, Michele; Junot, Christophe; Marchetti-Spaccamela, Alberto; Marino, Andrea; Stougie, Leen; Jourdan, Fabien; Crescenzi, Pierluigi; Lacroix, Vincent; Sagot, Marie-France

2014-01-01

Motivation: The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected. Results: We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications. Availability and implementation: The code for the method
[Mitochondria inheritance in yeast saccharomyces cerevisiae].

PubMed

Fizikova, A Iu

2011-01-01

The review is devoted to the main mechanisms of mitochondria inheritance in yeast Saccharonmyces cerevisiae. The genetic mechanisms of functionally active mitochondria inheritance in eukaryotic cells is one of the most relevant in modem researches. A great number of genetic diseases are associated with mitochondria dysfunction. Plasticity of eukaryotic cell metabolism according to the environmental changes is ensured by adequate mitochondria functioning by means of ATP synthesis coordination, reactive oxygen species accumulation, apoptosis regulation and is an important factor of cell adaptation to stress. Mitochondria participation in important for cell vitality processes masters the presence of accurate mechanisms of mitochondria functions regulation according to environment fluctuations. The mechanisms of mitochondria division and distribution are highly conserved. Baker yeast S. cerevisiae is an ideal model object for mitochondria researches due to energetic metabolism lability, ability to switch over respiration to fermentation, and petite-positive phenotype. Correction of metabolism according to the environmental changes is necessary for cell vitality. The influence of respiratory, carbon, amino acid and phosphate metabolism on mitochondria functions was shown. As far as the mechanisms that stabilize functions of mitochondria and mtDNA are highly conserve, we can project yeast regularities on higher eukaryotes systems. This makes it possible to approximate understanding the etiology and pathogenesis of a great number of human diseases.
Effect of the cancer specific shorter form of human 6-phosphofructo-1-kinase on the metabolism of the yeast Saccharomyces cerevisiae.

PubMed

Andrejc, Darjan; Možir, Alenka; Legiša, Matic

2017-05-08

At first glance, there appears to be a high degree of similarity between the metabolism of yeast (the Crabtree effect) and human cancer cells (the Warburg effect). At the root of both effects is accelerated metabolic flow through glycolysis which leads to overflows of ethanol and lactic acid, respectively. It has been proposed that enhanced glycolytic flow in cancer cells is triggered by the altered kinetic characteristics of the key glycolytic regulatory enzyme 6-phosphofructo-1-kinase (Pfk). Through a posttranslational modification, highly active shorter Pfk-M fragments, which are resistant to feedback inhibition, are formed after the proteolytic cleavage of the C-terminus of the native human Pfk-M. Alternatively, enhanced glycolysis is triggered by optimal growth conditions in the yeast Saccharomyces cerevisiae. To assess the deregulation of glycolysis in yeast cells, the sfPFKM gene encoding highly active human shorter Pfk-M fragments was introduced into pfk-null S. cerevisiae. No growth of the transformants with the sfPFKM gene was observed on glucose and fructose. Glucose even induced rapid deactivation of Pfk1 activities in such transformants. However, Pfk1 activities of the sfPFKM transformants were detected in maltose medium, but the growth in maltose was possible only after the addition of 10 mM of ethanol to the medium. Ethanol seemed to alleviate the severely unbalanced NADH/NADPH ratio in the sfPFKM cells. However, the transformants carrying modified Pfk-M enzymes grew faster than the transformants with the human native human Pfk-M enzyme in a narrow ecological niche with a low maltose concentration medium that was further improved by additional modifications. Interestingly, periodic extracellular accumulation of phenylacetaldehyde was detected during the growth of the strain with modified Pfk-M but not with the strain encoding the human native enzyme. Highly active cancer-specific shorter Pfk-M fragments appear to trigger several controlling
Metabolic engineering of a haploid strain derived from a triploid industrial yeast for producing cellulosic ethanol.

PubMed

Kim, Soo Rin; Skerker, Jeffrey M; Kong, In Iok; Kim, Heejin; Maurer, Matthew J; Zhang, Guo-Chang; Peng, Dairong; Wei, Na; Arkin, Adam P; Jin, Yong-Su

2017-03-01

Many desired phenotypes for producing cellulosic biofuels are often observed in industrial Saccharomyces cerevisiae strains. However, many industrial yeast strains are polyploid and have low spore viability, making it difficult to use these strains for metabolic engineering applications. We selected the polyploid industrial strain S. cerevisiae ATCC 4124 exhibiting rapid glucose fermentation capability, high ethanol productivity, strong heat and inhibitor tolerance in order to construct an optimal yeast strain for producing cellulosic ethanol. Here, we focused on developing a general approach and high-throughput screening method to isolate stable haploid segregants derived from a polyploid parent, such as triploid ATCC 4124 with a poor spore viability. Specifically, we deleted the HO genes, performed random sporulation, and screened the resulting segregants based on growth rate, mating type, and ploidy. Only one stable haploid derivative (4124-S60) was isolated, while 14 other segregants with a stable mating type were aneuploid. The 4124-S60 strain inherited only a subset of desirable traits present in the parent strain, same as other aneuploids, suggesting that glucose fermentation and specific ethanol productivity are likely to be genetically complex traits and/or they might depend on ploidy. Nonetheless, the 4124-60 strain did inherit the ability to tolerate fermentation inhibitors. When additional genetic perturbations known to improve xylose fermentation were introduced into the 4124-60 strain, the resulting engineered strain (IIK1) was able to ferment a Miscanthus hydrolysate better than a previously engineered laboratory strain (SR8), built by making the same genetic changes. However, the IIK1 strain showed higher glycerol and xylitol yields than the SR8 strain. In order to decrease glycerol and xylitol production, an NADH-dependent acetate reduction pathway was introduced into the IIK1 strain. By consuming 2.4g/L of acetate, the resulting strain (IIK1A
Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris.

PubMed

Saitua, Francisco; Torres, Paulina; Pérez-Correa, José Ricardo; Agosin, Eduardo

2017-02-21

Pichia pastoris shows physiological advantages in producing recombinant proteins, compared to other commonly used cell factories. This yeast is mostly grown in dynamic cultivation systems, where the cell's environment is continuously changing and many variables influence process productivity. In this context, a model capable of explaining and predicting cell behavior for the rational design of bioprocesses is highly desirable. Currently, there are five genome-scale metabolic reconstructions of P. pastoris which have been used to predict extracellular cell behavior in stationary conditions. In this work, we assembled a dynamic genome-scale metabolic model for glucose-limited, aerobic cultivations of Pichia pastoris. Starting from an initial model structure for batch and fed-batch cultures, we performed pre/post regression diagnostics to ensure that model parameters were identifiable, significant and sensitive. Once identified, the non-relevant ones were iteratively fixed until a priori robust modeling structures were found for each type of cultivation. Next, the robustness of these reduced structures was confirmed by calibrating the model with new datasets, where no sensitivity, identifiability or significance problems appeared in their parameters. Afterwards, the model was validated for the prediction of batch and fed-batch dynamics in the studied conditions. Lastly, the model was employed as a case study to analyze the metabolic flux distribution of a fed-batch culture and to unravel genetic and process engineering strategies to improve the production of recombinant Human Serum Albumin (HSA). Simulation of single knock-outs indicated that deviation of carbon towards cysteine and tryptophan formation improves HSA production. The deletion of methylene tetrahydrofolate dehydrogenase could increase the HSA volumetric productivity by 630%. Moreover, given specific bioprocess limitations and strain characteristics, the model suggests that implementation of a decreasing
Induction of energy metabolism related enzymes in yeast Saccharomyces cerevisiae exposed to ibogaine is adaptation to acute decrease in ATP energy pool.

PubMed

Paskulin, Roman; Jamnik, Polona; Obermajer, Natasa; Slavić, Marija; Strukelj, Borut

2010-02-10

Ibogaine has been extensively studied in the last decades in relation to its anti-addictive properties that have been repeatedly reported as being addiction interruptive and craving eliminative. In our previous study we have already demonstrated induction of energy related enzymes in rat brains treated with ibogaine at a dose of 20mg/kg i.p. 24 and 72 h prior to proteomic analysis. In this study a model organism yeast Saccharomyces cerevisiae was cultivated with ibogaine in a concentration of 1mg/l. Energy metabolism cluster enzymes glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, enolase and alcohol dehydrogenase were induced after 5h of exposure. This is a compensation of demonstrated ATP pool decrease after ibogaine. Yeast in a stationary growth phase is an accepted model for studies of housekeeping metabolism of eukaryotes, including humans. Study showed that ibogaine's influence on metabolism is neither species nor tissue specific. Effect is not mediated by binding of ibogaine to receptors, as previously described in literature since they are lacking in this model. Copyright (c) 2009 Elsevier B.V. All rights reserved.
Second Law of Thermodynamics Applied to Metabolic Networks

NASA Technical Reports Server (NTRS)

Nigam, R.; Liang, S.

2003-01-01

We present a simple algorithm based on linear programming, that combines Kirchoff's flux and potential laws and applies them to metabolic networks to predict thermodynamically feasible reaction fluxes. These law's represent mass conservation and energy feasibility that are widely used in electrical circuit analysis. Formulating the Kirchoff's potential law around a reaction loop in terms of the null space of the stoichiometric matrix leads to a simple representation of the law of entropy that can be readily incorporated into the traditional flux balance analysis without resorting to non-linear optimization. Our technique is new as it can easily check the fluxes got by applying flux balance analysis for thermodynamic feasibility and modify them if they are infeasible so that they satisfy the law of entropy. We illustrate our method by applying it to the network dealing with the central metabolism of Escherichia coli. Due to its simplicity this algorithm will be useful in studying large scale complex metabolic networks in the cell of different organisms.

MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

PubMed

Zhou, Yikang; Li, Gang; Dong, Junkai; Xing, Xin-Hui; Dai, Junbiao; Zhang, Chong

2018-05-01

Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric plate-based, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.
Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

PubMed

Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

2010-05-01

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.
Multi-Criteria Optimization of Regulation in Metabolic Networks

PubMed Central

Higuera, Clara; Villaverde, Alejandro F.; Banga, Julio R.; Ross, John; Morán, Federico

2012-01-01

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks. PMID:22848435
Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfurfural tolerance for ethanologenic yeast.

PubMed

Song, M; Ouyang, Z; Liu, Z L

2009-05-01

Composed of linear difference equations, a discrete dynamical system (DDS) model was designed to reconstruct transcriptional regulations in gene regulatory networks (GRNs) for ethanologenic yeast Saccharomyces cerevisiae in response to 5-hydroxymethylfurfural (HMF), a bioethanol conversion inhibitor. The modelling aims at identification of a system of linear difference equations to represent temporal interactions among significantly expressed genes. Power stability is imposed on a system model under the normal condition in the absence of the inhibitor. Non-uniform sampling, typical in a time-course experimental design, is addressed by a log-time domain interpolation. A statistically significant DDS model of the yeast GRN derived from time-course gene expression measurements by exposure to HMF, revealed several verified transcriptional regulation events. These events implicate Yap1 and Pdr3, transcription factors consistently known for their regulatory roles by other studies or postulated by independent sequence motif analysis, suggesting their involvement in yeast tolerance and detoxification of the inhibitor.
An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221
Blueprint for antimicrobial hit discovery targeting metabolic networks.

PubMed

Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

2010-01-19

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.
Metabolic network flux analysis for engineering plant systems.

PubMed

Shachar-Hill, Yair

2013-04-01

Metabolic network flux analysis (NFA) tools have proven themselves to be powerful aids to metabolic engineering of microbes by providing quantitative insights into the flows of material and energy through cellular systems. The development and application of NFA tools to plant systems has advanced in recent years and are yielding significant insights and testable predictions. Plants present substantial opportunities for the practical application of NFA but they also pose serious challenges related to the complexity of plant metabolic networks and to deficiencies in our knowledge of their structure and regulation. By considering the tools available and selected examples, this article attempts to assess where and how NFA is most likely to have a real impact on plant biotechnology. Copyright © 2013 Elsevier Ltd. All rights reserved.
The topology of metabolic isotope labeling networks.

PubMed

Weitzel, Michael; Wiechert, Wolfgang; Nöh, Katharina

2007-08-29

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively
A polyphasic study on the taxonomic position of industrial sour dough yeasts.

PubMed

Mäntynen, V H; Korhola, M; Gudmundsson, H; Turakainen, H; Alfredsson, G A; Salovaara, H; Lindström, K

1999-02-01

The sour dough bread making process is extensively used to produce wholesome palatable rye bread. The process is traditionally done using a back-slopping procedure. Traditional sour doughs in Finland comprise of lactic acid bacteria and yeasts. The yeasts present in these doughs have been enriched in the doughs due to their metabolic activities, e.g. acid tolerance. We characterized the yeasts in five major sour bread bakeries in Finland. We found that most of the commercial sour doughs contained yeasts which were similar to Candida milleri on the basis of 18S rDNA and EF-3 PCR-RFLP patterns and metabolic activities. Some of the bakery yeasts exhibited extensive karyotype polymorphism. The minimum growth temperature was 8 degrees C for C. milleri and also for most of sour dough yeasts.
A toolbox model of evolution of metabolic pathways on networks of arbitrary topology.

PubMed

Pang, Tin Yau; Maslov, Sergei

2011-05-01

In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway
Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.
Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614
Metabolic Pathways and Networks Associated with Tobacco Use in Military Personnel

PubMed Central

Jones, Dean P.; Walker, Douglas I.; Uppal, Karan; Rohrbeck, Patricia; Mallon, Timothy M.; Go, Young-Mi

2016-01-01

Objective Use high-resolution metabolomics (HRM) to identify metabolic pathways and networks associated with tobacco use in military personnel. Methods Four hundred de-identified samples obtained from the Department of Defense Serum Repository were classified as tobacco users or non-users according to cotinine content. HRM and bioinformatic methods were used to determine pathways and networks associated with classification. Results Eighty individuals were classified as tobacco users compared to 320 non-users based on cotinine levels ≥10 ng/mL. Alterations in lipid and xenobiotic metabolism, and diverse effects on amino acid, sialic acid and purine and pyrimidine metabolism were observed. Importantly, network analysis showed broad effects on metabolic associations not simply linked to well-defined pathways. Conclusions Tobacco use has complex metabolic effects which must be considered in evaluation of deployment-associated environmental exposures in military personnel. PMID:27501098
Metabolic Pathways and Networks Associated With Tobacco Use in Military Personnel.

PubMed

Jones, Dean P; Walker, Douglas I; Uppal, Karan; Rohrbeck, Patricia; Mallon, Col Timothy M; Go, Young-Mi

2016-08-01

The aim of this study is to use high-resolution metabolomics (HRM) to identify metabolic pathways and networks associated with tobacco use in military personnel. Four hundred deidentified samples obtained from the Department of Defense Serum Repository were classified as tobacco users or nonusers according to cotinine content. HRM and bioinformatic methods were used to determine pathways and networks associated with classification. Eighty individuals were classified as tobacco users compared with 320 nonusers on the basis of cotinine levels at least 10 ng/mL. Alterations in lipid and xenobiotic metabolism, and diverse effects on amino acid, sialic acid, and purine and pyrimidine metabolism were observed. Importantly, network analysis showed broad effects on metabolic associations not simply linked to well-defined pathways. Tobacco use has complex metabolic effects that must be considered in evaluation of deployment-associated environmental exposures in military personnel.
Exploring metabolic pathways in genome-scale networks via generating flux modes.

PubMed

Rezola, A; de Figueiredo, L F; Brock, M; Pey, J; Podhorski, A; Wittmann, C; Schuster, S; Bockmayr, A; Planes, F J

2011-02-15

The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.
Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

PubMed

Kiparissides, A; Hatzimanikatis, V

2017-01-01

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier
Computational identification of obligatorily autocatalytic replicators embedded in metabolic networks

PubMed Central

Kun, Ádám; Papp, Balázs; Szathmáry, Eörs

2008-01-01

Background If chemical A is necessary for the synthesis of more chemical A, then A has the power of replication (such systems are known as autocatalytic systems). We provide the first systems-level analysis searching for small-molecular autocatalytic components in the metabolisms of diverse organisms, including an inferred minimal metabolism. Results We find that intermediary metabolism is invariably autocatalytic for ATP. Furthermore, we provide evidence for the existence of additional, organism-specific autocatalytic metabolites in the forms of coenzymes (NAD+, coenzyme A, tetrahydrofolate, quinones) and sugars. Although the enzymatic reactions of a number of autocatalytic cycles are present in most of the studied organisms, they display obligatorily autocatalytic behavior in a few networks only, hence demonstrating the need for a systems-level approach to identify metabolic replicators embedded in large networks. Conclusion Metabolic replicators are apparently common and potentially both universal and ancestral: without their presence, kick-starting metabolic networks is impossible, even if all enzymes and genes are present in the same cell. Identification of metabolic replicators is also important for attempts to create synthetic cells, as some of these autocatalytic molecules will presumably be needed to be added to the system as, by definition, the system cannot synthesize them without their initial presence. PMID:18331628
Blueprint for antimicrobial hit discovery targeting metabolic networks

PubMed Central

Shen, Y.; Liu, J.; Estiu, G.; Isin, B.; Ahn, Y-Y.; Lee, D-S.; Barabási, A-L.; Kapatral, V.; Wiest, O.; Oltvai, Z. N.

2010-01-01

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy. PMID:20080587
Metabolic GARD: Replicating Catalytic Network of Lipid-Anchored Metabolites

NASA Astrophysics Data System (ADS)

Lancet, D.; Zidovetzki, R.; Shenhav, B.; Markovitch, O.

2017-07-01

We propose a computer-simulated M-GARD model, with mutually catalytic metabolic network of amphiphiles. It can show compositional reproduction of both bilayer and lumen content of lipid vesicles, thus joining metabolism, compartment and replication.
No effect of yeast-like fungi on lipid metabolism and vascular endothelial growth factor level in children and adolescents with type 1 diabetes mellitus.

PubMed

Zorena, Katarzyna; Kowalewska, Beata; Szmigiero-Kawko, Małgorzata; Wąż, Piotr; Myśliwiec, Małgorzata

2016-12-12

The objective of the research was to investigate vascular endothelial growth factor (VEGF) levels in the context of lipid metabolism and amount of yeast-like fungi colonizing the digestive tract in children and adolescents with type 1 diabetes mellitus (T1DM). The study included 45 children with T1DM and 27 age- and sex-matched healthy control subjects. In the study sample 33 T1DM patients were administered insulin pump therapy and 12 T1DM patients were administered multiple daily injections with insulin pen devices. All T1DM patients were free of micro- and macrovascular complications. In T1DM patients and healthy controls biochemical tests were performed and measurements of yeast-like fungi colonizing the alimentary tract were conducted. Moreover all study subjects had their serum VEGF levels measured with ELISA test. The subgroup of children and adolescents with T1DM and yeast-like fungus colony number 10^ 3 CFU/g was shown statistically significantly lower HbA1c levels, and lower but not statistically significantly total cholesterol, LDL cholesterol and VEGF levels versus T1DM patients with the amount of yeast-like fungi 10^ 6 CFU/g. Moreover higher HDL levels were observed in this subgroup versus T1DM patients with the amount of yeast-like fungi 10^ 6 CFU/g although the difference was not statistically significant. Our study has shown no influence of yeast-like fungi on lipid metabolism and VEGF level in children and adolescents with T1DM. Comprehensive treatment of T1DM patients and intensive insulin therapy with help of personal insulin pumps can reduce or prevent the development of long-term diabetic complications. Further studies in this field are needed.

Systematic assignment of thermodynamic constraints in metabolic network models

PubMed Central

Kümmel, Anne; Panke, Sven; Heinemann, Matthias

2006-01-01

Background The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models. Results We present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to
An integrated network visualization framework towards metabolic engineering applications.

PubMed

Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

2014-12-30

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.
Interaction Between Yeasts and Zinc

NASA Astrophysics Data System (ADS)

Nicola, Raffaele De; Walker, Graeme

Zinc is an essential trace element in biological systems. For example, it acts as a cellular membrane stabiliser, plays a critical role in gene expression and genome modification and activates nearly 300 enzymes, including alcohol dehydrogenase. The present chapter will be focused on the influence of zinc on cell physiology of industrial yeast strains of Saccharomyces cerevisiae, with special regard to the uptake and subsequent utilisation of this metal. Zinc uptake by yeast is metabolism-dependent, with most of the available zinc translocated very quickly into the vacuole. At cell division, zinc is distributed from mother to daughter cells and this effectively lowers the individual cellular zinc concentration, which may become zinc depleted at the onset of the fermentation. Zinc influences yeast fermentative performance and examples will be provided relating to brewing and wine fermentations. Industrial yeasts are subjected to several stresses that may impair fermentation performance. Such stresses may also impact on yeast cell zinc homeostasis. This chapter will discuss the practical implications for the correct management of zinc bioavailability for yeast-based biotechnologies aimed at improving yeast growth, viability, fermentation performance and resistance to environmental stresses
Advanced Stoichiometric Analysis of Metabolic Networks of Mammalian Systems

PubMed Central

Orman, Mehmet A.; Berthiaume, Francois; Androulakis, Ioannis P.; Ierapetritou, Marianthi G.

2013-01-01

Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted. PMID:22196224
Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast.

PubMed

Abd-Rabbo, Diala; Michnick, Stephen W

2017-03-16

Kinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted. We assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package ( https://cran.r-project.org/web/packages/VertexSort/index.html ). The KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes
Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

PubMed

Hamilton, Joshua J; Reed, Jennifer L

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different
Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

PubMed Central

Hamilton, Joshua J.; Reed, Jennifer L.

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different
Topological analysis of metabolic networks based on petri net theory.

PubMed

Zevedei-Oancea, Ionela; Schuster, Stefan

2011-01-01

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.
Topological analysis of metabolic networks based on Petri net theory.

PubMed

Zevedei-Oancea, Ionela; Schuster, Stefan

2003-01-01

Petri net concepts provide additional tools for the modelling of metabolic networks. Here, the similarities between the counterparts in traditional biochemical modelling and Petri net theory are discussed. For example the stoichiometry matrix of a metabolic network corresponds to the incidence matrix of the Petri net. The flux modes and conservation relations have the T-invariants, respectively, P-invariants as counterparts. We reveal the biological meaning of some notions specific to the Petri net framework (traps, siphons, deadlocks, liveness). We focus on the topological analysis rather than on the analysis of the dynamic behaviour. The treatment of external metabolites is discussed. Some simple theoretical examples are presented for illustration. Also the Petri nets corresponding to some biochemical networks are built to support our results. For example, the role of triose phosphate isomerase (TPI) in Trypanosoma brucei metabolism is evaluated by detecting siphons and traps. All Petri net properties treated in this contribution are exemplified on a system extracted from nucleotide metabolism.
Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

PubMed

Morrison, Erin S; Badyaev, Alexander V

2018-05-01

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.
A general model for metabolic scaling in self-similar asymmetric networks

PubMed Central

Savage, Van M.; Enquist, Brian J.

2017-01-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber’s Law can still be attained within many asymmetric networks. PMID:28319153
A general model for metabolic scaling in self-similar asymmetric networks.

PubMed

Brummer, Alexander Byers; Savage, Van M; Enquist, Brian J

2017-03-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.
A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

PubMed

Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T; Tyson, John J

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.
A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

PubMed Central

Laomettachit, Teeraphan; Chen, Katherine C.; Baumann, William T.

2016-01-01

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804
Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence

DTIC Science & Technology

2016-08-01

Award Number: TITLE: Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence PRINCIPAL INVESTIGATOR: COL (Ret) Marina N...2016 2. REPORT TYPE FINAL 3. DATES COVERED (From - To) 29 Sep 2011 – 31 May 2016 4. TITLE AND SUBTITLE "Metabolic Networks Integrative Cardiac Health...ABSTRACT The Integrative Cardiac Health Project (ICHP) aims to lead the way in Cardiovascular Disease (CVD) Prevention by conducting novel research
Dissecting Germ Cell Metabolism through Network Modeling.

PubMed

Whitmore, Leanne S; Ye, Ping

2015-01-01

Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.
Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks*

PubMed Central

Krumholz, Elias W.; Libourel, Igor G. L.

2015-01-01

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. PMID:26041773
Regulation of intracellular formaldehyde toxicity during methanol metabolism of the methylotrophic yeast Pichia methanolica.

PubMed

Wakayama, Keishi; Yamaguchi, Sakiko; Takeuchi, Akihito; Mizumura, Tasuku; Ozawa, Shotaro; Tomizuka, Noboru; Hayakawa, Takashi; Nakagawa, Tomoyuki

2016-11-01

In this study we found that the methylotrophic yeast Pichia methanolica showed impaired growth on high methanol medium (>5%, or 1.56 M, methanol). In contrast, P. methanolica grew well on glucose medium containing 5% methanol, but the growth defects reappeared on glucose medium supplemented with 5 mM formaldehyde. During methanol growth of P. methanolica, formaldehyde accumulated in the medium up to 0.3 mM before it was consumed rapidly based on cell growth. These findings indicate that the growth defect of P. methanolica on high methanol media is not caused directly by methanol toxicity, but rather by formaldehyde, which is a key toxic intermediate of methanol metabolism. Moreover, during methanol growth of P. methanolica, expression of enzymes in the methanol-oxidation pathway were induced before the alcohol oxidase isozymes Mod1p and Mod2p, and Mod1p expression was induced before Mod2p. These results suggest that to avoid excess accumulation of formaldehyde-the toxic intermediate of methanol metabolism-P. methanolica grown on methanol strictly regulates the order in which methanol-metabolizing enzymes are expressed. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.
EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

PubMed Central

Choudhary, Kumari Sonal; Rohatgi, Neha; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-01-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373
EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

PubMed

Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-06-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

PubMed Central

Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M.; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

2017-01-01

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to
Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.

PubMed

Prigent, Sylvain; Frioux, Clémence; Dittami, Simon M; Thiele, Sven; Larhlimi, Abdelhalim; Collet, Guillaume; Gutknecht, Fabien; Got, Jeanne; Eveillard, Damien; Bourdon, Jérémie; Plewniak, Frédéric; Tonon, Thierry; Siegel, Anne

2017-01-01

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to
Yeast killer systems.

PubMed Central

Magliani, W; Conti, S; Gerloni, M; Bertolotti, D; Polonelli, L

1997-01-01

The killer phenomenon in yeasts has been revealed to be a multicentric model for molecular biologists, virologists, phytopathologists, epidemiologists, industrial and medical microbiologists, mycologists, and pharmacologists. The surprisingly widespread occurrence of the killer phenomenon among taxonomically unrelated microorganisms, including prokaryotic and eukaryotic pathogens, has engendered a new interest in its biological significance as well as its theoretical and practical applications. The search for therapeutic opportunities by using yeast killer systems has conceptually opened new avenues for the prevention and control of life-threatening fungal diseases through the idiotypic network that is apparently exploited by the immune system in the course of natural infections. In this review, the biology, ecology, epidemiology, therapeutics, serology, and idiotypy of yeast killer systems are discussed. PMID:9227858
Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

PubMed Central

Geiselman, Gina M; Ito, Masakazu; Mondo, Stephen J; Reilly, Morgann C; Cheng, Ya-Fang; Bauer, Stefan; Grigoriev, Igor V; Gladden, John M; Simmons, Blake A; Brem, Rachel B

2018-01-01

The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted function in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. These results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi. PMID:29521624
Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

DOE PAGES

Coradetti, Samuel T.; Pinel, Dominic; Geiselman, Gina M.; ...

2018-03-09

The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted functionmore » in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. Lastly, these results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi.« less
Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coradetti, Samuel T.; Pinel, Dominic; Geiselman, Gina M.

The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted functionmore » in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. Lastly, these results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi.« less
Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence

DTIC Science & Technology

2016-04-01

2.6; P = 0.001) among all variables, as the most significant predictor of abnormal CIMT, thus increasing risk for CVD. Conclusions: The Integrative ...1 Award Number: W81XWH-11-2-0227 TITLE: "Metabolic Networks Integrative Cardiac Health Project (ICHP) - Center of Excellence." PRINCIPAL...April 2016 2. REPORT TYPE ANNUAL 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE "Metabolic Networks Integrative Cardiac Health Project (ICHP
Metagenomics reveals flavour metabolic network of cereal vinegar microbiota.

PubMed

Wu, Lin-Huan; Lu, Zhen-Ming; Zhang, Xiao-Juan; Wang, Zong-Min; Yu, Yong-Jian; Shi, Jin-Song; Xu, Zheng-Hong

2017-04-01

Multispecies microbial community formed through centuries of repeated batch acetic acid fermentation (AAF) is crucial for the flavour quality of traditional vinegar produced from cereals. However, the metabolism to generate and/or formulate the essential flavours by the multispecies microbial community is hardly understood. Here we used metagenomic approach to clarify in situ metabolic network of key microbes responsible for flavour synthesis of a typical cereal vinegar, Zhenjiang aromatic vinegar, produced by solid-state fermentation. First, we identified 3 organic acids, 7 amino acids, and 20 volatiles as dominant vinegar metabolites. Second, we revealed taxonomic and functional composition of the microbiota by metagenomic shotgun sequencing. A total of 86 201 predicted protein-coding genes from 35 phyla (951 genera) were involved in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of Metabolism (42.3%), Genetic Information Processing (28.3%), and Environmental Information Processing (10.1%). Furthermore, a metabolic network for substrate breakdown and dominant flavour formation in vinegar microbiota was constructed, and microbial distribution discrepancy in different metabolic pathways was charted. This study helps elucidating different metabolic roles of microbes during flavour formation in vinegar microbiota. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Engineering a Saccharomyces cerevisiae wine yeast that exhibits reduced ethanol production during fermentation under controlled microoxygenation conditions.

PubMed

Heux, Stéphanie; Sablayrolles, Jean-Marie; Cachon, Rémy; Dequin, Sylvie

2006-09-01

We recently showed that expressing an H(2)O-NADH oxidase in Saccharomyces cerevisiae drastically reduces the intracellular NADH concentration and substantially alters the distribution of metabolic fluxes in the cell. Although the engineered strain produces a reduced amount of ethanol, a high level of acetaldehyde accumulates early in the process (1 g/liter), impairing growth and fermentation performance. To overcome these undesirable effects, we carried out a comprehensive analysis of the impact of oxygen on the metabolic network of the same NADH oxidase-expressing strain. While reducing the oxygen transfer rate led to a gradual recovery of the growth and fermentation performance, its impact on the ethanol yield was negligible. In contrast, supplying oxygen only during the stationary phase resulted in a 7% reduction in the ethanol yield, but without affecting growth and fermentation. This approach thus represents an effective strategy for producing wine with reduced levels of alcohol. Importantly, our data also point to a significant role for NAD(+) reoxidation in controlling the glycolytic flux, indicating that engineered yeast strains expressing an NADH oxidase can be used as a powerful tool for gaining insight into redox metabolism in yeast.
Efficient searching and annotation of metabolic networks using chemical similarity

PubMed Central

Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Tyo, Keith E.J.

2015-01-01

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Availability and implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/. Contact: k-tyo@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25417203
Filamentous invasive growth of mutants of the genes encoding ammonia-metabolizing enzymes in the fission yeast Schizosaccharomyces pombe.

PubMed

Sasaki, Yoshie; Kojima, Ayumi; Shibata, Yuriko; Mitsuzawa, Hiroshi

2017-01-01

The fission yeast Schizosaccharomyces pombe undergoes a switch from yeast to filamentous invasive growth in response to certain environmental stimuli. Among them is ammonium limitation. Amt1, one of the three ammonium transporters in this yeast, is required for the ammonium limitation-induced morphological transition; however, the underlying molecular mechanism remains to be understood. Cells lacking Amt1 became capable of invasive growth upon increasing concentrations of ammonium in the medium, suggesting that the ammonium taken up into the cell or a metabolic intermediate in ammonium assimilation might serve as a signal for the ammonium limitation-induced morphological transition. To investigate the possible role of ammonium-metabolizing enzymes in the signaling process, deletion mutants were constructed for the gdh1, gdh2, gln1, and glt1 genes, which were demonstrated by enzyme assays to encode NADP-specific glutamate dehydrogenase, NAD-specific glutamate dehydrogenase, glutamine synthetase, and glutamate synthase, respectively. Growth tests on various nitrogen sources revealed that a gln1Δ mutant was a glutamine auxotroph and that a gdh1Δ mutant had a defect in growth on ammonium, particularly at high concentrations. The latter observation indicates that the NADP-specific glutamate dehydrogenase of S. pombe plays a major role in ammonium assimilation under high ammonium concentrations. Invasive growth assays showed that gdh1Δ and glt1Δ mutants underwent invasive growth to a lesser extent than did wild-type strains. Increasing the ammonium concentration in the medium suppressed the invasive growth defect of the glt1Δ mutant, but not the gdh1Δ mutant. These results suggest that the nitrogen status of the cell is important in the induction of filamentous invasive growth in S. pombe.
The wine and beer yeast Dekkera bruxellensis

PubMed Central

Schifferdecker, Anna Judith; Dashko, Sofia; Ishchuk, Olena P; Piškur, Jure

2014-01-01

Recently, the non-conventional yeast Dekkera bruxellensis has been gaining more and more attention in the food industry and academic research. This yeast species is a distant relative of Saccharomyces cerevisiae and is especially known for two important characteristics: on the one hand, it is considered to be one of the main spoilage organisms in the wine and bioethanol industry; on the other hand, it is 'indispensable' as a contributor to the flavour profile of Belgium lambic and gueuze beers. Additionally, it adds to the characteristic aromatic properties of some red wines. Recently this yeast has also become a model for the study of yeast evolution. In this review we focus on the recently developed molecular and genetic tools, such as complete genome sequencing and transformation, to study and manipulate this yeast. We also focus on the areas that are particularly well explored in this yeast, such as the synthesis of off-flavours, yeast detection methods, carbon metabolism and evolutionary history. © 2014 The Authors. Yeast published by John Wiley & Sons, Ltd. PMID:24932634
The wine and beer yeast Dekkera bruxellensis.

PubMed

Schifferdecker, Anna Judith; Dashko, Sofia; Ishchuk, Olena P; Piškur, Jure

2014-09-01

Recently, the non-conventional yeast Dekkera bruxellensis has been gaining more and more attention in the food industry and academic research. This yeast species is a distant relative of Saccharomyces cerevisiae and is especially known for two important characteristics: on the one hand, it is considered to be one of the main spoilage organisms in the wine and bioethanol industry; on the other hand, it is 'indispensable' as a contributor to the flavour profile of Belgium lambic and gueuze beers. Additionally, it adds to the characteristic aromatic properties of some red wines. Recently this yeast has also become a model for the study of yeast evolution. In this review we focus on the recently developed molecular and genetic tools, such as complete genome sequencing and transformation, to study and manipulate this yeast. We also focus on the areas that are particularly well explored in this yeast, such as the synthesis of off-flavours, yeast detection methods, carbon metabolism and evolutionary history. © 2014 The Authors. Yeast published by John Wiley & Sons, Ltd.
Estimating the size of the solution space of metabolic networks

PubMed Central

Braunstein, Alfredo; Mulet, Roberto; Pagnani, Andrea

2008-01-01

Background Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion We propose a novel efficient
Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs

PubMed Central

Sharifpoor, Sara; van Dyk, Dewald; Costanzo, Michael; Baryshnikova, Anastasia; Friesen, Helena; Douglas, Alison C.; Youn, Ji-Young; VanderSluis, Benjamin; Myers, Chad L.; Papp, Balázs; Boone, Charles; Andrews, Brenda J.

2012-01-01

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase–substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks. PMID:22282571
Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

PubMed

Krumholz, Elias W; Libourel, Igor G L

2015-07-31

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.
Synchronization of glycolytic oscillations in a yeast cell population.

PubMed

Danø, S; Hynne, F; De Monte, S; d'Ovidio, F; Sørensen, P G; Westerhoff, H

2001-01-01

The mechanism of active phase synchronization in a suspension of oscillatory yeast cells has remained a puzzle for almost half a century. The difficulty of the problem stems from the fact that the synchronization phenomenon involves the entire metabolic network of glycolysis and fermentation, and consequently it cannot be addressed at the level of a single enzyme or a single chemical species. In this paper it is shown how this system in a CSTR (continuous flow stirred tank reactor) can be modelled quantitatively as a population of Stuart-Landau oscillators interacting by exchange of metabolites through the extracellular medium, thus reducing the complexity of the problem without sacrificing the biochemical realism. The parameters of the model can be derived by a systematic expansion from any full-scale model of the yeast cell kinetics with a supercritical Hopf bifurcation. Some parameter values can also be obtained directly from analysis of perturbation experiments. In the mean-field limit, equations for the study of populations having a distribution of frequencies are used to simulate the effect of the inherent variations between cells.
Culturable yeasts in meltwaters draining from two glaciers in the Italian Alps

NASA Astrophysics Data System (ADS)

Buzzini, Pietro; Turchetti, Benedetta; Diolaiuti, Guglielmina; D'Agata, Carlo; Martini, Alessandro; Smiraglia, Claudio

The meltwaters draining from two glaciers in the Italian Alps contain metabolically active yeasts isolable by culture-based laboratory procedures. The average number of culturable yeast cells in the meltwaters was 10 20 colony-forming units (CFU) L-1, whereas supraglacial stream waters originating from overlying glacier ice contained <1 CFU L-1. Yeast cell number increased as the suspended-sediment content of the water samples increased. Basidiomycetous yeasts represent >80% of isolated strains (Cryptococcus spp. and Rhodotorula spp. were 33.3% and 17.8% of total strains, respectively). Culturable yeasts were psychrotolerant, predominantly obligate aerobes and able to degrade organic macromolecules (e.g. starch, esters, lipids, proteins). To the authors' knowledge, this is the first study to report the presence of culturable yeasts in meltwaters originating from glaciers. On the basis of these results, it is reasonable to suppose that the viable yeasts observed in meltwaters derived predominantly from the subglacial zone and that they originated from the subglacial microbial community. Their metabolic abilities could contribute to the microbial activity occurring in subglacial environments.
Genome-wide bisulfite sensitivity profiling of yeast suggests bisulfite inhibits transcription.

PubMed

Segovia, Romulo; Mathew, Veena; Tam, Annie S; Stirling, Peter C

2017-09-01

Bisulfite, in the form of sodium bisulfite or metabisulfite, is used commercially as a food preservative. Bisulfite is used in the laboratory as a single-stranded DNA mutagen in epigenomic analyses of DNA methylation. Recently it has also been used on whole yeast cells to induce mutations in exposed single-stranded regions in vivo. To understand the effects of bisulfite on live cells we conducted a genome-wide screen for bisulfite sensitive mutants in yeast. Screening the deletion mutant array, and collections of essential gene mutants we define a genetic network of bisulfite sensitive mutants. Validation of screen hits revealed hyper-sensitivity of transcription and RNA processing mutants, rather than DNA repair pathways and follow-up analyses support a role in perturbation of RNA transactions. We propose a model in which bisulfite-modified nucleotides may interfere with transcription or RNA metabolism when used in vivo. Copyright © 2017 Elsevier B.V. All rights reserved.
Influence of different yeast cell wall preparations and their components on performance and immune and metabolic pathways in Clostridium perfringens-challenged broiler chicks

USDA-ARS?s Scientific Manuscript database

A study was conducted to evaluate the influence of purification of yeast cell wall (YCW) preparations on broiler performance, and immunogenic and metabolic pathways under microbial challenge. A total of 240 day-of-hatch chicks were distributed among two battery brooder units (48 pens; 5 birds/pen; ...

The Fermentative and Aromatic Ability of Kloeckera and Hanseniaspora Yeasts

NASA Astrophysics Data System (ADS)

Díaz-Montaño, Dulce M.; de Jesús Ramírez Córdova, J.

Spontaneous alcoholic fermentation from grape, agave and others musts into an alcoholic beverage is usually characterized by the presence of several non-Saccharomyces yeasts. These genera yeasts are dominant in the early stages of the alcoholic fermentation. However the genera Hanseniaspora and Kloeckera may survive at a significant level during fermentation and can influence the chemical composition of the beverage. Several strains belonging to the species Kloeckera api-culata and Hanseniaspora guilliermondii have been extensively studied in relation to the formation of some metabolic compounds affecting the bouquet of the final product. Indeed some apiculate yeast showed positive oenological properties and their use in the alcoholic fermentations has been suggested to enhance the aroma and flavor profiles. The non- Saccharomyces yeasts have the capability to produce and secrete enzymes in the medium, such as β -glucosidases, which release monoterpenes derived from their glycosylated form. These compounds contribute to the higher fruit-like characteristic of final product. This chapter reviews metabolic activity of Kloeckera and Hanseniaspora yeasts in several aspects: fermentative capability, aromatic compounds production and transformation of aromatic precursor present in the must, also covers the molecular methods for identifying of the yeast
Methods to Measure Lipophagy in Yeast.

PubMed

Cristobal-Sarramian, A; Radulovic, M; Kohlwein, S D

2017-01-01

Maintenance of cellular and organismal lipid homeostasis is critical for life, and any deviation from a balanced equilibrium between fat uptake and degradation may have deleterious consequences, resulting in severe lipid-associated disorders. Excess fat is typically stored in cytoplasmic organelles termed "lipid droplets" (LDs); to adjust for a constantly fluctuating supply of and demand for cellular fat, these organelles are metabolically highly dynamic and subject to multiple levels of regulation. In addition to a well-described cytosolic lipid degradation pathway, recent evidence underscores the importance of "lipophagy" in cellular lipid homeostasis, i.e., the degradation of LD by autophagy in the lysosome/vacuole. Pioneering work in yeast mutant models has unveiled the requirement of key components of the autophagy machinery, providing evidence for a highly conserved process of lipophagy from yeast to man. However, further work is required to unveil the intricate metabolic interaction between LD metabolism and autophagy to sustain membrane homeostasis and cellular survival. © 2017 Elsevier Inc. All rights reserved.
Evolutionarily engineered ethanologenic yeast detoxifies lignocellulosic biomass conversion inhibitors by reprogrammed pathways

PubMed Central

Ma, Menggen; Song, Mingzhou

2010-01-01

Lignocellulosic biomass conversion inhibitors, furfural and HMF, inhibit microbial growth and interfere with subsequent fermentation of ethanol, posing significant challenges for a sustainable cellulosic ethanol conversion industry. Numerous yeast genes were found to be associated with the inhibitor tolerance. However, limited knowledge is available about mechanisms of the tolerance and the detoxification of the biomass conversion inhibitors. Using a robust standard for absolute mRNA quantification assay and a recently developed tolerant ethanologenic yeast Saccharomyces cerevisiae NRRL Y-50049, we investigate pathway-based transcription profiles relevant to the yeast tolerance and the inhibitor detoxification. Under the synergistic inhibitory challenges by furfural and HMF, Y-50049 was able to withstand the inhibitor stress, in situ detoxify furfural and HMF, and produce ethanol, while its parental control Y-12632 failed to function till 65 h after incubation. The tolerant strain Y-50049 displayed enriched genetic background with significantly higher abundant of transcripts for at least 16 genes than a non-tolerant parental strain Y-12632. The enhanced expression of ZWF1 appeared to drive glucose metabolism in favor of pentose phosphate pathway over glycolysis at earlier steps of glucose metabolisms. Cofactor NAD(P)H generation steps were likely accelerated by enzymes encoded by ZWF1, GND1, GND2, TDH1, and ALD4. NAD(P)H-dependent aldehyde reductions including conversion of furfural and HMF, in return, provided sufficient NAD(P)+ for NAD(P)H regeneration in the yeast detoxification pathways. Enriched genetic background and a well maintained redox balance through reprogrammed expression responses of Y-50049 were accountable for the acquired tolerance and detoxification of furfural to furan methanol and HMF to furan dimethanol. We present significant gene interactions and regulatory networks involved in NAD(P)H regenerations and functional aldehyde reductions under
Linking gene regulation and the exo-metabolome: A comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast

PubMed Central

Rossouw, Debra; Næs, Tormod; Bauer, Florian F

2008-01-01

Background 'Omics' tools provide novel opportunities for system-wide analysis of complex cellular functions. Secondary metabolism is an example of a complex network of biochemical pathways, which, although well mapped from a biochemical point of view, is not well understood with regards to its physiological roles and genetic and biochemical regulation. Many of the metabolites produced by this network such as higher alcohols and esters are significant aroma impact compounds in fermentation products, and different yeast strains are known to produce highly divergent aroma profiles. Here, we investigated whether we can predict the impact of specific genes of known or unknown function on this metabolic network by combining whole transcriptome and partial exo-metabolome analysis. Results For this purpose, the gene expression levels of five different industrial wine yeast strains that produce divergent aroma profiles were established at three different time points of alcoholic fermentation in synthetic wine must. A matrix of gene expression data was generated and integrated with the concentrations of volatile aroma compounds measured at the same time points. This relatively unbiased approach to the study of volatile aroma compounds enabled us to identify candidate genes for aroma profile modification. Five of these genes, namely YMR210W, BAT1, AAD10, AAD14 and ACS1 were selected for overexpression in commercial wine yeast, VIN13. Analysis of the data show a statistically significant correlation between the changes in the exo-metabome of the overexpressing strains and the changes that were predicted based on the unbiased alignment of transcriptomic and exo-metabolomic data. Conclusion The data suggest that a comparative transcriptomics and metabolomics approach can be used to identify the metabolic impacts of the expression of individual genes in complex systems, and the amenability of transcriptomic data to direct applications of biotechnological relevance. PMID:18990252
A Novel Hybrid Iron Regulation Network Combines Features from Pathogenic and Nonpathogenic Yeasts.

PubMed

Gerwien, Franziska; Safyan, Abu; Wisgott, Stephanie; Hille, Fabrice; Kaemmer, Philipp; Linde, Jörg; Brunke, Sascha; Kasper, Lydia; Hube, Bernhard

2016-10-18

Iron is an essential micronutrient for both pathogens and their hosts, which restrict iron availability during infections in an effort to prevent microbial growth. Successful human pathogens like the yeast Candida glabrata have thus developed effective iron acquisition strategies. Their regulation has been investigated well for some pathogenic fungi and in the model organism Saccharomyces cerevisiae, which employs an evolutionarily derived system. Here, we show that C. glabrata uses a regulation network largely consisting of components of the S. cerevisiae regulon but also of elements of other pathogenic fungi. Specifically, similarly to baker's yeast, Aft1 is the main positive regulator under iron starvation conditions, while Cth2 degrades mRNAs encoding iron-requiring enzymes. However, unlike the case with S. cerevisiae, a Sef1 ortholog is required for full growth under iron limitation conditions, making C. glabrata an evolutionary intermediate to SEF1-dependent fungal pathogens. Therefore, C. glabrata has evolved an iron homeostasis system which seems to be unique within the pathogenic fungi. The fungus Candida glabrata represents an evolutionarily close relative of the well-studied and benign baker's yeast and model organism Saccharomyces cerevisiae On the other hand, C. glabrata is an important opportunistic human pathogen causing both superficial and systemic infections. The ability to acquire trace metals, in particular, iron, and to tightly regulate this process during infection is considered an important virulence attribute of a variety of pathogens. Importantly, S. cerevisiae uses a highly derivative regulatory system distinct from those of other fungi. Until now, the regulatory mechanism of iron homeostasis in C. glabrata has been mostly unknown. Our study revealed a hybrid iron regulation network that is unique to C. glabrata and is placed at an evolutionary midpoint between those of S. cerevisiae and related fungal pathogens. We thereby
Efficient searching and annotation of metabolic networks using chemical similarity.

PubMed

Pertusi, Dante A; Stine, Andrew E; Broadbelt, Linda J; Tyo, Keith E J

2015-04-01

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Python files will be available for download at http://tyolab.northwestern.edu/tools/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Yeasts: providing questions and answers for modern biology.

PubMed

Dickinson, J R

2000-01-01

Yeasts are to be found in virtually every conceivable niche on this planet and are amazingly varied in their shapes ('morphologies'), life cycles, metabolic capabilities, potentials for use in industrial processes, abilities to spoil food and drink or to act as dangerous human pathogens. This review describes four very different species of yeast to illustrate some of the diversity which exists and, in the case of one of them, Saccharomyces cerevisiae (the familiar baker's or brewer's yeast), the extent of both our knowledge and ignorance.
Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

PubMed Central

von Kamp, Axel; Klamt, Steffen

2014-01-01

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly
Metabolic flux profiling of recombinant protein secreting Pichia pastoris growing on glucose:methanol mixtures

PubMed Central

2012-01-01

Background The methylotrophic yeast Pichia pastoris has emerged as one of the most promising yeast hosts for the production of heterologous proteins. Mixed feeds of methanol and a multicarbon source instead of methanol as sole carbon source have been shown to improve product productivities and alleviate metabolic burden derived from protein production. Nevertheless, systematic quantitative studies on the relationships between the central metabolism and recombinant protein production in P. pastoris are still rather limited, particularly when growing this yeast on mixed carbon sources, thus hampering future metabolic network engineering strategies for improved protein production. Results The metabolic flux distribution in the central metabolism of P. pastoris growing on a mixed feed of glucose and methanol was analyzed by Metabolic Flux Analysis (MFA) using 13C-NMR-derived constraints. For this purpose, we defined new flux ratios for methanol assimilation pathways in P. pastoris cells growing on glucose:methanol mixtures. By using this experimental approach, the metabolic burden caused by the overexpression and secretion of a Rhizopus oryzae lipase (Rol) in P. pastoris was further analyzed. This protein has been previously shown to trigger the unfolded protein response in P. pastoris. A series of 13C-tracer experiments were performed on aerobic chemostat cultivations with a control and two different Rol producing strains growing at a dilution rate of 0.09 h−1 using a glucose:methanol 80:20 (w/w) mix as carbon source. The MFA performed in this study reveals a significant redistristribution of carbon fluxes in the central carbon metabolism when comparing the two recombinant strains vs the control strain, reflected in increased glycolytic, TCA cycle and NADH regeneration fluxes, as well as higher methanol dissimilation rates. Conclusions Overall, a further 13C-based MFA development to characterise the central metabolism of methylotrophic yeasts when growing on mixed
Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

PubMed

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; Ng, Patrick; Khraiwesh, Basel; Jaiswal, Ashish; Jijakli, Kenan; Koussa, Joseph; Nelson, David R; Cai, Hong; Yang, Xinping; Chang, Roger L; Papin, Jason; Yu, Haiyuan; Balaji, Santhanam; Salehi-Ashtiani, Kourosh

2016-07-19

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.
Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

PubMed

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.
A Mutation in PGM2 Causing Inefficient Galactose Metabolism in the Probiotic Yeast Saccharomyces boulardii.

PubMed

Liu, Jing-Jing; Zhang, Guo-Chang; Kong, In Iok; Yun, Eun Ju; Zheng, Jia-Qi; Kweon, Dae-Hyuk; Jin, Yong-Su

2018-05-15

The probiotic yeast Saccharomyces boulardii has been extensively studied for the prevention and treatment of diarrheal diseases, and it is now commercially available in some countries. S. boulardii displays notable phenotypic characteristics, such as a high optimal growth temperature, high tolerance against acidic conditions, and the inability to form ascospores, which differentiate S. boulardii from Saccharomyces cerevisiae The majority of prior studies stated that S. boulardii exhibits sluggish or halted galactose utilization. Nonetheless, the molecular mechanisms underlying inefficient galactose uptake have yet to be elucidated. When the galactose utilization of a widely used S. boulardii strain, ATCC MYA-796, was examined under various culture conditions, the S. boulardii strain could consume galactose, but at a much lower rate than that of S. cerevisiae While all GAL genes were present in the S. boulardii genome, according to analysis of genomic sequencing data in a previous study, a point mutation (G1278A) in PGM2 , which codes for phosphoglucomutase, was identified in the genome of the S. boulardii strain. As the point mutation resulted in the truncation of the Pgm2 protein, which is known to play a pivotal role in galactose utilization, we hypothesized that the truncated Pgm2 might be associated with inefficient galactose metabolism. Indeed, complementation of S. cerevisiae PGM2 in S. boulardii restored galactose utilization. After reverting the point mutation to a full-length PGM2 in S. boulardii by Cas9-based genome editing, the growth rates of wild-type (with a truncated PGM2 gene) and mutant (with a full-length PGM2 ) strains with glucose or galactose as the carbon source were examined. As expected, the mutant (with a full-length PGM2 ) was able to ferment galactose faster than the wild-type strain. Interestingly, the mutant showed a lower growth rate than that of the wild-type strain on glucose at 37°C. Also, the wild-type strain was enriched in the
Foundations of metabolic organization: coherence as a basis of computational properties in metabolic networks.

PubMed

Igamberdiev, A U

1999-04-01

Biological organization is based on the coherent energy transfer allowing for macromolecules to operate with high efficiency and realize computation. Computation is executed with virtually 100% efficiency via the coherent operation of molecular machines in which low-energy recognitions trigger energy-driven non-equilibrium dynamic processes. The recognition process is of quantum mechanical nature being a non-demolition measurement. It underlies the enzymatic conversion of a substrate into the product (an elementary metabolic phenomenon); the switching via separation of the direct and reverse routes in futile cycles provides the generation and complication of metabolic networks (coherence within cycles is maintained by the supramolecular organization of enzymes); the genetic level corresponding to the appearance of digital information is based on reflective arrows (catalysts realize their own self-reproduction) and operation of hypercycles. Every metabolic cycle via reciprocal regulation of both its halves can generate rhythms and spatial structures (resulting from the temporally organized depositions from the cycles). Via coherent events which percolate from the elementary submolecular level to organismic entities, self-assembly based on the molecular complementarity is realized and the dynamic informational field operating within the metabolic network is generated.
Experimental determination of group flux control coefficients in metabolic networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, T.W.; Shimizu, Hiroshi; Stephanopoulos, G.

1998-04-20

Grouping of reactions around key metabolite branch points can facilitate the study of metabolic control of complex metabolic networks. This top-down Metabolic Control Analysis is exemplified through the introduction of group control coefficients whose magnitudes provide a measure of the relative impact of each reaction group on the overall network flux, as well as on the overall network stability, following enzymatic amplification. In this article, the authors demonstrate the application of previously developed theory to the determination of group flux control coefficients. Experimental data for the changes in metabolic fluxes obtained in response to the introduction of six different environmentalmore » perturbations are used to determine the group flux control coefficients for three reaction groups formed around the phosphoenolpyruvate/pyruvate branch point. The consistency of the obtained group flux control coefficient estimates is systematically analyzed to ensure that all necessary conditions are satisfied. The magnitudes of the determined control coefficients suggest that the control of lysine production flux in Corynebacterium glutamicum cells at a growth base state resides within the lysine biosynthetic pathway that begins with the PEP/PYR carboxylation anaplorotic pathway.« less
A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

PubMed Central

2013-01-01

Background Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. Results Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia
YeastFab: the design and construction of standard biological parts for metabolic engineering in Saccharomyces cerevisiae

PubMed Central

Guo, Yakun; Dong, Junkai; Zhou, Tong; Auxillos, Jamie; Li, Tianyi; Zhang, Weimin; Wang, Lihui; Shen, Yue; Luo, Yisha; Zheng, Yijing; Lin, Jiwei; Chen, Guo-Qiang; Wu, Qingyu; Cai, Yizhi; Dai, Junbiao

2015-01-01

It is a routine task in metabolic engineering to introduce multicomponent pathways into a heterologous host for production of metabolites. However, this process sometimes may take weeks to months due to the lack of standardized genetic tools. Here, we present a method for the design and construction of biological parts based on the native genes and regulatory elements in Saccharomyces cerevisiae. We have developed highly efficient protocols (termed YeastFab Assembly) to synthesize these genetic elements as standardized biological parts, which can be used to assemble transcriptional units in a single-tube reaction. In addition, standardized characterization assays are developed using reporter constructs to calibrate the function of promoters. Furthermore, the assembled transcription units can be either assayed individually or applied to construct multi-gene metabolic pathways, which targets a genomic locus or a receiving plasmid effectively, through a simple in vitro reaction. Finally, using β-carotene biosynthesis pathway as an example, we demonstrate that our method allows us not only to construct and test a metabolic pathway in several days, but also to optimize the production through combinatorial assembly of a pathway using hundreds of regulatory biological parts. PMID:25956650
What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633
A Novel Hybrid Iron Regulation Network Combines Features from Pathogenic and Nonpathogenic Yeasts

PubMed Central

Gerwien, Franziska; Safyan, Abu; Wisgott, Stephanie; Hille, Fabrice; Kaemmer, Philipp; Linde, Jörg; Brunke, Sascha; Kasper, Lydia

2016-01-01

ABSTRACT Iron is an essential micronutrient for both pathogens and their hosts, which restrict iron availability during infections in an effort to prevent microbial growth. Successful human pathogens like the yeast Candida glabrata have thus developed effective iron acquisition strategies. Their regulation has been investigated well for some pathogenic fungi and in the model organism Saccharomyces cerevisiae, which employs an evolutionarily derived system. Here, we show that C. glabrata uses a regulation network largely consisting of components of the S. cerevisiae regulon but also of elements of other pathogenic fungi. Specifically, similarly to baker’s yeast, Aft1 is the main positive regulator under iron starvation conditions, while Cth2 degrades mRNAs encoding iron-requiring enzymes. However, unlike the case with S. cerevisiae, a Sef1 ortholog is required for full growth under iron limitation conditions, making C. glabrata an evolutionary intermediate to SEF1-dependent fungal pathogens. Therefore, C. glabrata has evolved an iron homeostasis system which seems to be unique within the pathogenic fungi. PMID:27795405
Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

PubMed

Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

2016-05-01

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
Novel insights into the architecture and protein interaction network of yeast eIF3.

PubMed

Khoshnevis, Sohail; Hauer, Florian; Milón, Pohl; Stark, Holger; Ficner, Ralf

2012-12-01

Translation initiation in eukaryotes is a multistep process requiring the orchestrated interaction of several eukaryotic initiation factors (eIFs). The largest of these factors, eIF3, forms the scaffold for other initiation factors, promoting their binding to the 40S ribosomal subunit. Biochemical and structural studies on eIF3 need highly pure eIF3. However, natively purified eIF3 comprise complexes containing other proteins such as eIF5. Therefore we have established in vitro reconstitution protocols for Saccharomyces cerevisiae eIF3 using its five recombinantly expressed and purified subunits. This reconstituted eIF3 complex (eIF3(rec)) exhibits the same size and activity as the natively purified eIF3 (eIF3(nat)). The homogeneity and stoichiometry of eIF3(rec) and eIF3(nat) were confirmed by analytical size exclusion chromatography, mass spectrometry, and multi-angle light scattering, demonstrating the presence of one copy of each subunit in the eIF3 complex. The reconstituted and native eIF3 complexes were compared by single-particle electron microscopy showing a high degree of structural conservation. The interaction network between eIF3 proteins was studied by means of limited proteolysis, analytical size exclusion chromatography, in vitro binding assays, and isothermal titration calorimetry, unveiling distinct protein domains and subcomplexes that are critical for the integrity of the protein network in yeast eIF3. Taken together, the data presented here provide a novel procedure to obtain highly pure yeast eIF3, suitable for biochemical and structural analysis, in addition to a detailed picture of the network of protein interactions within this complex.

Uncoupling reproduction from metabolism extends chronological lifespan in yeast

PubMed Central

Nagarajan, Saisubramanian; Kruckeberg, Arthur L.; Schmidt, Karen H.; Kroll, Evgueny; Hamilton, Morgan; McInnerney, Kate; Summers, Ryan; Taylor, Timothy; Rosenzweig, Frank

2014-01-01

Studies of replicative and chronological lifespan in Saccharomyces cerevisiae have advanced understanding of longevity in all eukaryotes. Chronological lifespan in this species is defined as the age-dependent viability of nondividing cells. To date this parameter has only been estimated under calorie restriction, mimicked by starvation. Because postmitotic cells in higher eukaryotes often do not starve, we developed a model yeast system to study cells as they age in the absence of calorie restriction. Yeast cells were encapsulated in a matrix consisting of calcium alginate to form ∼3 mm beads that were packed into bioreactors and fed ad libitum. Under these conditions cells ceased to divide, became heat shock and zymolyase resistant, yet retained high fermentative capacity. Over the course of 17 d, immobilized yeast cells maintained >95% viability, whereas the viability of starving, freely suspended (planktonic) cells decreased to <10%. Immobilized cells exhibited a stable pattern of gene expression that differed markedly from growing or starving planktonic cells, highly expressing genes in glycolysis, cell wall remodeling, and stress resistance, but decreasing transcription of genes in the tricarboxylic acid cycle, and genes that regulate the cell cycle, including master cyclins CDC28 and CLN1. Stress resistance transcription factor MSN4 and its upstream effector RIM15 are conspicuously up-regulated in the immobilized state, and an immobilized rim15 knockout strain fails to exhibit the long-lived, growth-arrested phenotype, suggesting that altered regulation of the Rim15-mediated nutrient-sensing pathway plays an important role in extending yeast chronological lifespan under calorie-unrestricted conditions. PMID:24706810
Oxidative Stress and Programmed Cell Death in Yeast

PubMed Central

Farrugia, Gianluca; Balzan, Rena

2012-01-01

Yeasts, such as Saccharomyces cerevisiae, have long served as useful models for the study of oxidative stress, an event associated with cell death and severe human pathologies. This review will discuss oxidative stress in yeast, in terms of sources of reactive oxygen species (ROS), their molecular targets, and the metabolic responses elicited by cellular ROS accumulation. Responses of yeast to accumulated ROS include upregulation of antioxidants mediated by complex transcriptional changes, activation of pro-survival pathways such as mitophagy, and programmed cell death (PCD) which, apart from apoptosis, includes pathways such as autophagy and necrosis, a form of cell death long considered accidental and uncoordinated. The role of ROS in yeast aging will also be discussed. PMID:22737670
Expanding xylose metabolism in yeast for plant cell wall conversion to biofuels.

PubMed

Li, Xin; Yu, Vivian Yaci; Lin, Yuping; Chomvong, Kulika; Estrela, Raíssa; Park, Annsea; Liang, Julie M; Znameroski, Elizabeth A; Feehan, Joanna; Kim, Soo Rin; Jin, Yong-Su; Glass, N Louise; Cate, Jamie H D

2015-02-03

Sustainable biofuel production from renewable biomass will require the efficient and complete use of all abundant sugars in the plant cell wall. Using the cellulolytic fungus Neurospora crassa as a model, we identified a xylodextrin transport and consumption pathway required for its growth on hemicellulose. Reconstitution of this xylodextrin utilization pathway in Saccharomyces cerevisiae revealed that fungal xylose reductases act as xylodextrin reductases, producing xylosyl-xylitol oligomers as metabolic intermediates. These xylosyl-xylitol intermediates are generated by diverse fungi and bacteria, indicating that xylodextrin reduction is widespread in nature. Xylodextrins and xylosyl-xylitol oligomers are then hydrolyzed by two hydrolases to generate intracellular xylose and xylitol. Xylodextrin consumption using a xylodextrin transporter, xylodextrin reductases and tandem intracellular hydrolases in cofermentations with sucrose and glucose greatly expands the capacity of yeast to use plant cell wall-derived sugars and has the potential to increase the efficiency of both first-generation and next-generation biofuel production.
Expanding xylose metabolism in yeast for plant cell wall conversion to biofuels

PubMed Central

Li, Xin; Yu, Vivian Yaci; Lin, Yuping; Chomvong, Kulika; Estrela, Raíssa; Park, Annsea; Liang, Julie M; Znameroski, Elizabeth A; Feehan, Joanna; Kim, Soo Rin; Jin, Yong-Su; Glass, N Louise; Cate, Jamie HD

2015-01-01

Sustainable biofuel production from renewable biomass will require the efficient and complete use of all abundant sugars in the plant cell wall. Using the cellulolytic fungus Neurospora crassa as a model, we identified a xylodextrin transport and consumption pathway required for its growth on hemicellulose. Reconstitution of this xylodextrin utilization pathway in Saccharomyces cerevisiae revealed that fungal xylose reductases act as xylodextrin reductases, producing xylosyl-xylitol oligomers as metabolic intermediates. These xylosyl-xylitol intermediates are generated by diverse fungi and bacteria, indicating that xylodextrin reduction is widespread in nature. Xylodextrins and xylosyl-xylitol oligomers are then hydrolyzed by two hydrolases to generate intracellular xylose and xylitol. Xylodextrin consumption using a xylodextrin transporter, xylodextrin reductases and tandem intracellular hydrolases in cofermentations with sucrose and glucose greatly expands the capacity of yeast to use plant cell wall-derived sugars and has the potential to increase the efficiency of both first-generation and next-generation biofuel production. DOI: http://dx.doi.org/10.7554/eLife.05896.001 PMID:25647728
On-line metabolic pathway analysis based on metabolic signal flow diagram.

PubMed

Shi, H; Shimizu, K

In this work, an integrated modeling approach based on a metabolic signal flow diagram and cellular energetics was used to model the metabolic pathway analysis for the cultivation of yeast on glucose. This approach enables us to make a clear analysis of the flow direction of the carbon fluxes in the metabolic pathways as well as of the degree of activation of a particular pathway for the synthesis of biomaterials for cell growth. The analyses demonstrate that the main metabolic pathways of Saccharomyces cerevisiae change significantly during batch culture. Carbon flow direction is toward glycolysis to satisfy the increase of requirement for precursors and energy. The enzymatic activation of TCA cycle seems to always be at normal level, which may result in the overflow of ethanol due to its limited capacity. The advantage of this approach is that it adopts both virtues of the metabolic signal flow diagram and the simple network analysis method, focusing on the investigation of the flow directions of carbon fluxes and the degree of activation of a particular pathway or reaction loop. All of the variables used in the model equations were determined on-line; the information obtained from the calculated metabolic coefficients may result in a better understanding of cell physiology and help to evaluate the state of the cell culture process. Copyright 1998 John Wiley & Sons, Inc.
Yeast-based biosensors: design and applications.

PubMed

Adeniran, Adebola; Sherer, Michael; Tyo, Keith E J

2015-02-01

Yeast-based biosensing (YBB) is an exciting research area, as many studies have demonstrated the use of yeasts to accurately detect specific molecules. Biosensors incorporating various yeasts have been reported to detect an incredibly large range of molecules including but not limited to odorants, metals, intracellular metabolites, carcinogens, lactate, alcohols, and sugars. We review the detection strategies available for different types of analytes, as well as the wide range of output methods that have been incorporated with yeast biosensors. We group biosensors into two categories: those that are dependent upon transcription of a gene to report the detection of a desired molecule and those that are independent of this reporting mechanism. Transcription-dependent biosensors frequently depend on heterologous expression of sensing elements from non-yeast organisms, a strategy that has greatly expanded the range of molecules available for detection by YBBs. Transcription-independent biosensors circumvent the problem of sensing difficult-to-detect analytes by instead relying on yeast metabolism to generate easily detected molecules when the analyte is present. The use of yeast as the sensing element in biosensors has proven to be successful and continues to hold great promise for a variety of applications. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.
Data-driven integration of genome-scale regulatory and metabolic network models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less
Data-driven integration of genome-scale regulatory and metabolic network models

DOE PAGES

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; ...

2015-05-05

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less
The yeast actin cytoskeleton.

PubMed

Mishra, Mithilesh; Huang, Junqi; Balasubramanian, Mohan K

2014-03-01

The actin cytoskeleton is a complex network of dynamic polymers, which plays an important role in various fundamental cellular processes, including maintenance of cell shape, polarity, cell division, cell migration, endocytosis, vesicular trafficking, and mechanosensation. Precise spatiotemporal assembly and disassembly of actin structures is regulated by the coordinated activity of about 100 highly conserved accessory proteins, which nucleate, elongate, cross-link, and sever actin filaments. Both in vivo studies in a wide range of organisms from yeast to metazoans and in vitro studies of purified proteins have helped shape the current understanding of actin dynamics and function. Molecular genetics, genome-wide functional analysis, sophisticated real-time imaging, and ultrastructural studies in concert with biochemical analysis have made yeast an attractive model to understand the actin cytoskeleton, its molecular dynamics, and physiological function. Studies of the yeast actin cytoskeleton have contributed substantially in defining the universal mechanism regulating actin assembly and disassembly in eukaryotes. Here, we review some of the important insights generated by the study of actin cytoskeleton in two important yeast models the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.
Quantitative petri net model of gene regulated metabolic networks in the cell.

PubMed

Chen, Ming; Hofestädt, Ralf

2011-01-01

A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.
Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less
Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE PAGES

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; ...

2016-06-14

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less
Reprogrammed Glucose Metabolic Pathways of Inhibitor-Tolerant Yeast

USDA-ARS?s Scientific Manuscript database

Representative inhibitory compounds such as furfural and 5-hydroxymethylfurfural generated from lignocellulosic biomass pretreatment inhibit yeast growth and interfere with the subsequent ethanol fermentation. Evolutionary engineering under laboratory settings is a powerful tool that can be used to ...
Reprogrammed glucose metabolic pathways of inhibitor-tolerant yeast

USDA-ARS?s Scientific Manuscript database

Representative inhibitory compounds such as furfural and 5-hydroxymethylfurfural generated from lignocellulosic biomass pretreatment inhibit yeast growth and interfere with the subsequent ethanol fermentation. Evolutionary engineering under laboratory settings is a powerful tool that can be used to...
Whole-Genome Analysis of Three Yeast Strains Used for Production of Sherry-Like Wines Revealed Genetic Traits Specific to Flor Yeasts

PubMed Central

Eldarov, Mikhail A.; Beletsky, Alexey V.; Tanashchuk, Tatiana N.; Kishkovskaya, Svetlana A.; Ravin, Nikolai V.; Mardanov, Andrey V.

2018-01-01

Flor yeast strains represent a specialized group of Saccharomyces cerevisiae yeasts used for biological wine aging. We have sequenced the genomes of three flor strains originated from different geographic regions and used for production of sherry-like wines in Russia. According to the obtained phylogeny of 118 yeast strains, flor strains form very tight cluster adjacent to the main wine clade. SNP analysis versus available genomes of wine and flor strains revealed 2,270 genetic variants in 1,337 loci specific to flor strains. Gene ontology analysis in combination with gene content evaluation revealed a complex landscape of possibly adaptive genetic changes in flor yeast, related to genes associated with cell morphology, mitotic cell cycle, ion homeostasis, DNA repair, carbohydrate metabolism, lipid metabolism, and cell wall biogenesis. Pangenomic analysis discovered the presence of several well-known “non-reference” loci of potential industrial importance. Events of gene loss included deletions of asparaginase genes, maltose utilization locus, and FRE-FIT locus involved in iron transport. The latter in combination with a flor-yeast-specific mutation in the Aft1 transcription factor gene is likely to be responsible for the discovered phenotype of increased iron sensitivity and improved iron uptake of analyzed strains. Expansion of the coding region of the FLO11 flocullin gene and alteration of the balance between members of the FLO gene family are likely to positively affect the well-known propensity of flor strains for velum formation. Our study provides new insights in the nature of genetic variation in flor yeast strains and demonstrates that different adaptive properties of flor yeast strains could have evolved through different mechanisms of genetic variation. PMID:29867869
Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence
Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae

PubMed Central

Feng, Quanzhou; Weber, Scott A.; Li, Shizhong

2018-01-01

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production. PMID:29621349
Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae.

PubMed

Feng, Quanzhou; Liu, Z Lewis; Weber, Scott A; Li, Shizhong

2018-01-01

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production.
Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics

PubMed Central

Costenoble, Roeland; Picotti, Paola; Reiter, Lukas; Stallmach, Robert; Heinemann, Matthias; Sauer, Uwe; Aebersold, Ruedi

2011-01-01

Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes. PMID:21283140
Network-level architecture and the evolutionary potential of underground metabolism.

PubMed

Notebaart, Richard A; Szappanos, Balázs; Kintses, Bálint; Pál, Ferenc; Györkei, Ádám; Bogos, Balázs; Lázár, Viktória; Spohn, Réka; Csörgő, Bálint; Wagner, Allon; Ruppin, Eytan; Pál, Csaba; Papp, Balázs

2014-08-12

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

Human metabolism and renal excretion of selenium compounds after oral ingestion of sodium selenite and selenized yeast dependent on the trimethylselenium ion (TMSe) status.

PubMed

Jäger, Thomas; Drexler, Hans; Göen, Thomas

2016-05-01

A human in vivo metabolism study was carried out to investigate the impact of the trimethylselenium ion (TMSe) status on metabolism and toxicokinetics of sodium selenite and selenized yeast. Nine healthy human volunteers were orally exposed to 200 µg selenium as sodium selenite and seven with selenized yeast (100 µg selenium). In each intervention group, three subjects belong to TMSe eliminators. Blood samples were withdrawn before and up to 6 h after administration. Urine samples were collected before and within 24 h after administration. Total selenium (Se) was quantified in blood plasma and urine and low molecular Se species in urine. Selenium concentration in plasma increased from 84.5 ± 13.2 µg Se/L before to 97.4 ± 13.2 µg Se/L 2-3 h after selenite supplementation and 89.5 ± 12.9 µg Se/L to 92.1 ± 13.9 µg Se/L after selenized yeast intake. The oral ingestion caused an additional Se elimination via urine of 16.9 ± 10.6 µg/24 h (TMSe elim.: 10.8 ± 6.9 µg/24 h; non-TMSe elim.: 20.0 ± 11.3 µg Se/24 h) after selenite exposure and 11.8 ± 4.1 µg/24 h (TMSe elim.: 10.8 ± 4.6 µg/24 h; non-TMSe elim.: 12.6 ± 4.2 µg Se/24 h) after selenized yeast exposure. Methyl-2-acetamido-2-deoxy-1-seleno-β-D-galactopyranoside (SeSug1) was the main metabolite in all urine samples, whereas TMSe was another main metabolite in TMSe eliminators' urine. After selenite exposure, a small amount of the dose (0.5 ± 0.2 %) was oxidized to selenate and rapidly excreted via urine. With the exception of selenite exposure in TMSe eliminators, the comparison of total Se and the sum of quantified Se species revealed a high renal portion of unidentified species. The study indicated a different metabolism of inorganic and organic Se compounds in human, but also crucial differences of Se metabolism in TMSe eliminators and non-TMSe eliminators.
Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges.

PubMed

Töpfer, Nadine; Seaver, Samuel M D; Aharoni, Asaph

2018-01-01

In the last decade, plant genome-scale modeling has developed rapidly and modeling efforts have advanced from representing metabolic behavior of plant heterotrophic cell suspensions to studying the complex interplay of cell types, tissues, and organs. A crucial driving force for such developments is the availability and integration of "omics" data (e.g., transcriptomics, proteomics, and metabolomics) which enable the reconstruction, extraction, and application of context-specific metabolic networks. In this chapter, we demonstrate a workflow to integrate gas chromatography coupled to mass spectrometry (GC-MS)-based metabolomics data of tomato fruit pericarp (flesh) tissue, at five developmental stages, with a genome-scale reconstruction of tomato metabolism. This method allows for the extraction of context-specific networks reflecting changing activities of metabolic pathways throughout fruit development and maturation.
Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the
MPact: the MIPS protein interaction resource on yeast.

PubMed

Güldener, Ulrich; Münsterkötter, Martin; Oesterheld, Matthias; Pagel, Philipp; Ruepp, Andreas; Mewes, Hans-Werner; Stümpflen, Volker

2006-01-01

In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein-protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107-1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539-5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through http://mips.gsf.de/genre/proj/mpact.
Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

PubMed Central

Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

2016-01-01

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can
[Yeast urinary tract infections. Multicentre study in 14 hospitals belonging to the Buenos Aires City Mycology Network].

PubMed

Maldonado, Ivana; Arechavala, Alicia; Guelfand, Liliana; Relloso, Silvia; Garbasz, Claudia

2016-01-01

Urinary tract infections are a frequent ailment in patients in intensive care units. Candida and other yeasts cause 5-12% of these infections. The value of the finding of any yeast is controversial, and there is no consensus about which parameters are adequate for differentiating urinary infections from colonization or contamination. To analyse the epidemiological characteristics of patients with funguria, to determine potential cut-off points in cultures (to distinguish an infection from other conditions), to identify the prevalent yeast species, and to determine the value of a second urine sample. A multicentre study was conducted in intensive care units of 14 hospitals in the Buenos Aires City Mycology Network. The first and second samples of urine from every patient were cultured. The presence of white cells and yeasts in direct examination, colony counts, and the identification of the isolated species, were evaluated. Yeasts grew in 12.2% of the samples. There was no statistical correlation between the number of white cells and the fungal colony-forming units. Eighty five percent of the patients had indwelling catheters. Funguria was not prevalent in women or in patients over the age of 65. Candida albicans, followed by Candida tropicalis, were the most frequently isolated yeasts. Candida parapsilosis and Candida glabrata appeared less frequently. The same species were isolated in 70% of second samples, and in 23% of the cases the second culture was negative. It was not possible to determine a useful cut-off point for colony counts to help in the diagnosis of urinary infections. As in other publications, C. albicans, followed by C. tropicalis, were the most prevalent species. Copyright © 2015 Asociación Española de Micología. Published by Elsevier Espana. All rights reserved.
Regulatory network rewiring for secondary metabolism in Arabidopsis thaliana under various conditions

PubMed Central

2014-01-01

Background Plant secondary metabolites are critical to various biological processes. However, the regulations of these metabolites are complex because of regulatory rewiring or crosstalk. To unveil how regulatory behaviors on secondary metabolism reshape biological processes, we constructed and analyzed a dynamic regulatory network of secondary metabolic pathways in Arabidopsis. Results The dynamic regulatory network was constructed through integrating co-expressed gene pairs and regulatory interactions. Regulatory interactions were either predicted by conserved transcription factor binding sites (TFBSs) or proved by experiments. We found that integrating two data (co-expression and predicted regulatory interactions) enhanced the number of highly confident regulatory interactions by over 10% compared with using single data. The dynamic changes of regulatory network systematically manifested regulatory rewiring to explain the mechanism of regulation, such as in terpenoids metabolism, the regulatory crosstalk of RAV1 (AT1G13260) and ATHB1 (AT3G01470) on HMG1 (hydroxymethylglutaryl-CoA reductase, AT1G76490); and regulation of RAV1 on epoxysqualene biosynthesis and sterol biosynthesis. Besides, we investigated regulatory rewiring with expression, network topology and upstream signaling pathways. Regulatory rewiring was revealed by the variability of genes’ expression: pathway genes and transcription factors (TFs) were significantly differentially expressed under different conditions (such as terpenoids biosynthetic genes in tissue experiments and E2F/DP family members in genotype experiments). Both network topology and signaling pathways supported regulatory rewiring. For example, we discovered correlation among the numbers of pathway genes, TFs and network topology: one-gene pathways (such as δ-carotene biosynthesis) were regulated by a fewer TFs, and were not critical to metabolic network because of their low degrees in topology. Upstream signaling pathways of 50
Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

PubMed Central

Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-01-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT’s unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the “symbiotic layout” of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu. PMID:27081850
Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

PubMed

Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-04-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.
Yeast nitrogen utilization in the phyllosphere during plant lifespan under regulation of autophagy

PubMed Central

Shiraishi, Kosuke; Oku, Masahide; Kawaguchi, Kosuke; Uchida, Daichi; Yurimoto, Hiroya; Sakai, Yasuyoshi

2015-01-01

Recently, microbe-plant interactions at the above-ground parts have attracted great attention. Here we describe nitrogen metabolism and regulation of autophagy in the methylotrophic yeast Candida boidinii, proliferating and surviving on the leaves of Arabidopsis thaliana. After quantitative analyses of yeast growth on the leaves of A. thaliana with the wild-type and several mutant yeast strains, we showed that on young leaves, nitrate reductase (Ynr1) was necessary for yeast proliferation, and the yeast utilized nitrate as nitrogen source. On the other hand, a newly developed methylamine sensor revealed appearance of methylamine on older leaves, and methylamine metabolism was induced in C. boidinii, and Ynr1 was subjected to degradation. Biochemical and microscopic analysis of Ynr1 in vitro during a shift of nitrogen source from nitrate to methylamine revealed that Ynr1 was transported to the vacuole being the cargo for biosynthetic cytoplasm-to-vacuole targeting (Cvt) pathway, and degraded. Our results reveal changes in the nitrogen source composition for phyllospheric yeasts during plant aging, and subsequent adaptation of the yeasts to this environmental change mediated by regulation of autophagy. PMID:25900611
Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

PubMed

Tomasi, Dardo G; Shokri-Kojori, Ehsan; Wiers, Corinde E; Kim, Sunny W; Demiral, Şukru B; Cabrera, Elizabeth A; Lindgren, Elsa; Miller, Gregg; Wang, Gene-Jack; Volkow, Nora D

2017-12-01

It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.
Random sampling of elementary flux modes in large-scale metabolic networks.

PubMed

Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

2012-09-15

The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.
Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

PubMed

Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

2017-01-01

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests , which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best
Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

PubMed Central

Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

2017-01-01

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of
Protein biogenesis machinery is a driver of replicative aging in yeast

PubMed Central

Janssens, Georges E; Meinema, Anne C; González, Javier; Wolters, Justina C; Schmidt, Alexander; Guryev, Victor; Bischoff, Rainer; Wit, Ernst C; Veenhoff, Liesbeth M; Heinemann, Matthias

2015-01-01

An integrated account of the molecular changes occurring during the process of cellular aging is crucial towards understanding the underlying mechanisms. Here, using novel culturing and computational methods as well as latest analytical techniques, we mapped the proteome and transcriptome during the replicative lifespan of budding yeast. With age, we found primarily proteins involved in protein biogenesis to increase relative to their transcript levels. Exploiting the dynamic nature of our data, we reconstructed high-level directional networks, where we found the same protein biogenesis-related genes to have the strongest ability to predict the behavior of other genes in the system. We identified metabolic shifts and the loss of stoichiometry in protein complexes as being consequences of aging. We propose a model whereby the uncoupling of protein levels of biogenesis-related genes from their transcript levels is causal for the changes occurring in aging yeast. Our model explains why targeting protein synthesis, or repairing the downstream consequences, can serve as interventions in aging. DOI: http://dx.doi.org/10.7554/eLife.08527.001 PMID:26422514
CMPF: class-switching minimized pathfinding in metabolic networks.

PubMed

Lim, Kevin; Wong, Limsoon

2012-01-01

The metabolic network is an aggregation of enzyme catalyzed reactions that converts one compound to another. Paths in a metabolic network are a sequence of enzymes that describe how a chemical compound of interest can be produced in a biological system. As the number of such paths is quite large, many methods have been developed to score paths so that the k-shortest paths represent the set of paths that are biologically meaningful or efficient. However, these approaches do not consider whether the sequence of enzymes can be manufactured in the same pathway/species/localization. As a result, a predicted sequence might consist of groups of enzymes that operate in distinct pathway/species/localization and may not truly reflect the events occurring within cell. We propose a path weighting method CMPF (Class-switching Minimized Pathfinder) to search for routes in a metabolic network which minimizes pathway switching. In biological terms, a pathway is a series of chemical reactions which define a specific function (e.g. glycolysis). We conjecture that routes that cross many pathways are inefficient since different pathways define different metabolic functions. In addition, native routes are also well characterized within pathways, suggesting that reasonable paths should not involve too many pathway switches. Our method can be generalized when reactions participate in a class set (e.g., pathways, species or cellular localization) so that the paths predicted have minimal class crossings. We show that our method generates k-paths that involve the least number of class switching. In addition, we also show that native paths are recoverable and alternative paths deviates less from native paths compared to other methods. This suggests that paths ranked by our method could be a way to predict paths that are likely to occur in biological systems.
Advancing secondary metabolite biosynthesis in yeast with synthetic biology tools.

PubMed

Siddiqui, Michael S; Thodey, Kate; Trenchard, Isis; Smolke, Christina D

2012-03-01

Secondary metabolites are an important source of high-value chemicals, many of which exhibit important pharmacological properties. These valuable natural products are often difficult to synthesize chemically and are commonly isolated through inefficient extractions from natural biological sources. As such, they are increasingly targeted for production by biosynthesis from engineered microorganisms. The budding yeast species Saccharomyces cerevisiae has proven to be a powerful microorganism for heterologous expression of biosynthetic pathways. S. cerevisiae's usefulness as a host organism is owed in large part to the wealth of knowledge accumulated over more than a century of intense scientific study. Yet many challenges are currently faced in engineering yeast strains for the biosynthesis of complex secondary metabolite production. However, synthetic biology is advancing the development of new tools for constructing, controlling, and optimizing complex metabolic pathways in yeast. Here, we review how the coupling between yeast biology and synthetic biology is advancing the use of S. cerevisiae as a microbial host for the construction of secondary metabolic pathways. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.
A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

PubMed

Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

2016-01-01

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.
Use of randomized sampling for analysis of metabolic networks.

PubMed

Schellenberger, Jan; Palsson, Bernhard Ø

2009-02-27

Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to study by Monte Carlo sampling of the solution space. These techniques have been applied extensively to the reaction rate (flux) space of networks, with more recent work focusing on dynamic/kinetic properties. Monte Carlo sampling as an analysis tool has many advantages, including the ability to work with missing data, the ability to apply post-processing techniques, and the ability to quantify uncertainty and to optimize experiments to reduce uncertainty. We present an overview of this emerging area of research in systems biology.
Breeding of Freeze-tolerant Yeast and the Mechanisms of Stress-tolerance

NASA Astrophysics Data System (ADS)

Hino, Akihiro

Frozen dough method have been adopted in the baking industry to reduce labor and to produce fresh breads in stores. New freeze-tolerant yeasts for frozen dough preparations were isolated from banana peel and identified. To obtain strains that have fermentative ability even after several months of frozen storage in fermented dough, we attempted to breed new freeze-tolerantstrain. The hybrid between S.cerevisiae, which is a isolated freeze-tolerant strain, and a strain isolated from bakers' yeast with sexual conjugation gave a good quality bread made from frozen dough method. Freeze-tolerant strains showed higher surviving and trehalose accumulating abilities than freeze-sensitive strains. The freeze tolerance of the yeasts was associated with the basal amount of intracellular trehalose after rapid degradation at the onset of the prefermentation period. The complicated metabolic pathway and the regulation system of trehalose in yeast cells are introduced. The trehalose synthesis may act as a metabolic buffer system which contribute to maintain the intracellular inorganic phosphate and as a feedback regulation system in the glycolysis. However, it is not known enough how the trehalose protects yeast cells from stress.

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).
Linking metabolic network features to phenotypes using sparse group lasso.

PubMed

Samal, Satya Swarup; Radulescu, Ovidiu; Weber, Andreas; Fröhlich, Holger

2017-11-01

Integration of metabolic networks with '-omics' data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features. We apply our method to metabolic networks from KEGG database and study the association of network features to prostate cancer (where the outcome is tumor and normal, respectively) as well as glioblastoma multiforme (where the outcome is survival time). In addition, simulations show the superior performance of our method compared to global test, which is an existing self-contained gene set analysis method. R code (compatible with version 3.2.5) is available from http://www.abi.bit.uni-bonn.de/index.php?id=17. samal@combine.rwth-aachen.de or frohlich@bit.uni-bonn.de. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Comprehensive analysis of a Metabolic Model for lipid production in Rhodosporidium toruloides.

PubMed

Castañeda, María Teresita; Nuñez, Sebastián; Garelli, Fabricio; Voget, Claudio; Battista, Hernán De

2018-05-19

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with Flux Balance Analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism. Copyright © 2018 Elsevier B.V. All rights reserved.
A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

PubMed Central

Smallbone, Kieran; Messiha, Hanan L.; Carroll, Kathleen M.; Winder, Catherine L.; Malys, Naglis; Dunn, Warwick B.; Murabito, Ettore; Swainston, Neil; Dada, Joseph O.; Khan, Farid; Pir, Pınar; Simeonidis, Evangelos; Spasić, Irena; Wishart, Jill; Weichart, Dieter; Hayes, Neil W.; Jameson, Daniel; Broomhead, David S.; Oliver, Stephen G.; Gaskell, Simon J.; McCarthy, John E.G.; Paton, Norman W.; Westerhoff, Hans V.; Kell, Douglas B.; Mendes, Pedro

2013-01-01

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. PMID:23831062
Selective inhibition of yeast regulons by daunorubicin: A transcriptome-wide analysis

PubMed Central

Rojas, Marta; Casado, Marta; Portugal, José; Piña, Benjamin

2008-01-01

Background The antitumor drug daunorubicin exerts some of its cytotoxic effects by binding to DNA and inhibiting the transcription of different genes. We analysed this effect in vivo at the transcriptome level using the budding yeast Saccharomyces cerevisiae as a model and sublethal (IC40) concentrations of the drug to minimise general toxic effects. Results Daunorubicin affected a minor proportion (14%) of the yeast transcriptome, increasing the expression of 195 genes and reducing expression of 280 genes. Daunorubicin down-regulated genes included essentially all genes involved in the glycolytic pathway, the tricarboxylic acid cycle and alcohol metabolism, whereas transcription of ribosomal protein genes was not affected or even slightly increased. This pattern is consistent with a specific inhibition of glucose usage in treated cells, with only minor effects on proliferation or other basic cell functions. Analysis of promoters of down-regulated genes showed that they belong to a limited number of transcriptional regulatory units (regulons). Consistently, data mining showed that daunorubicin-induced changes in expression patterns were similar to those observed in yeast strains deleted for some transcription factors functionally related to the glycolysis and/or the cAMP regulatory pathway, which appeared to be particularly sensitive to daunorubicin. Conclusion The effects of daunorubicin treatment on the yeast transcriptome are consistent with a model in which this drug impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks. This proposed mechanism might have broad therapeutic implications against cancer cells growing under hypoxic conditions. PMID:18667070
Lager Yeast Comes of Age

PubMed Central

2014-01-01

Alcoholic fermentations have accompanied human civilizations throughout our history. Lager yeasts have a several-century-long tradition of providing fresh beer with clean taste. The yeast strains used for lager beer fermentation have long been recognized as hybrids between two Saccharomyces species. We summarize the initial findings on this hybrid nature, the genomics/transcriptomics of lager yeasts, and established targets of strain improvements. Next-generation sequencing has provided fast access to yeast genomes. Its use in population genomics has uncovered many more hybridization events within Saccharomyces species, so that lager yeast hybrids are no longer the exception from the rule. These findings have led us to propose network evolution within Saccharomyces species. This “web of life” recognizes the ability of closely related species to exchange DNA and thus drain from a combined gene pool rather than be limited to a gene pool restricted by speciation. Within the domesticated lager yeasts, two groups, the Saaz and Frohberg groups, can be distinguished based on fermentation characteristics. Recent evidence suggests that these groups share an evolutionary history. We thus propose to refer to the Saaz group as Saccharomyces carlsbergensis and to the Frohberg group as Saccharomyces pastorianus based on their distinct genomes. New insight into the hybrid nature of lager yeast will provide novel directions for future strain improvement. PMID:25084862
Yeast as a model for Ras signalling.

PubMed

Tisi, Renata; Belotti, Fiorella; Martegani, Enzo

2014-01-01

For centuries yeast species have been popular hosts for classical biotechnology processes, such as baking, brewing, and wine making, and more recently for recombinant proteins production, thanks to the advantages of unicellular organisms (i.e., ease of genetic manipulation and rapid growth) together with the ability to perform eukaryotic posttranslational modifications. Moreover, yeast cells have been used for few decades as a tool for identifying the genes and pathways involved in basic cellular processes such as the cell cycle, aging, and stress response. In the budding yeast S. cerevisiae the Ras/cAMP/PKA pathway is directly involved in the regulation of metabolism, cell growth, stress resistance, and proliferation in response to the availability of nutrients and in the adaptation to glucose, controlling cytosolic cAMP levels and consequently the cAMP-dependent protein kinase (PKA) activity. Moreover, Ras signalling has been identified in several pathogenic yeasts as a key controller for virulence, due to its involvement in yeast morphogenesis. Nowadays, yeasts are still useful for Ras-like proteins investigation, both as model organisms and as a test tube to study variants of heterologous Ras-like proteins.
Flor Yeast: New Perspectives Beyond Wine Aging

PubMed Central

Legras, Jean-Luc; Moreno-Garcia, Jaime; Zara, Severino; Zara, Giacomo; Garcia-Martinez, Teresa; Mauricio, Juan C.; Mannazzu, Ilaria; Coi, Anna L.; Bou Zeidan, Marc; Dequin, Sylvie; Moreno, Juan; Budroni, Marilena

2016-01-01

The most important dogma in white-wine production is the preservation of the wine aroma and the limitation of the oxidative action of oxygen. In contrast, the aging of Sherry and Sherry-like wines is an aerobic process that depends on the oxidative activity of flor strains of Saccharomyces cerevisiae. Under depletion of nitrogen and fermentable carbon sources, these yeast produce aggregates of floating cells and form an air–liquid biofilm on the wine surface, which is also known as velum or flor. This behavior is due to genetic and metabolic peculiarities that differentiate flor yeast from other wine yeast. This review will focus first on the most updated data obtained through the analysis of flor yeast with -omic tools. Comparative genomics, proteomics, and metabolomics of flor and wine yeast strains are shedding new light on several features of these special yeast, and in particular, they have revealed the extent of proteome remodeling imposed by the biofilm life-style. Finally, new insights in terms of promotion and inhibition of biofilm formation through small molecules, amino acids, and di/tri-peptides, and novel possibilities for the exploitation of biofilm immobilization within a fungal hyphae framework, will be discussed. PMID:27148192
Dissecting metabolic behavior of lipid over-producing strain of Mucor circinelloides through genome-scale metabolic network and multi-level data integration.

PubMed

Vongsangnak, Wanwipa; Kingkaw, Amornthep; Yang, Junhuan; Song, Yuanda; Laoteng, Kobkul

2018-09-05

Lipid accumulation is an important cellular process of oleaginous microorganisms. To dissect metabolic behavior of oleaginous Zygomycetes, the lipid over-producing strain, Mucor circinelloides WJ11, was subjected for omics-scale analysis. The genome annotation was improved and used for construction of genome-scale metabolic network of WJ11 strain. Then, the quality of the metabolic network was enhanced by incorporating gene and protein expression data. In addition to the known oleaginous genes, our results showed a number of newly identified unique genes of WJ11 strain, which involved in central carbon metabolism, lipid, amino acid and nitrogen metabolisms. The systematic compilations indicated the additional metabolic routes with the involvement in supplying precursors (acetyl-CoA, NADPH and fatty acyl substrate) for fatty acid and lipid biosynthesis. Interestingly, amino acid metabolism played a substantial role in responsive mechanism of the fungal cells to nutrient imbalance circumstance through lipogenesis as the finding of reporter metabolites (l-methionine, l-glutamate, l-aspartate, l-asparagine and l-glutamine) at lipid-accumulating stage. The cooperative function of certain lipid-degrading enzymes at the particular growth stage was elucidated by integrating the metabolic networks with gene expression data. The unique feature of carotenoid biosynthetic route in WJ11 strain was also identified by protein domain analysis. Taken together, there were cross-functional metabolisms in regulating lipid biosynthesis and retaining high level of cellular lipids in the representative of lipid over-producing strains. Copyright © 2018 Elsevier B.V. All rights reserved.
Non-conventional Yeast Species for Lowering Ethanol Content of Wines

PubMed Central

Ciani, Maurizio; Morales, Pilar; Comitini, Francesca; Tronchoni, Jordi; Canonico, Laura; Curiel, José A.; Oro, Lucia; Rodrigues, Alda J.; Gonzalez, Ramon

2016-01-01

Rising sugar content in grape must, and the concomitant increase in alcohol levels in wine, are some of the main challenges affecting the winemaking industry nowadays. Among the several alternative solutions currently under study, the use of non-conventional yeasts during fermentation holds good promise for contributing to relieve this problem. Non-Saccharomyces wine yeast species comprise a high number or species, so encompassing a wider physiological diversity than Saccharomyces cerevisiae. Indeed, the current oenological interest of these microorganisms was initially triggered by their potential positive contribution to the sensorial complexity of quality wines, through the production of aroma and other sensory-active compounds. This diversity also involves ethanol yield on sugar, one of the most invariant metabolic traits of S. cerevisiae. This review gathers recent research on non-Saccharomyces yeasts, aiming to produce wines with lower alcohol content than those from pure Saccharomyces starters. Critical aspects discussed include the selection of suitable yeast strains (considering there is a noticeable intra-species diversity for ethanol yield, as shown for other fermentation traits), identification of key environmental parameters influencing ethanol yields (including the use of controlled oxygenation conditions), and managing mixed fermentations, by either the sequential or simultaneous inoculation of S. cerevisiae and non-Saccharomyces starter cultures. The feasibility, at the industrial level, of using non-Saccharomyces yeasts for reducing alcohol levels in wine will require an improved understanding of the metabolism of these alternative yeast species, as well as of the interactions between different yeast starters during the fermentation of grape must. PMID:27199967
Abnormal metabolic brain networks in Parkinson's disease from blackboard to bedside.

PubMed

Tang, Chris C; Eidelberg, David

2010-01-01

Metabolic imaging in the rest state has provided valuable information concerning the abnormalities of regional brain function that underlie idiopathic Parkinson's disease (PD). Moreover, network modeling procedures, such as spatial covariance analysis, have further allowed for the quantification of these changes at the systems level. In recent years, we have utilized this strategy to identify and validate three discrete metabolic networks in PD associated with the motor and cognitive manifestations of the disease. In this chapter, we will review and compare the specific functional topographies underlying parkinsonian akinesia/rigidity, tremor, and cognitive disturbance. While network activity progressed over time, the rate of change for each pattern was distinctive and paralleled the development of the corresponding clinical symptoms in early-stage patients. This approach is already showing great promise in identifying individuals with prodromal manifestations of PD and in assessing the rate of progression before clinical onset. Network modulation was found to correlate with the clinical effects of dopaminergic treatment and surgical interventions, such as subthalamic nucleus (STN) deep brain stimulation (DBS) and gene therapy. Abnormal metabolic networks have also been identified for atypical parkinsonian syndromes, such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Using multiple disease-related networks for PD, MSA, and PSP, we have developed a novel, fully automated algorithm for accurate classification at the single-patient level, even at early disease stages. Copyright © 2010 Elsevier B.V. All rights reserved.
Ask yeast how to burn your fats: lessons learned from the metabolic adaptation to salt stress.

PubMed

Pascual-Ahuir, Amparo; Manzanares-Estreder, Sara; Timón-Gómez, Alba; Proft, Markus

2018-02-01

Here, we review and update the recent advances in the metabolic control during the adaptive response of budding yeast to hyperosmotic and salt stress, which is one of the best understood signaling events at the molecular level. This environmental stress can be easily applied and hence has been exploited in the past to generate an impressively detailed and comprehensive model of cellular adaptation. It is clear now that this stress modulates a great number of different physiological functions of the cell, which altogether contribute to cellular survival and adaptation. Primary defense mechanisms are the massive induction of stress tolerance genes in the nucleus, the activation of cation transport at the plasma membrane, or the production and intracellular accumulation of osmolytes. At the same time and in a coordinated manner, the cell shuts down the expression of housekeeping genes, delays the progression of the cell cycle, inhibits genomic replication, and modulates translation efficiency to optimize the response and to avoid cellular damage. To this fascinating interplay of cellular functions directly regulated by the stress, we have to add yet another layer of control, which is physiologically relevant for stress tolerance. Salt stress induces an immediate metabolic readjustment, which includes the up-regulation of peroxisomal biomass and activity in a coordinated manner with the reinforcement of mitochondrial respiratory metabolism. Our recent findings are consistent with a model, where salt stress triggers a metabolic shift from fermentation to respiration fueled by the enhanced peroxisomal oxidation of fatty acids. We discuss here the regulatory details of this stress-induced metabolic shift and its possible roles in the context of the previously known adaptive functions.
Screening the yeast genome for energetic metabolism pathways involved in a phenotypic response to the anti-cancer agent 3-bromopyruvate.

PubMed

Lis, Paweł; Jurkiewicz, Paweł; Cal-Bąkowska, Magdalena; Ko, Young H; Pedersen, Peter L; Goffeau, Andre; Ułaszewski, Stanisław

2016-03-01

In this study the detailed characteristic of the anti-cancer agent 3-bromopyruvate (3-BP) activity in the yeast Saccharomyces cerevisiae model is described, with the emphasis on its influence on energetic metabolism of the cell. It shows that 3-BP toxicity in yeast is strain-dependent and influenced by the glucose-repression system. Its toxic effect is mainly due to the rapid depletion of intracellular ATP. Moreover, lack of the Whi2p phosphatase results in strongly increased sensitivity of yeast cells to 3-BP, possibly due to the non-functional system of mitophagy of damaged mitochondria through the Ras-cAMP-PKA pathway. Single deletions of genes encoding glycolytic enzymes, the TCA cycle enzymes and mitochondrial carriers result in multiple effects after 3-BP treatment. However, it can be concluded that activity of the pentose phosphate pathway is necessary to prevent the toxicity of 3-BP, probably due to the fact that large amounts of NADPH are produced by this pathway, ensuring the reducing force needed for glutathione reduction, crucial to cope with the oxidative stress. Moreover, single deletions of genes encoding the TCA cycle enzymes and mitochondrial carriers generally cause sensitivity to 3-BP, while totally inactive mitochondrial respiration in the rho0 mutant resulted in increased resistance to 3-BP.
Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".
Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets.

PubMed

Koch, Ina; Nöthen, Joachim; Schleiff, Enrico

2017-01-01

Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana . We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to
Identifying all moiety conservation laws in genome-scale metabolic networks.

PubMed

De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

2014-01-01

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.
MetaNET--a web-accessible interactive platform for biological metabolic network analysis.

PubMed

Narang, Pankaj; Khan, Shawez; Hemrom, Anmol Jaywant; Lynn, Andrew Michael

2014-01-01

Metabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level. While tools for the perturbation of metabolic networks are available, their applications are limited and restricted as they require varied dependencies and often a commercial platform for full functionality. We have developed MetaNET, an open source user-friendly platform-independent and web-accessible resource consisting of several pre-defined workflows for metabolic network analysis. MetaNET is a web-accessible platform that incorporates a range of functions which can be combined to produce different simulations related to metabolic networks. These include (i) optimization of an objective function for wild type strain, gene/catalyst/reaction knock-out/knock-down analysis using flux balance analysis. (ii) flux variability analysis (iii) chemical species participation (iv) cycles and extreme paths identification and (v) choke point reaction analysis to facilitate identification of potential drug targets. The platform is built using custom scripts along with the open-source Galaxy workflow and Systems Biology Research Tool as components. Pre-defined workflows are available for common processes, and an exhaustive list of over 50 functions are provided for user defined workflows. MetaNET, available at http://metanet.osdd.net , provides a user-friendly rich interface allowing the analysis of genome-scale metabolic networks under various genetic and environmental conditions. The framework permits the storage of previous results, the ability to repeat analysis and share results with other users over the internet as well as run different tools simultaneously using pre-defined workflows, and user-created custom workflows.
Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.

PubMed

Wang, B H; Lim, J W; Lim, J S

2016-08-30

Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.
Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803

PubMed Central

Knoop, Henning; Gründel, Marianne; Zilliges, Yvonne; Lehmann, Robert; Hoffmann, Sabrina; Lockau, Wolfgang; Steuer, Ralf

2013-01-01

Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism. PMID:23843751
Diet shifts provoke complex and variable changes in the metabolic networks of the ruminal microbiome.

PubMed

Wolff, Sara M; Ellison, Melinda J; Hao, Yue; Cockrum, Rebecca R; Austin, Kathy J; Baraboo, Michael; Burch, Katherine; Lee, Hyuk Jin; Maurer, Taylor; Patil, Rocky; Ravelo, Andrea; Taxis, Tasia M; Truong, Huan; Lamberson, William R; Cammack, Kristi M; Conant, Gavin C

2017-06-08

Grazing mammals rely on their ruminal microbial symbionts to convert plant structural biomass into metabolites they can assimilate. To explore how this complex metabolic system adapts to the host animal's diet, we inferred a microbiome-level metabolic network from shotgun metagenomic data. Using comparative genomics, we then linked this microbial network to that of the host animal using a set of interface metabolites likely to be transferred to the host. When the host sheep were fed a grain-based diet, the induced microbial metabolic network showed several critical differences from those seen on the evolved forage-based diet. Grain-based (e.g., concentrate) diets tend to be dominated by a smaller set of reactions that employ metabolites that are nearer in network space to the host's metabolism. In addition, these reactions are more central in the network and employ substrates with shorter carbon backbones. Despite this apparent lower complexity, the concentrate-associated metabolic networks are actually more dissimilar from each other than are those of forage-fed animals. Because both groups of animals were initially fed on a forage diet, we propose that the diet switch drove the appearance of a number of different microbial networks, including a degenerate network characterized by an inefficient use of dietary nutrients. We used network simulations to show that such disparate networks are not an unexpected result of a diet shift. We argue that network approaches, particularly those that link the microbial network with that of the host, illuminate aspects of the structure of the microbiome not seen from a strictly taxonomic perspective. In particular, different diets induce predictable and significant differences in the enzymes used by the microbiome. Nonetheless, there are clearly a number of microbiomes of differing structure that show similar functional properties. Changes such as a diet shift uncover more of this type of diversity.

The uptake of different iron salts by the yeast Saccharomyces cerevisiae

PubMed Central

Gaensly, Fernanda; Picheth, Geraldo; Brand, Debora; Bonfim, Tania M.B.

2014-01-01

Yeasts can be enriched with microelements, including iron; however, special physicochemical conditions are required to formulate a culture media that promotes both yeast growth and iron uptake. Different iron sources do not affect biomass formation; however, considering efficacy, cost, stability, and compatibility with Saccharomyces cerevisiae metabolism, ferrous sulphate is recommended. PMID:25242932
Revealing oxidative damage to enzymes of carbohydrate metabolism in yeast: An integration of 2D DIGE, quantitative proteomics, and bioinformatics.

PubMed

Boone, Cory H T; Grove, Ryan A; Adamcova, Dana; Braga, Camila P; Adamec, Jiri

2016-07-01

Clinical usage of lidocaine, a pro-oxidant has been linked with severe, mostly neurological complications. The mechanism(s) causing these complications is independent of the blockade of voltage-gated sodium channels. The budding yeast Saccharomyces cerevisiae lacks voltage-gated sodium channels, thus provides an ideal system to investigate lidocaine-induced protein and pathway alterations. Whole-proteome alterations leading to these complications have not been identified. To address this, S. cerevisiae was grown to stationary phase and exposed to an LC50 dose of lidocaine. The differential proteomes of lidocaine treatment and control were resolved 6 h post exposure using 2D DIGE. Amine reactive dyes and carbonyl reactive dyes were used to assess protein abundance and protein oxidation, respectively. Quantitative analysis of these dyes (⩾ 1.5-fold alteration, p ⩽ 0.05) revealed a total of 33 proteoforms identified by MS differing in abundance and/or oxidation upon lidocaine exposure. Network analysis showed enrichment of apoptotic proteins and cell wall maintenance proteins, while the abundance of proteins central to carbohydrate metabolism, such as triosephosphate isomerase and glyceraldehyde-3-phosphate dehydrogenase, and redox proteins superoxide dismutase and peroxiredoxin were significantly decreased. Enzymes of carbohydrate metabolism, such as phosphoglycerate kinase and enolase, the TCA cycle enzyme aconitase, and multiple ATP synthase subunits were found to be oxidatively modified. Also, the activity of aconitase was found to be decreased. Overall, these data suggest that toxic doses of lidocaine induce significant disruption of glycolytic pathways, energy production, and redox balance, potentially leading to cell malfunction and death. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Metabolic PathFinding: inferring relevant pathways in biochemical networks.

PubMed

Croes, Didier; Couche, Fabian; Wodak, Shoshana J; van Helden, Jacques

2005-07-01

Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).
Anaerobic digestion of food waste using yeast.

PubMed

Suwannarat, Jutarat; Ritchie, Raymond J

2015-08-01

Fermentative breakdown of food waste seems a plausible alternative to feeding food waste to pigs, incineration or garbage disposal in tourist areas. We determined the optimal conditions for the fermentative breakdown of food waste using yeast (Saccharomyces cerevisiae) in incubations up to 30days. Yeast efficiently broke down food waste with food waste loadings as high as 700g FW/l. The optimum inoculation was ≈46×10(6)cells/l of culture with a 40°C optimum (25-40°C). COD and BOD were reduced by ≈30-50%. Yeast used practically all the available sugars and reduced proteins and lipids by ≈50%. Yeast was able to metabolize lipids much better than expected. Starch was mobilized after very long term incubations (>20days). Yeast was effective in breaking down the organic components of food waste but CO2 gas and ethanol production (≈1.5%) were only significant during the first 7days of incubations. Copyright © 2015 Elsevier Ltd. All rights reserved.
The Antarctic yeast Candida sake: Understanding cold metabolism impact on wine.

PubMed

Ballester-Tomás, Lidia; Prieto, Jose A; Gil, Jose V; Baeza, Marcelo; Randez-Gil, Francisca

2017-03-20

Current winemaking trends include low-temperature fermentations and using non-Saccharomyces yeasts as the most promising tools to produce lower alcohol and increased aromatic complexity wines. Here we explored the oenological attributes of a C. sake strain, H14Cs, isolated in the sub-Antarctic region. As expected, the cold sea water yeast strain showed greater cold growth, Na + -toxicity resistance and freeze tolerance than the S. cerevisiae QA23 strain, which we used as a commercial wine yeast control. C. sake H14Cs was found to be more sensitive to ethanol. The fermentation trials of low-sugar content must demonstrated that C. sake H14Cs allowed the cold-induced lag phase of growth to be eliminated and also notably reduced the ethanol (-30%) and glycerol (-50%) content in wine. Instead C. sake produced sorbitol as a compatible osmolyte. Finally, the inspection of the main wine volatile compounds revealed that C. sake produced more higher alcohols than S. cerevisiae. In conclusion, our work evidences that using the Antarctic C. sake H14Cs yeast improves low-temperature must fermentations and has the potential to provide a wine with less ethanol and also particular attributes. Copyright © 2017 Elsevier B.V. All rights reserved.
The evolution of metabolic networks of E. coli

PubMed Central

2011-01-01

Background Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts. Results We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome
A potential mechanism of energy-metabolism oscillation in an aerobic chemostat culture of the yeast Saccharomyces cerevisiae.

PubMed

Xu, Zhaojun; Tsurugi, Kunio

2006-04-01

The energy-metabolism oscillation in aerobic chemostat cultures of yeast is a periodic change of the respiro-fermentative and respiratory phase. In the respiro-fermentative phase, the NADH level was kept high and respiration was suppressed, and glucose was anabolized into trehalose and glycogen at a rate comparable to that of catabolism. On the transition to the respiratory phase, cAMP levels increased triggering the breakdown of storage carbohydrates and the increased influx of glucose into the glycolytic pathway activated production of glycerol and ethanol consuming NADH. The resulting increase in the NAD(+)/NADH ratio stimulated respiration in combination with a decrease in the level of ATP, which was consumed mainly in the formation of biomass accompanying budding, and the accumulated ethanol and glycerol were gradually degraded by respiration via NAD(+)-dependent oxidation to acetate and the respiratory phase ceased after the recovery of NADH and ATP levels. However, the mRNA levels of both synthetic and degradative enzymes of storage carbohydrates were increased around the early respiro-fermentative phase, when storage carbohydrates are being synthesized, suggesting that the synthetic enzymes were expressed directly as active forms while the degradative enzymes were activated late by cAMP. In summary, the energy-metabolism oscillation is basically regulated by a feedback loop of oxido-reductive reactions of energy metabolism mediated by metabolites like NADH and ATP, and is modulated by metabolism of storage carbohydrates in combination of post-translational and transcriptional regulation of the related enzymes. A potential mechanism of energy-metabolism oscillation is proposed.
(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

PubMed

He, Fei; Fromion, Vincent; Westerhoff, Hans V

2013-11-21

Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems
(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

PubMed Central

2013-01-01

Background Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. Results This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. Conclusions A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and
Effect of resistant and digestible rice starches on human cytokine and lactate metabolic networks in serum.

PubMed

Wang, Huisong; Pang, Guangchang

2017-05-01

Resistant starch generated after treating ordinary starch is of great significance to human health in the countries with overnutrition. However, its functional evaluation in the human body has been rarely reported. By determining the lactate metabolic flux, 12 serum enzymes expression level and 38 serum cytokines in healthy volunteers, the variation in cytokine network and lactate metabolic network in serum were investigated to compare the mechanism of the physiological effects between the two starches. The results indicated that compared with digestible starch, resistant starch had anti-inflammatory effects, increased anabolism, and decreased catabolism. Further, the intercellular communication networks including cytokine and lactate metabolic networks were mapped out. The relationship suggested that resistant starch might affect and control the secretion of cytokines to regulate lactate metabolic network in the body, promoting the development of immunometabolism. Copyright © 2017 Elsevier Ltd. All rights reserved.
Engineering tolerance to industrially relevant stress factors in yeast cell factories.

PubMed

Deparis, Quinten; Claes, Arne; Foulquié-Moreno, Maria R; Thevelein, Johan M

2017-06-01

The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way. © FEMS 2017.
Engineering tolerance to industrially relevant stress factors in yeast cell factories

PubMed Central

Deparis, Quinten; Claes, Arne; Foulquié-Moreno, Maria R.

2017-01-01

Abstract The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way. PMID:28586408
Field Assessment of Yeast- and Oxalic Acid-generated Carbon Dioxide for Mosquito Surveillance

DTIC Science & Technology

2014-12-01

SentinelTM, Centers for Disease Control and Prevention light trap, sugar- fermenting yeast, electrolyzed oxalic acid INTRODUCTION Successful vector-borne...generated by a fermentation chamber, in which yeast metabolized sucrose. This source had been shown to attract various mosquito species in field and...surveillance periods. The 2 novel CO2 sources evaluated were yeast- fermenting sugar and electro-stripping a carboxylated organic compound (oxalic acid
Optimality principles in the regulation of metabolic networks.

PubMed

Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas

2012-08-29

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Study on Incompatibility of Traditional Chinese Medicine: Evidence from Formula Network, Chemical Space, and Metabolism Room

PubMed Central

Zhang, Xiao-Dong; Wu, Hong-Ying; Jin, Jin; Yu, Guang-Yun; He, Xin; Wang, Hao; Shen, Xiu; Zhou, Ze-Wei; Liu, Pei-Xun; Fan, Sai-Jun

2013-01-01

A traditional Chinese medicine (TCM) formula network including 362 TCM formulas was built by using complex network methodologies. The properties of this network were analyzed including network diameter, average distance, clustering coefficient, and average degree. Meanwhile, we built a TCM chemical space and a TCM metabolism room under the theory of chemical space. The properties of chemical space and metabolism room were calculated and analyzed. The properties of the medicine pairs in “eighteen antagonisms and nineteen mutual inhibitors,” an ancient rule for TCM incompatibility, were studied based on the TCM formula network, chemical space, and metabolism room. The results showed that the properties of these incompatible medicine pairs are different from those of the other TCM based on the analysis of the TCM formula network, chemical space, and metabolism room. The lines of evidence derived from our work demonstrated that the ancient rule of TCM incompatibility, “eighteen antagonisms and nineteen mutual inhibitors,” is probably scientifically based. PMID:24369478
Bacterial Unculturability and the Formation of Intercellular Metabolic Networks.

PubMed

Pande, Samay; Kost, Christian

2017-05-01

The majority of known bacterial species cannot be cultivated under laboratory conditions. Here we argue that the adaptive emergence of obligate metabolic interactions in natural bacterial communities can explain this pattern. Bacteria commonly release metabolites into the external environment. Accumulating pools of extracellular metabolites create an ecological niche that benefits auxotrophic mutants, which have lost the ability to autonomously produce the corresponding metabolites. In addition to a diffusion-based metabolite transfer, auxotrophic cells can use contact-dependent means to obtain nutrients from other co-occurring cells. Spatial colocalisation and a continuous coevolution further increase the nutritional dependency and optimise fluxes through combined metabolic networks. Thus, bacteria likely function as networks of interacting cells that reciprocally exchange nutrients and biochemical functions rather than as physiologically autonomous units. Copyright © 2017 Elsevier Ltd. All rights reserved.
Progress in Metabolic Engineering of Saccharomyces cerevisiae

PubMed Central

Nevoigt, Elke

2008-01-01

Summary: The traditional use of the yeast Saccharomyces cerevisiae in alcoholic fermentation has, over time, resulted in substantial accumulated knowledge concerning genetics, physiology, and biochemistry as well as genetic engineering and fermentation technologies. S. cerevisiae has become a platform organism for developing metabolic engineering strategies, methods, and tools. The current review discusses the relevance of several engineering strategies, such as rational and inverse metabolic engineering, evolutionary engineering, and global transcription machinery engineering, in yeast strain improvement. It also summarizes existing tools for fine-tuning and regulating enzyme activities and thus metabolic pathways. Recent examples of yeast metabolic engineering for food, beverage, and industrial biotechnology (bioethanol and bulk and fine chemicals) follow. S. cerevisiae currently enjoys increasing popularity as a production organism in industrial (“white”) biotechnology due to its inherent tolerance of low pH values and high ethanol and inhibitor concentrations and its ability to grow anaerobically. Attention is paid to utilizing lignocellulosic biomass as a potential substrate. PMID:18772282
Dekkera bruxellensis--spoilage yeast with biotechnological potential, and a model for yeast evolution, physiology and competitiveness.

PubMed

Blomqvist, Johanna; Passoth, Volkmar

2015-06-01

Dekkera bruxellensis is a non-conventional yeast normally considered a spoilage organism in wine (off-flavours) and in the bioethanol industry. But it also has potential as production yeast. The species diverged from Saccharomyces cerevisiae 200 mya, before the whole genome duplication. However, it displays similar characteristics such as being Crabtree- and petite positive, and the ability to grow anaerobically. Partial increases in ploidy and promoter rewiring may have enabled evolution of the fermentative lifestyle in D. bruxellensis. On the other hand, it has genes typical for respiratory yeasts, such as for complex I or the alternative oxidase AOX1. Dekkera bruxellensis grows more slowly than S. cerevisiae, but produces similar or greater amounts of ethanol, and very low amounts of glycerol. Glycerol production represents a loss of energy but also functions as a redox sink for NADH formed during synthesis of amino acids and other compounds. Accordingly, anaerobic growth required addition of certain amino acids. In spite of its slow growth, D. bruxellensis outcompeted S. cerevisiae in glucose-limited cultures, indicating a more efficient energy metabolism and/or higher affinity for glucose. This review tries to summarize the latest discoveries about evolution, physiology and metabolism, and biotechnological potential of D. bruxellensis. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Nicotinamide supplementation phenocopies SIR2 inactivation by modulating carbon metabolism and respiration during yeast chronological aging.

PubMed

Orlandi, Ivan; Pellegrino Coppola, Damiano; Strippoli, Maurizio; Ronzulli, Rossella; Vai, Marina

2017-01-01

Nicotinamide (NAM), a form of vitamin B 3 , is a byproduct and noncompetitive inhibitor of the deacetylation reaction catalyzed by Sirtuins. These represent a family of evolutionarily conserved NAD + -dependent deacetylases that are well-known critical regulators of metabolism and aging and whose founding member is Sir2 of Saccharomyces cerevisiae. Here, we investigated the effects of NAM supplementation in the context of yeast chronological aging, the established model for studying aging of postmitotic quiescent mammalian cells. Our data show that NAM supplementation at the diauxic shift results in a phenocopy of chronologically aging sir2Δ cells. In fact, NAM-supplemented cells display the same chronological lifespan extension both in expired medium and extreme Calorie Restriction. Furthermore, NAM allows the cells to push their metabolism toward the same outcomes of sir2Δ cells by elevating the level of the acetylated Pck1. Both these cells have the same metabolic changes that concern not only anabolic pathways such as an increased gluconeogenesis but also respiratory activity in terms both of respiratory rate and state of respiration. In particular, they have a higher respiratory reserve capacity and a lower non-phosphorylating respiration that in concert with a low burden of superoxide anions can affect positively chronological aging. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

PubMed Central

Zhang, Ai-Di; Dai, Shao-Xing; Huang, Jing-Fei

2013-01-01

With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases. PMID:24222897
Comparison of different estimation techniques for biomass concentration in large scale yeast fermentation.

PubMed

Hocalar, A; Türker, M; Karakuzu, C; Yüzgeç, U

2011-04-01

In this study, previously developed five different state estimation methods are examined and compared for estimation of biomass concentrations at a production scale fed-batch bioprocess. These methods are i. estimation based on kinetic model of overflow metabolism; ii. estimation based on metabolic black-box model; iii. estimation based on observer; iv. estimation based on artificial neural network; v. estimation based on differential evaluation. Biomass concentrations are estimated from available measurements and compared with experimental data obtained from large scale fermentations. The advantages and disadvantages of the presented techniques are discussed with regard to accuracy, reproducibility, number of primary measurements required and adaptation to different working conditions. Among the various techniques, the metabolic black-box method seems to have advantages although the number of measurements required is more than that for the other methods. However, the required extra measurements are based on commonly employed instruments in an industrial environment. This method is used for developing a model based control of fed-batch yeast fermentations. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Altered Fermentation Performances, Growth, and Metabolic Footprints Reveal Competition for Nutrients between Yeast Species Inoculated in Synthetic Grape Juice-Like Medium

PubMed Central

Rollero, Stephanie; Bloem, Audrey; Ortiz-Julien, Anne; Camarasa, Carole; Divol, Benoit

2018-01-01

The sequential inoculation of non-Saccharomyces yeasts and Saccharomyces cerevisiae in grape juice is becoming an increasingly popular practice to diversify wine styles and/or to obtain more complex wines with a peculiar microbial footprint. One of the main interactions is competition for nutrients, especially nitrogen sources, that directly impacts not only fermentation performance but also the production of aroma compounds. In order to better understand the interactions taking place between non-Saccharomyces yeasts and S. cerevisiae during alcoholic fermentation, sequential inoculations of three yeast species (Pichia burtonii, Kluyveromyces marxianus, Zygoascus meyerae) with S. cerevisiae were performed individually in a synthetic medium. Different species-dependent interactions were evidenced. Indeed, the three sequential inoculations resulted in three different behaviors in terms of growth. P. burtonii and Z. meyerae declined after the inoculation of S. cerevisiae which promptly outcompeted the other two species. However, while the presence of P. burtonii did not impact the fermentation kinetics of S. cerevisiae, that of Z. meyerae rendered the overall kinetics very slow and with no clear exponential phase. K. marxianus and S. cerevisiae both declined and became undetectable before fermentation completion. The results also demonstrated that yeasts differed in their preference for nitrogen sources. Unlike Z. meyerae and P. burtonii, K. marxianus appeared to be a competitor for S. cerevisiae (as evidenced by the uptake of ammonium and amino acids), thereby explaining the resulting stuck fermentation. Nevertheless, the results suggested that competition for other nutrients (probably vitamins) occurred during the sequential inoculation of Z. meyerae with S. cerevisiae. The metabolic footprint of the non-Saccharomyces yeasts determined after 48 h of fermentation remained until the end of fermentation and combined with that of S. cerevisiae. For instance
Altered Fermentation Performances, Growth, and Metabolic Footprints Reveal Competition for Nutrients between Yeast Species Inoculated in Synthetic Grape Juice-Like Medium.

PubMed

Rollero, Stephanie; Bloem, Audrey; Ortiz-Julien, Anne; Camarasa, Carole; Divol, Benoit

2018-01-01

The sequential inoculation of non- Saccharomyces yeasts and Saccharomyces cerevisiae in grape juice is becoming an increasingly popular practice to diversify wine styles and/or to obtain more complex wines with a peculiar microbial footprint. One of the main interactions is competition for nutrients, especially nitrogen sources, that directly impacts not only fermentation performance but also the production of aroma compounds. In order to better understand the interactions taking place between non-Saccharomyces yeasts and S. cerevisiae during alcoholic fermentation, sequential inoculations of three yeast species ( Pichia burtonii, Kluyveromyces marxianus, Zygoascus meyerae ) with S. cerevisiae were performed individually in a synthetic medium. Different species-dependent interactions were evidenced. Indeed, the three sequential inoculations resulted in three different behaviors in terms of growth. P. burtonii and Z. meyerae declined after the inoculation of S. cerevisiae which promptly outcompeted the other two species. However, while the presence of P. burtonii did not impact the fermentation kinetics of S. cerevisiae , that of Z. meyerae rendered the overall kinetics very slow and with no clear exponential phase. K. marxianus and S. cerevisiae both declined and became undetectable before fermentation completion. The results also demonstrated that yeasts differed in their preference for nitrogen sources. Unlike Z. meyerae and P. burtonii, K. marxianus appeared to be a competitor for S. cerevisiae (as evidenced by the uptake of ammonium and amino acids), thereby explaining the resulting stuck fermentation. Nevertheless, the results suggested that competition for other nutrients (probably vitamins) occurred during the sequential inoculation of Z. meyerae with S. cerevisiae . The metabolic footprint of the non- Saccharomyces yeasts determined after 48 h of fermentation remained until the end of fermentation and combined with that of S. cerevisiae . For instance
Optimality Principles in the Regulation of Metabolic Networks

PubMed Central

Berkhout, Jan; Bruggeman, Frank J.; Teusink, Bas

2012-01-01

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide. PMID:24957646
Impact of zinc supplementation on the improvement of ethanol tolerance and yield of self-flocculating yeast in continuous ethanol fermentation.

PubMed

Zhao, X Q; Xue, C; Ge, X M; Yuan, W J; Wang, J Y; Bai, F W

2009-01-01

The effects of zinc supplementation were investigated in the continuous ethanol fermentation using self-flocculating yeast. Zinc sulfate was added at the concentrations of 0.01, 0.05 and 0.1 g l(-1), respectively. Reduced average floc sizes were observed in all the zinc-supplemented cultures. Both the ethanol tolerance and thermal tolerance were significantly improved by zinc supplements, which correlated well with the increased ergosterol and trehalose contents in the yeast flocs. The highest ethanol concentration by 0.05 g l(-1) zinc sulfate supplementation attained 114.5 g l(-1), in contrast to 104.1 g l(-1) in the control culture. Glycerol production was decreased by zinc supplementations, with the lowest level 3.21 g l(-1), about 58% of the control. Zinc content in yeast cells was about 1.4 microMol g(-1) dry cell weight, about sixfold higher than that of control in all the zinc-supplemented cultures, and close correlation of zinc content in yeast cells with the cell viability against ethanol and heat shock treatment was observed. These studies suggest that exogenous zinc addition led to a reprogramming of cellular metabolic network, resulting in enhanced ethanol tolerance and ethanol production.
Cold adaptation shapes the robustness of metabolic networks in Drosophila melanogaster

PubMed Central

Williams, CM; Watanabe, M; Guarracino, MR; Ferraro, MB; Edison, AS; Morgan, TJ; Boroujerdi, AFB; Hahn, DA

2015-01-01

When ectotherms are exposed to low temperatures, they enter a cold-induced coma (chill coma) that prevents resource acquisition, mating, oviposition, and escape from predation. There is substantial variation in time taken to recover from chill coma both within and among species, and this variation is correlated with habitat temperatures such that insects from cold environments recover more quickly. This suggests an adaptive response, but the mechanisms underlying variation in recovery times are unknown, making it difficult to decisively test adaptive hypotheses. We use replicated lines of Drosophila melanogaster selected in the laboratory for fast (hardy) or slow (susceptible) chill-coma recovery times to investigate modifications to metabolic profiles associated with cold adaptation. We measured metabolite concentrations of flies before, during, and after cold exposure using NMR spectroscopy to test the hypotheses that hardy flies maintain metabolic homeostasis better during cold exposure and recovery, and that their metabolic networks are more robust to cold-induced perturbations. The metabolites of cold-hardy flies were less cold responsive and their metabolic networks during cold exposure were more robust, supporting our hypotheses. Metabolites involved in membrane lipid synthesis, tryptophan metabolism, oxidative stress, energy balance, and proline metabolism were altered by selection on cold tolerance. We discuss the potential significance of these alterations. PMID:25308124
Observability of Plant Metabolic Networks Is Reflected in the Correlation of Metabolic Profiles.

PubMed

Schwahn, Kevin; Küken, Anika; Kliebenstein, Daniel J; Fernie, Alisdair R; Nikoloski, Zoran

2016-10-01

Understanding whether the functionality of a biological system can be characterized by measuring few selected components is key to targeted phenotyping techniques in systems biology. Methods from observability theory have proven useful in identifying sensor components that have to be measured to obtain information about the entire system. Yet, the extent to which the data profiles reflect the role of components in the observability of the system remains unexplored. Here we first identify the sensor metabolites in the model plant Arabidopsis (Arabidopsis thaliana) by employing state-of-the-art genome-scale metabolic networks. By using metabolic data profiles from a set of seven environmental perturbations as well as from natural variability, we demonstrate that the data profiles of sensor metabolites are more correlated than those of nonsensor metabolites. This pattern was confirmed with in silico generated metabolic profiles from a medium-size kinetic model of plant central carbon metabolism. Altogether, due to the small number of identified sensors, our study implies that targeted metabolite analyses may provide the vast majority of relevant information about plant metabolic systems. © 2016 American Society of Plant Biologists. All Rights Reserved.
Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

DOE PAGES

Granger, Brian R.; Chang, Yi -Chien; Wang, Yan; ...

2016-04-15

Here, the complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique meta-graph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction networkmore » between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.« less
Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

PubMed

Takemoto, Kazuhiro; Aie, Kazuki

2017-05-25

Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.
Differential adaptation to multi-stressed conditions of wine fermentation revealed by variations in yeast regulatory networks

PubMed Central

2013-01-01

Background Variation of gene expression can lead to phenotypic variation and have therefore been assumed to contribute the diversity of wine yeast (Saccharomyces cerevisiae) properties. However, the molecular bases of this variation of gene expression are unknown. We addressed these questions by carrying out an integrated genetical-genomic study in fermentation conditions. We report here quantitative trait loci (QTL) mapping based on expression profiling in a segregating population generated by a cross between a derivative of the popular wine strain EC1118 and the laboratory strain S288c. Results Most of the fermentation traits studied appeared to be under multi-allelic control. We mapped five phenotypic QTLs and 1465 expression QTLs. Several expression QTLs overlapped in hotspots. Among the linkages unraveled here, several were associated with metabolic processes essential for wine fermentation such as glucose sensing or nitrogen and vitamin metabolism. Variations affecting the regulation of drug detoxification and export (TPO1, PDR12 or QDR2) were linked to variation in four genes encoding transcription factors (PDR8, WAR1, YRR1 and HAP1). We demonstrated that the allelic variation of WAR1 and TPO1 affected sorbic and octanoic acid resistance, respectively. Moreover, analysis of the transcription factors phylogeny suggests they evolved with a specific adaptation of the strains to wine fermentation conditions. Unexpectedly, we found that the variation of fermentation rates was associated with a partial disomy of chromosome 16. This disomy resulted from the well known 8–16 translocation. Conclusions This large data set made it possible to decipher the effects of genetic variation on gene expression during fermentation and certain wine fermentation properties. Our findings shed a new light on the adaptation mechanisms required by yeast to cope with the multiple stresses generated by wine fermentation. In this context, the detoxification and export systems appear
Making Sense of the Yeast Sphingolipid Pathway.

PubMed

Megyeri, Márton; Riezman, Howard; Schuldiner, Maya; Futerman, Anthony H

2016-12-04

Sphingolipids (SL) and their metabolites play key roles both as structural components of membranes and as signaling molecules. Many of the key enzymes and regulators of SL metabolism were discovered using the yeast Saccharomyces cerevisiae, and based on the high degree of conservation, a number of mammalian homologs were identified. Although yeast continues to be an important tool for SL research, the complexity of SL structure and nomenclature often hampers the ability of new researchers to grasp the subtleties of yeast SL biology and discover new modulators of this intricate pathway. Moreover, the emergence of lipidomics by mass spectrometry has enabled the rapid identification of SL species in yeast and rendered the analysis of SL composition under various physiological and pathophysiological conditions readily amenable. However, the complex nomenclature of the identified species renders much of the data inaccessible to non-specialists. In this review, we focus on parsing both the classical SL nomenclature and the nomenclature normally used during mass spectrometry analysis, which should facilitate the understanding of yeast SL data and might shed light on biological processes in which SLs are involved. Finally, we discuss a number of putative roles of various yeast SL species. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Synthetic genome engineering forging new frontiers for wine yeast.

PubMed

Pretorius, Isak S

2017-02-01

Over the past 15 years, the seismic shifts caused by the convergence of biomolecular, chemical, physical, mathematical, and computational sciences alongside cutting-edge developments in information technology and engineering have erupted into a new field of scientific endeavor dubbed Synthetic Biology. Recent rapid advances in high-throughput DNA sequencing and DNA synthesis techniques are enabling the design and construction of new biological parts (genes), devices (gene networks) and modules (biosynthetic pathways), and the redesign of biological systems (cells and organisms) for useful purposes. In 2014, the budding yeast Saccharomyces cerevisiae became the first eukaryotic cell to be equipped with a fully functional synthetic chromosome. This was achieved following the synthesis of the first viral (poliovirus in 2002 and bacteriophage Phi-X174 in 2003) and bacterial (Mycoplasma genitalium in 2008 and Mycoplasma mycoides in 2010) genomes, and less than two decades after revealing the full genome sequence of a laboratory (S288c in 1996) and wine (AWRI1631 in 2008) yeast strain. A large international project - the Synthetic Yeast Genome (Sc2.0) Project - is now underway to synthesize all 16 chromosomes (∼12 Mb carrying ∼6000 genes) of the sequenced S288c laboratory strain by 2018. If successful, S. cerevisiae will become the first eukaryote to cross the horizon of in silico design of complex cells through de novo synthesis, reshuffling, and editing of genomes. In the meantime, yeasts are being used as cell factories for the semi-synthetic production of high-value compounds, such as the potent antimalarial artemisinin, and food ingredients, such as resveratrol, vanillin, stevia, nootkatone, and saffron. As a continuum of previously genetically engineered industrially important yeast strains, precision genome engineering is bound to also impact the study and development of wine yeast strains supercharged with synthetic DNA. The first taste of what the future
Human alpha beta hydrolase domain containing protein 11 and its yeast homolog are lipid hydrolases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arya, Madhuri; Srinivasan, Malathi; Rajasekharan, Ram

Mammalian alpha/beta hydrolase domain (ABHD) family of proteins have emerged as key regulators of lipid metabolism and are found to be associated with human diseases. Human α/β-hydrolase domain containing protein 11 (ABHD11) has recently been predicted as a potential biomarker for human lung adenocarcinoma. In silico analyses of the ABHD11 protein sequence revealed the presence of a conserved lipase motif GXSXG. However, the role of ABHD11 in lipid metabolism is not known. To understand the biological function of ABHD11, we heterologously expressed the human ABHD11 in budding yeast, Saccharomyces cerevisiae. In vivo [{sup 14}C]acetate labeling of cellular lipids in yeast cellsmore » overexpressing ABHD11 showed a decrease in triacylglycerol content. Overexpression of ABHD11 also alters the molecular species of triacylglycerol in yeast. Similar activity was observed in its yeast homolog, Ygr031w. The role of the conserved lipase motif in the hydrolase activity was proven by the mutation of all conserved amino acid residues of GXSXG motif. Collectively, our results demonstrate that human ABHD11 and its yeast homolog YGR031W have a pivotal role in the lipid metabolism. - Highlights: • Overexpression of ABHD11 protein and its yeast homolog Ygr031w cause a reduction in triacylglycerol levels in yeast. • The reduction in triacylglycerol is due to the presence of lipase motif GXSXG. • Overexpression of ABHD11 and Ygr031w alters the molecular species of triacylglycerol.« less
Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery☆

PubMed Central

Kell, Douglas B.; Goodacre, Royston

2014-01-01

Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule ‘drug’ transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology. PMID:23892182
Producing human ceramide-NS by metabolic engineering using yeast Saccharomyces cerevisiae.

PubMed

Murakami, Suguru; Shimamoto, Toshi; Nagano, Hideaki; Tsuruno, Masahiro; Okuhara, Hiroaki; Hatanaka, Haruyo; Tojo, Hiromasa; Kodama, Yukiko; Funato, Kouichi

2015-11-17

Ceramide is one of the most important intercellular components responsible for the barrier and moisture retention functions of the skin. Because of the risks involved with using products of animal origin and the low productivity of plants, the availability of ceramides is currently limited. In this study, we successfully developed a system that produces sphingosine-containing human ceramide-NS in the yeast Saccharomyces cerevisiae by eliminating the genes for yeast sphingolipid hydroxylases (encoded by SUR2 and SCS7) and introducing the gene for a human sphingolipid desaturase (encoded by DES1). The inactivation of the ceramidase gene YDC1, overexpression of the inositol phosphosphingolipid phospholipase C gene ISC1, and endoplasmic reticulum localization of the DES1 gene product resulted in enhanced production of ceramide-NS. The engineered yeast strains can serve as hosts not only for providing a sustainable source of ceramide-NS but also for developing further systems to produce sphingosine-containing sphingolipids.
Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

PubMed Central

Koch, Ina; Nöthen, Joachim; Schleiff, Enrico

2017-01-01

Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to
Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

PubMed

Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

2013-12-01

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.
Screening the yeast genome for energetic metabolism pathways involved in a phenotypic response to the anti-cancer agent 3-bromopyruvate

PubMed Central

Lis, Paweł; Jurkiewicz, Paweł; Cal-Bąkowska, Magdalena; Ko, Young H.; Pedersen, Peter L.; Goffeau, Andre; Ułaszewski, Stanisław

2016-01-01

In this study the detailed characteristic of the anti-cancer agent 3-bromopyruvate (3-BP) activity in the yeast Saccharomyces cerevisiae model is described, with the emphasis on its influence on energetic metabolism of the cell. It shows that 3-BP toxicity in yeast is strain-dependent and influenced by the glucose-repression system. Its toxic effect is mainly due to the rapid depletion of intracellular ATP. Moreover, lack of the Whi2p phosphatase results in strongly increased sensitivity of yeast cells to 3-BP, possibly due to the non-functional system of mitophagy of damaged mitochondria through the Ras-cAMP-PKA pathway. Single deletions of genes encoding glycolytic enzymes, the TCA cycle enzymes and mitochondrial carriers result in multiple effects after 3-BP treatment. However, it can be concluded that activity of the pentose phosphate pathway is necessary to prevent the toxicity of 3-BP, probably due to the fact that large amounts of NADPH are produced by this pathway, ensuring the reducing force needed for glutathione reduction, crucial to cope with the oxidative stress. Moreover, single deletions of genes encoding the TCA cycle enzymes and mitochondrial carriers generally cause sensitivity to 3-BP, while totally inactive mitochondrial respiration in the rho0 mutant resulted in increased resistance to 3-BP. PMID:26862728
Genetic and phenotypic characteristics of baker's yeast: relevance to baking.

PubMed

Randez-Gil, Francisca; Córcoles-Sáez, Isaac; Prieto, José A

2013-01-01

Yeasts rarely encounter ideal physiological conditions during their industrial life span; therefore, their ability to adapt to changing conditions determines their usefulness and applicability. This is especially true for baking strains of Saccharomyces cerevisiae. The success of this yeast in the ancient art of bread making is based on its capacity to rapidly transform carbohydrates into CO2 rather than its unusual resistance to environmental stresses. Moreover, baker's yeast must exhibit efficient respiratory metabolism during yeast manufacturing, which determines biomass yield. However, optimal growth conditions often have negative consequences in other commercially important aspects, such as fermentative power or stress tolerance. This article reviews the genetic and physiological characteristics of baking yeast strains, emphasizing the activation of regulatory mechanisms in response to carbon source and stress signaling and their importance in defining targets for strain selection and improvement.

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.
Biotechnology of non-Saccharomyces yeasts-the basidiomycetes.

PubMed

Johnson, Eric A

2013-09-01

Yeasts are the major producer of biotechnology products worldwide, exceeding production in capacity and economic revenues of other groups of industrial microorganisms. Yeasts have wide-ranging fundamental and industrial importance in scientific, food, medical, and agricultural disciplines (Fig. 1). Saccharomyces is the most important genus of yeast from fundamental and applied perspectives and has been expansively studied. Non-Saccharomyces yeasts (non-conventional yeasts) including members of the Ascomycetes and Basidiomycetes also have substantial current utility and potential applicability in biotechnology. In an earlier mini-review, "Biotechnology of non-Saccharomyces yeasts-the ascomycetes" (Johnson Appl Microb Biotechnol 97: 503-517, 2013), the extensive biotechnological utility and potential of ascomycetous yeasts are described. Ascomycetous yeasts are particularly important in food and ethanol formation, production of single-cell protein, feeds and fodder, heterologous production of proteins and enzymes, and as model and fundamental organisms for the delineation of genes and their function in mammalian and human metabolism and disease processes. In contrast, the roles of basidiomycetous yeasts in biotechnology have mainly been evaluated only in the past few decades and compared to the ascomycetous yeasts and currently have limited industrial utility. From a biotechnology perspective, the basidiomycetous yeasts are known mainly for the production of enzymes used in pharmaceutical and chemical synthesis, for production of certain classes of primary and secondary metabolites such as terpenoids and carotenoids, for aerobic catabolism of complex carbon sources, and for bioremediation of environmental pollutants and xenotoxicants. Notwithstanding, the basidiomycetous yeasts appear to have considerable potential in biotechnology owing to their catabolic utilities, formation of enzymes acting on recalcitrant substrates, and through the production of unique primary
Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

PubMed

Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

2018-04-01

Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.
Context-specific metabolic networks are consistent with experiments.

PubMed

Becker, Scott A; Palsson, Bernhard O

2008-05-16

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.
Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks.

PubMed

Abedpour, Nima; Kollmann, Markus

2015-11-23

A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection. We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle. We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.
Metabolic network prediction through pairwise rational kernels.

PubMed

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy
Understanding Regulation of Metabolism through Feasibility Analysis

PubMed Central

Nikerel, Emrah; Berkhout, Jan; Hu, Fengyuan; Teusink, Bas; Reinders, Marcel J. T.; de Ridder, Dick

2012-01-01

Understanding cellular regulation of metabolism is a major challenge in systems biology. Thus far, the main assumption was that enzyme levels are key regulators in metabolic networks. However, regulation analysis recently showed that metabolism is rarely controlled via enzyme levels only, but through non-obvious combinations of hierarchical (gene and enzyme levels) and metabolic regulation (mass action and allosteric interaction). Quantitative analyses relating changes in metabolic fluxes to changes in transcript or protein levels have revealed a remarkable lack of understanding of the regulation of these networks. We study metabolic regulation via feasibility analysis (FA). Inspired by the constraint-based approach of Flux Balance Analysis, FA incorporates a model describing kinetic interactions between molecules. We enlarge the portfolio of objectives for the cell by defining three main physiologically relevant objectives for the cell: function, robustness and temporal responsiveness. We postulate that the cell assumes one or a combination of these objectives and search for enzyme levels necessary to achieve this. We call the subspace of feasible enzyme levels the feasible enzyme space. Once this space is constructed, we can study how different objectives may (if possible) be combined, or evaluate the conditions at which the cells are faced with a trade-off among those. We apply FA to the experimental scenario of long-term carbon limited chemostat cultivation of yeast cells, studying how metabolism evolves optimally. Cells employ a mixed strategy composed of increasing enzyme levels for glucose uptake and hexokinase and decreasing levels of the remaining enzymes. This trade-off renders the cells specialized in this low-carbon flux state to compete for the available glucose and get rid of over-overcapacity. Overall, we show that FA is a powerful tool for systems biologists to study regulation of metabolism, interpret experimental data and evaluate hypotheses. PMID
Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast.

PubMed

Himmelreich, Uwe; Sorrell, Tania C; Daniel, Heide-Marie

2017-01-01

Rapid and robust high-throughput identification of environmental, industrial, or clinical yeast isolates is important whenever relatively large numbers of samples need to be processed in a cost-efficient way. Nuclear magnetic resonance (NMR) spectroscopy generates complex data based on metabolite profiles, chemical composition and possibly on medium consumption, which can not only be used for the assessment of metabolic pathways but also for accurate identification of yeast down to the subspecies level. Initial results on NMR based yeast identification where comparable with conventional and DNA-based identification. Potential advantages of NMR spectroscopy in mycological laboratories include not only accurate identification but also the potential of automated sample delivery, automated analysis using computer-based methods, rapid turnaround time, high throughput, and low running costs.We describe here the sample preparation, data acquisition and analysis for NMR-based yeast identification. In addition, a roadmap for the development of classification strategies is given that will result in the acquisition of a database and analysis algorithms for yeast identification in different environments.
Gender Differences of Brain Glucose Metabolic Networks Revealed by FDG-PET: Evidence from a Large Cohort of 400 Young Adults

PubMed Central

Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Background Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. Materials and Methods FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25∼45 years, mean age±SD: 40.9±3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Results Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. Conclusion This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control
Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults.

PubMed

Hu, Yuxiao; Xu, Qiang; Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25:45 years, mean age ± SD: 40.9 ± 3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.
Live Yeast and Yeast Cell Wall Supplements Enhance Immune Function and Performance in Food-Producing Livestock: A Review †,‡

PubMed Central

Broadway, Paul R.; Carroll, Jeffery A.; Burdick Sanchez, Nicole C.

2015-01-01

More livestock producers are seeking natural alternatives to antibiotics and antimicrobials, and searching for supplements to enhance growth performance, and general animal health and well-being. Some of the compounds currently being utilized and studied are live yeast and yeast-based products derived from the strain Saccharomyces cerevisiae. These products have been reported to have positive effects both directly and indirectly on the immune system and its subsequent biomarkers, thereby mitigating negative effects associated with stress and disease. These yeast-based products have also been reported to simultaneously enhance growth and performance by enhancing dry matter intake (DMI) and average daily gain (ADG) perhaps through the establishment of a healthy gastrointestinal tract. These products may be especially useful in times of potential stress such as during birth, weaning, early lactation, and during the receiving period at the feedlot. Overall, yeast supplements appear to possess the ability to improve animal health and metabolism while decreasing morbidity, thereby enhancing profitability of these animals. PMID:27682097
Metabolic efficiency in yeast Saccharomyces cerevisiae in relation to temperature dependent growth and biomass yield.

PubMed

Zakhartsev, Maksim; Yang, Xuelian; Reuss, Matthias; Pörtner, Hans Otto

2015-08-01

Canonized view on temperature effects on growth rate of microorganisms is based on assumption of protein denaturation, which is not confirmed experimentally so far. We develop an alternative concept, which is based on view that limits of thermal tolerance are based on imbalance of cellular energy allocation. Therefore, we investigated growth suppression of yeast Saccharomyces cerevisiae in the supraoptimal temperature range (30-40°C), i.e. above optimal temperature (Topt). The maximal specific growth rate (μmax) of biomass, its concentration and yield on glucose (Yx/glc) were measured across the whole thermal window (5-40°C) of the yeast in batch anaerobic growth on glucose. Specific rate of glucose consumption, specific rate of glucose consumption for maintenance (mglc), true biomass yield on glucose (Yx/glc(true)), fractional conservation of substrate carbon in product and ATP yield on glucose (Yatp/glc) were estimated from the experimental data. There was a negative linear relationship between ATP, ADP and AMP concentrations and specific growth rate at any growth conditions, whilst the energy charge was always high (~0.83). There were two temperature regions where mglc differed 12-fold, which points to the existence of a 'low' (within 5-31°C) and a 'high' (within 33-40°C) metabolic mode regarding maintenance requirements. The rise from the low to high mode occurred at 31-32°C in step-wise manner and it was accompanied with onset of suppression of μmax. High mglc at supraoptimal temperatures indicates a significant reduction of scope for growth, due to high maintenance cost. Analysis of temperature dependencies of product formation efficiency and Yatp/glc revealed that the efficiency of energy metabolism approaches its lower limit at 26-31°C. This limit is reflected in the predetermined combination of Yx/glc(true), elemental biomass composition and degree of reduction of the growth substrate. Approaching the limit implies a reduction of the safety margin
The glyoxylate shunt is essential for desiccation tolerance in C. elegans and budding yeast

PubMed Central

Erkut, Cihan; Gade, Vamshidhar R; Laxman, Sunil; Kurzchalia, Teymuras V

2016-01-01

Many organisms, including species from all kingdoms of life, can survive desiccation by entering a state with no detectable metabolism. To survive, C. elegans dauer larvae and stationary phase S. cerevisiae require elevated amounts of the disaccharide trehalose. We found that dauer larvae and stationary phase yeast switched into a gluconeogenic mode in which metabolism was reoriented toward production of sugars from non-carbohydrate sources. This mode depended on full activity of the glyoxylate shunt (GS), which enables synthesis of trehalose from acetate. The GS was especially critical during preparation of worms for harsh desiccation (preconditioning) and during the entry of yeast into stationary phase. Loss of the GS dramatically decreased desiccation tolerance in both organisms. Our results reveal a novel physiological role for the GS and elucidate a conserved metabolic rewiring that confers desiccation tolerance on organisms as diverse as worm and yeast. DOI: http://dx.doi.org/10.7554/eLife.13614.001 PMID:27090086
Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

PubMed Central

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T.; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-01-01

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut. PMID:28585563
Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis.

PubMed

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-06-06

A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.
Yeasts are essential for cocoa bean fermentation.

PubMed

Ho, Van Thi Thuy; Zhao, Jian; Fleet, Graham

2014-03-17

Cocoa beans (Theobroma cacao) are the major raw material for chocolate production and fermentation of the beans is essential for the development of chocolate flavor precursors. In this study, a novel approach was used to determine the role of yeasts in cocoa fermentation and their contribution to chocolate quality. Cocoa bean fermentations were conducted with the addition of 200ppm Natamycin to inhibit the growth of yeasts, and the resultant microbial ecology and metabolism, bean chemistry and chocolate quality were compared with those of normal (control) fermentations. The yeasts Hanseniaspora guilliermondii, Pichia kudriavzevii and Kluyveromyces marxianus, the lactic acid bacteria Lactobacillus plantarum and Lactobacillus fermentum and the acetic acid bacteria Acetobacter pasteurianus and Gluconobacter frateurii were the major species found in the control fermentation. In fermentations with the presence of Natamycin, the same bacterial species grew but yeast growth was inhibited. Physical and chemical analyses showed that beans fermented without yeasts had increased shell content, lower production of ethanol, higher alcohols and esters throughout fermentation and lesser presence of pyrazines in the roasted product. Quality tests revealed that beans fermented without yeasts were purplish-violet in color and not fully brown, and chocolate prepared from these beans tasted more acid and lacked characteristic chocolate flavor. Beans fermented with yeast growth were fully brown in color and gave chocolate with typical characters which were clearly preferred by sensory panels. Our findings demonstrate that yeast growth and activity were essential for cocoa bean fermentation and the development of chocolate characteristics. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.
Detection of driver metabolites in the human liver metabolic network using structural controllability analysis

PubMed Central

2014-01-01

Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism. PMID:24885538
Yeasts in floral nectar: a quantitative survey

PubMed Central

Herrera, Carlos M.; de Vega, Clara; Canto, Azucena; Pozo, María I.

2009-01-01

Background and Aims One peculiarity of floral nectar that remains relatively unexplored from an ecological perspective is its role as a natural habitat for micro-organisms. This study assesses the frequency of occurrence and abundance of yeast cells in floral nectar of insect-pollinated plants from three contrasting plant communities on two continents. Possible correlations between interspecific differences in yeast incidence and pollinator composition are also explored. Methods The study was conducted at three widely separated areas, two in the Iberian Peninsula (Spain) and one in the Yucatán Peninsula (Mexico). Floral nectar samples from 130 species (37–63 species per region) in 44 families were examined microscopically for the presence of yeast cells. For one of the Spanish sites, the relationship across species between incidence of yeasts in nectar and the proportion of flowers visited by each of five major pollinator categories was also investigated. Key Results Yeasts occurred regularly in the floral nectar of many species, where they sometimes reached extraordinary densities (up to 4 × 105 cells mm−3). Depending on the region, between 32 and 44 % of all nectar samples contained yeasts. Yeast cell densities in the order of 104 cells mm−3 were commonplace, and densities >105 cells mm−3 were not rare. About one-fifth of species at each site had mean yeast cell densities >104 cells mm−3. Across species, yeast frequency and abundance were directly correlated with the proportion of floral visits by bumble-bees, and inversely with the proportion of visits by solitary bees. Conclusions Incorporating nectar yeasts into the scenario of plant–pollinator interactions opens up a number of intriguing avenues for research. In addition, with yeasts being as ubiquitous and abundant in floral nectars as revealed by this study, and given their astounding metabolic versatility, studies focusing on nectar chemical features should carefully control for the presence
Transporter engineering in biomass utilization by yeast.

PubMed

Hara, Kiyotaka Y; Kobayashi, Jyumpei; Yamada, Ryosuke; Sasaki, Daisuke; Kuriya, Yuki; Hirono-Hara, Yoko; Ishii, Jun; Araki, Michihiro; Kondo, Akihiko

2017-11-01

Biomass resources are attractive carbon sources for bioproduction because of their sustainability. Many studies have been performed using biomass resources to produce sugars as carbon sources for cell factories. Expression of biomass hydrolyzing enzymes in cell factories is an important approach for constructing biomass-utilizing bioprocesses because external addition of these enzymes is expensive. In particular, yeasts have been extensively engineered to be cell factories that directly utilize biomass because of their manageable responses to many genetic engineering tools, such as gene expression, deletion and editing. Biomass utilizing bioprocesses have also been developed using these genetic engineering tools to construct metabolic pathways. However, sugar input and product output from these cells are critical factors for improving bioproduction along with biomass utilization and metabolic pathways. Transporters are key components for efficient input and output activities. In this review, we focus on transporter engineering in yeast to enhance bioproduction from biomass resources. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

PubMed

Holland, David O; Johnson, Margaret E

2018-03-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

PubMed Central

Blais, Edik M.; Rawls, Kristopher D.; Dougherty, Bonnie V.; Li, Zhuo I.; Kolling, Glynis L.; Ye, Ping; Wallqvist, Anders; Papin, Jason A.

2017-01-01

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications. PMID:28176778
The Importance of Transition Metals in the Expanding Network of Microbial Metabolism in the Archean Eon

NASA Astrophysics Data System (ADS)

Moore, E. K.; Jelen, B. I.; Giovannelli, D.; Prabhu, A.; Raanan, H.; Falkowski, P. G.

2017-12-01

Deep time changes in Earth surface redox conditions, particularly due to global oxygenation, has impacted the availability of different metals and substrates that are central in biology. Oxidoreductase proteins are molecular nanomachines responsible for all biological electron transfer processes across the tree of life. These enzymes largely contain transition metals in their active sites. Microbial metabolic pathways form a global network of electron transfer, which expanded throughout the Archean eon. Older metabolisms (sulfur reduction, methanogenesis, anoxygenic photosynthesis) accessed negative redox potentials, while later evolving metabolisms (oxygenic photosynthesis, nitrification/denitrification, aerobic respiration) accessed positive redox potentials. The incorporation of different transition metals facilitated biological innovation and the expansion of the network of microbial metabolism. Network analysis was used to examine the connections between microbial taxa, metabolic pathways, crucial metallocofactors, and substrates in deep time by incorporating biosignatures preserved in the geologic record. Nitrogen fixation and aerobic respiration have the highest level of betweenness among metabolisms in the network, indicating that the oldest metabolisms are not the most central. Fe has by far the highest betweenness among metals. Clustering analysis largely separates High Metal Bacteria (HMB), Low Metal Bacteria (LMB), and Archaea showing that simple un-weighted links between taxa, metabolism, and metals have phylogenetic relevance. On average HMB have the highest betweenness among taxa, followed by Archaea and LMB. There is a correlation between the number of metallocofactors and metabolic pathways in representative bacterial taxa, but Archaea do not follow this trend. In many cases older and more recently evolved metabolisms were clustered together supporting previous findings that proliferation of metabolic pathways is not necessarily chronological.
Integrating data from biological experiments into metabolic networks with the DBE information system.

PubMed

Borisjuk, Ljudmilla; Hajirezaei, Mohammad-Reza; Klukas, Christian; Rolletschek, Hardy; Schreiber, Falk

2005-01-01

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.
Teaching nutritional biochemistry: an experimental approach using yeast.

PubMed

Alonso, Manuel; Stella, Carlos A

2012-12-01

In this report, we present a practical approach to teaching several topics in nutrition to science students at the high school and college freshmen levels. This approach uses baker's yeast (Saccharomyces cerevisiae) as a biological system model. The diameters of yeast colonies, which vary according to the nutrients present in the medium, can be observed, compared, and used to teach metabolic requirements. The experiments described in this report show simple macroscopic evidence of submicroscopic nutritional events. This can serve as a useful base for an analogy of heterotrophic human cell nutrition.
Polycyclic aromatic hydrocarbon metabolic network in Mycobacterium vanbaalenii PYR-1.

PubMed

Kweon, Ohgew; Kim, Seong-Jae; Holland, Ricky D; Chen, Hongyan; Kim, Dae-Wi; Gao, Yuan; Yu, Li-Rong; Baek, Songjoon; Baek, Dong-Heon; Ahn, Hongsik; Cerniglia, Carl E

2011-09-01

This study investigated a metabolic network (MN) from Mycobacterium vanbaalenii PYR-1 for polycyclic aromatic hydrocarbons (PAHs) from the perspective of structure, behavior, and evolution, in which multilayer omics data are integrated. Initially, we utilized a high-throughput proteomic analysis to assess the protein expression response of M. vanbaalenii PYR-1 to seven different aromatic compounds. A total of 3,431 proteins (57.38% of the genome-predicted proteins) were identified, which included 160 proteins that seemed to be involved in the degradation of aromatic hydrocarbons. Based on the proteomic data and the previous metabolic, biochemical, physiological, and genomic information, we reconstructed an experiment-based system-level PAH-MN. The structure of PAH-MN, with 183 metabolic compounds and 224 chemical reactions, has a typical scale-free nature. The behavior and evolution of the PAH-MN reveals a hierarchical modularity with funnel effects in structure/function and intimate association with evolutionary modules of the functional modules, which are the ring cleavage process (RCP), side chain process (SCP), and central aromatic process (CAP). The 189 commonly upregulated proteins in all aromatic hydrocarbon treatments provide insights into the global adaptation to facilitate the PAH metabolism. Taken together, the findings of our study provide the hierarchical viewpoint from genes/proteins/metabolites to the network via functional modules of the PAH-MN equipped with the engineering-driven approaches of modularization and rationalization, which may expand our understanding of the metabolic potential of M. vanbaalenii PYR-1 for bioremediation applications.
Analyzing and Understanding Lipids of Yeast: A Challenging Endeavor.

PubMed

Kohlwein, Sepp D

2017-05-01

Lipids are essential biomolecules with diverse biological functions, ranging from building blocks for all biological membranes to energy substrates, signaling molecules, and protein modifiers. Despite advances in lipid analytics by mass spectrometry, the extraction and quantitative analysis of the diverse classes of lipids are still an experimental challenge. Yeast is a model organism that provides several advantages for studying lipid metabolism, because most biosynthetic pathways are well described and a great deal of information is available on the regulatory mechanisms that control lipid homeostasis. In addition, the composition of yeast lipids is much less complex than that of mammalian lipids, making yeast an excellent reference system for studying lipid-associated cell functions. © 2017 Cold Spring Harbor Laboratory Press.
Polyamines in the Context of Metabolic Networks.

PubMed

Wuddineh, Wegi; Minocha, Rakesh; Minocha, Subhash C

2018-01-01

Polyamines (PAs) are essential biomolecules that are known to be involved in the regulation of many plant developmental and growth processes as well as their response to different environmental stimuli. Maintaining the cellular pools of PAs or their metabolic precursors and by-products is critical to accomplish their normal functions. Therefore, the titre of PAs in the cells must be under tight regulation to enable cellular PA homeostasis. Polyamine homeostasis is hence achieved by the regulation of their input into the cellular PA pool, their conversion into secondary metabolites, their transport to other issues/organs, and their catabolism or turnover. The major contributors of input to the PA pools are their in vivo biosynthesis, interconversion between different PAs, and transport from other tissues/organs; while the output or turnover of PAs is facilitated by transport, conjugation and catabolism. Polyamine metabolic pathways including the biosynthesis, catabolism/turnover and conjugation with various organic molecules have been widely studied in all kingdoms. Discoveries on the molecular transporters facilitating the intracellular and intercellular translocation of PAs have also been reported. Numerous recent studies using transgenic approaches and mutagenesis have shown that plants can tolerate quite large concentrations of PAs in the cells; even though, at times, high cellular accumulation of PAs is quite detrimental, and so is high rate of catabolism. The mechanism by which plants tolerate such large quantities of PAs is still unclear. Interestingly, enhanced PA biosynthesis via manipulation of the PA metabolic networks has been suggested to contribute directly to increased growth and improvements in plant abiotic and biotic stress responses; hence greater biomass and productivity. Genetic manipulation of the PA metabolic networks has also been shown to improve plant nitrogen assimilation capacity, which may in turn lead to enhanced carbon assimilation
Physiological and environmental control of yeast prions

PubMed Central

Chernova, Tatiana A.; Wilkinson, Keith D.; Chernoff, Yury O.

2014-01-01

Prions are self-perpetuating protein isoforms that cause fatal and incurable neurodegenerative disease in mammals. Recent evidence indicates that a majority of human proteins involved in amyloid and neural inclusion disorders possess at least some prion properties. In lower eukaryotes, such as yeast, prions act as epigenetic elements, which increase phenotypic diversity by altering a range of cellular processes. While some yeast prions are clearly pathogenic, it is also postulated that prion formation could be beneficial in variable environmental conditions. Yeast and mammalian prions have similar molecular properties. Crucial cellular factors and conditions influencing prion formation and propagation were uncovered in the yeast models. Stress-related chaperones, protein quality control deposits, degradation pathways and cytoskeletal networks control prion formation and propagation in yeast. Environmental stresses trigger prion formation and loss, supposedly acting via influencing intracellular concentrations of the prion-inducing proteins, and/or by localizing prionogenic proteins to the prion induction sites via heterologous ancillary helpers. Physiological and environmental modulation of yeast prions points to new opportunities for pharmacological intervention and/or prophylactic measures targeting general cellular systems rather than the properties of individual amyloids and prions. PMID:24236638
Selection of oleaginous yeasts for fatty acid production.

PubMed

Lamers, Dennis; van Biezen, Nick; Martens, Dirk; Peters, Linda; van de Zilver, Eric; Jacobs-van Dreumel, Nicole; Wijffels, René H; Lokman, Christien

2016-05-27

Oleaginous yeast species are an alternative for the production of lipids or triacylglycerides (TAGs). These yeasts are usually non-pathogenic and able to store TAGs ranging from 20 % to 70 % of their cell mass depending on culture conditions. TAGs originating from oleaginous yeasts can be used as the so-called second generation biofuels, which are based on non-food competing "waste carbon sources". In this study the selection of potentially new interesting oleaginous yeast strains is described. Important selection criteria were: a broad maximum temperature and pH range for growth (robustness of the strain), a broad spectrum of carbon sources that can be metabolized (preferably including C-5 sugars), a high total fatty acid content in combination with a low glycogen content and genetic accessibility. Based on these selection criteria, among 24 screened species, Schwanniomyces occidentalis (Debaromyces occidentalis) CBS2864 was selected as a promising strain for the production of high amounts of lipids.
Comparative genomics of biotechnologically important yeasts

PubMed Central

Riley, Robert; Haridas, Sajeet; Wolfe, Kenneth H.; Lopes, Mariana R.; Hittinger, Chris Todd; Göker, Markus; Salamov, Asaf A.; Wisecaver, Jennifer H.; Long, Tanya M.; Aerts, Andrea L.; Barry, Kerrie W.; Choi, Cindy; Clum, Alicia; Coughlan, Aisling Y.; Deshpande, Shweta; Douglass, Alexander P.; Hanson, Sara J.; Klenk, Hans-Peter; LaButti, Kurt M.; Lapidus, Alla; Lindquist, Erika A.; Lipzen, Anna M.; Meier-Kolthoff, Jan P.; Ohm, Robin A.; Otillar, Robert P.; Pangilinan, Jasmyn L.; Peng, Yi; Rosa, Carlos A.; Scheuner, Carmen; Sibirny, Andriy A.; Slot, Jason C.; Stielow, J. Benjamin; Sun, Hui; Kurtzman, Cletus P.; Blackwell, Meredith; Grigoriev, Igor V.

2016-01-01

Ascomycete yeasts are metabolically diverse, with great potential for biotechnology. Here, we report the comparative genome analysis of 29 taxonomically and biotechnologically important yeasts, including 16 newly sequenced. We identify a genetic code change, CUG-Ala, in Pachysolen tannophilus in the clade sister to the known CUG-Ser clade. Our well-resolved yeast phylogeny shows that some traits, such as methylotrophy, are restricted to single clades, whereas others, such as l-rhamnose utilization, have patchy phylogenetic distributions. Gene clusters, with variable organization and distribution, encode many pathways of interest. Genomics can predict some biochemical traits precisely, but the genomic basis of others, such as xylose utilization, remains unresolved. Our data also provide insight into early evolution of ascomycetes. We document the loss of H3K9me2/3 heterochromatin, the origin of ascomycete mating-type switching, and panascomycete synteny at the MAT locus. These data and analyses will facilitate the engineering of efficient biosynthetic and degradative pathways and gateways for genomic manipulation. PMID:27535936
Sphingolipid hydroxylation in mammals, yeast and plants - An integrated view.

PubMed

Marquês, Joaquim Trigo; Susana Marinho, H; de Almeida, Rodrigo Freire Martins

2018-05-07

This review is focused on sphingolipid backbone hydroxylation, a small but widespread structural feature, with profound impact on membrane biophysical properties. We start by summarizing sphingolipid metabolism in mammalian cells, yeast and plants, focusing on how distinct hydroxylation patterns emerge in different eukaryotic kingdoms. Then, a comparison of the biophysical properties in membrane model systems and cellular membranes from diverse organisms is made. From an integrative perspective, these results can be rationalized considering that superficial hydroxyl groups in the backbone of sphingolipids (by intervening in the H-bond network) alter the balance of favorable interactions between membrane lipids. They may strengthen the bonding or compete with other hydroxyl groups, in particular the one of membrane sterols. Different sphingolipid hydroxylation patterns can stabilize/disrupt specific membrane domains or change whole plasma membrane properties, and therefore be important in the control of protein distribution, function and lateral diffusion and in the formation and overtime stability of signaling platforms. The recent examples explored throughout this review unveil a potentially key role for sphingolipid backbone hydroxylation in both physiological and pathological situations, as they can be of extreme importance for the proper organization of cell membranes in mammalian cells, yeast and, most likely, also in plants. Copyright © 2017. Published by Elsevier Ltd.
Evolutionary engineering of a wine yeast strain revealed a key role of inositol and mannoprotein metabolism during low-temperature fermentation.

PubMed

López-Malo, María; García-Rios, Estéfani; Melgar, Bruno; Sanchez, Monica R; Dunham, Maitreya J; Guillamón, José Manuel

2015-07-22

Wine produced at low temperature is often considered to improve sensory qualities. However, there are certain drawbacks to low temperature fermentations: e.g. low growth rate, long lag phase, and sluggish or stuck fermentations. Selection and development of new Saccharomyces cerevisiae strains well adapted at low temperature is interesting for future biotechnological applications. This study aimed to select and develop wine yeast strains that well adapt to ferment at low temperature through evolutionary engineering, and to decipher the process underlying the obtained phenotypes. We used a pool of 27 commercial yeast strains and set up batch serial dilution experiments to mimic wine fermentation conditions at 12 °C. Evolutionary engineering was accomplished by using the natural yeast mutation rate and mutagenesis procedures. One strain (P5) outcompeted the others under both experimental conditions and was able to impose after 200 generations. The evolved strains showed improved growth and low-temperature fermentation performance compared to the ancestral strain. This improvement was acquired only under inositol limitation. The transcriptomic comparison between the evolved and parental strains showed the greatest up-regulation in four mannoprotein coding genes, which belong to the DAN/TIR family (DAN1, TIR1, TIR4 and TIR3). Genome sequencing of the evolved strain revealed the presence of a SNP in the GAA1 gene and the construction of a site-directed mutant (GAA1 (Thr108)) in a derivative haploid of the ancestral strain resulted in improved fermentation performance. GAA1 encodes a GPI transamidase complex subunit that adds GPI, which is required for inositol synthesis, to newly synthesized proteins, including mannoproteins. In this study we demonstrate the importance of inositol and mannoproteins in yeast adaptation at low temperature and the central role of the GAA1 gene by linking both metabolisms.
Topological Organization of Metabolic Brain Networks in Pre-Chemotherapy Cancer with Depression: A Resting-State PET Study

PubMed Central

An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing

2016-01-01

This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients. PMID:27832148
Topological Organization of Metabolic Brain Networks in Pre-Chemotherapy Cancer with Depression: A Resting-State PET Study.

PubMed

Fang, Lei; Yao, Zhijun; An, Jianping; Chen, Xuejiao; Xie, Yuanwei; Zhao, Hui; Mao, Junfeng; Liang, Wangsheng; Ma, Xiangxing

2016-01-01

This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients.
Yeast "make-accumulate-consume" life strategy evolved as a multi-step process that predates the whole genome duplication.

PubMed

Hagman, Arne; Säll, Torbjörn; Compagno, Concetta; Piskur, Jure

2013-01-01

When fruits ripen, microbial communities start a fierce competition for the freely available fruit sugars. Three yeast lineages, including baker's yeast Saccharomyces cerevisiae, have independently developed the metabolic activity to convert simple sugars into ethanol even under fully aerobic conditions. This fermentation capacity, named Crabtree effect, reduces the cell-biomass production but provides in nature a tool to out-compete other microorganisms. Here, we analyzed over forty Saccharomycetaceae yeasts, covering over 200 million years of the evolutionary history, for their carbon metabolism. The experiments were done under strictly controlled and uniform conditions, which has not been done before. We show that the origin of Crabtree effect in Saccharomycetaceae predates the whole genome duplication and became a settled metabolic trait after the split of the S. cerevisiae and Kluyveromyces lineages, and coincided with the origin of modern fruit bearing plants. Our results suggest that ethanol fermentation evolved progressively, involving several successive molecular events that have gradually remodeled the yeast carbon metabolism. While some of the final evolutionary events, like gene duplications of glucose transporters and glycolytic enzymes, have been deduced, the earliest molecular events initiating Crabtree effect are still to be determined.
Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

DOE PAGES

Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; ...

2015-05-28

Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combinedmore » omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. In conclusion, evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.« less
Regulation of Amino Acid, Nucleotide, and Phosphate Metabolism in Saccharomyces cerevisiae

PubMed Central

Ljungdahl, Per O.; Daignan-Fornier, Bertrand

2012-01-01

Ever since the beginning of biochemical analysis, yeast has been a pioneering model for studying the regulation of eukaryotic metabolism. During the last three decades, the combination of powerful yeast genetics and genome-wide approaches has led to a more integrated view of metabolic regulation. Multiple layers of regulation, from suprapathway control to individual gene responses, have been discovered. Constitutive and dedicated systems that are critical in sensing of the intra- and extracellular environment have been identified, and there is a growing awareness of their involvement in the highly regulated intracellular compartmentalization of proteins and metabolites. This review focuses on recent developments in the field of amino acid, nucleotide, and phosphate metabolism and provides illustrative examples of how yeast cells combine a variety of mechanisms to achieve coordinated regulation of multiple metabolic pathways. Importantly, common schemes have emerged, which reveal mechanisms conserved among various pathways, such as those involved in metabolite sensing and transcriptional regulation by noncoding RNAs or by metabolic intermediates. Thanks to the remarkable sophistication offered by the yeast experimental system, a picture of the intimate connections between the metabolomic and the transcriptome is becoming clear. PMID:22419079
Mimoza: web-based semantic zooming and navigation in metabolic networks.

PubMed

Zhukova, Anna; Sherman, David J

2015-02-26

The complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. Interestingly, hidden similarities between groups of reactions can be discovered, and generalized to reveal higher-level patterns. The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down manner Mimoza can be installed standalone, or used on-line at http://mimoza.bordeaux.inria.fr/ , or installed in a Galaxy server for use in workflows. Mimoza views can be embedded in web pages, or downloaded as COMBINE archives.
Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder.

PubMed

Wu, Ping; Yu, Huan; Peng, Shichun; Dauvilliers, Yves; Wang, Jian; Ge, Jingjie; Zhang, Huiwei; Eidelberg, David; Ma, Yilong; Zuo, Chuantao

2014-12-01

Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Analysis of the yeast short-term Crabtree effect and its origin

PubMed Central

Hagman, Arne; Säll, Torbjörn; Piškur, Jure

2014-01-01

The short-term Crabtree effect is defined as the immediate occurrence of aerobic alcoholic fermentation in response to provision of a pulse of excess sugar to sugar-limited yeast cultures. Here we have characterized ten yeast species with a clearly defined phylogenetic relationship. Yeast species were cultivated under glucose-limited conditions, and we studied their general carbon metabolism in response to a glucose pulse. We generated an extensive collection of data on glucose and oxygen consumption, and ethanol and carbon dioxide generation. We conclude that the Pichia,Debaryomyces,Eremothecium and Kluyveromyces marxianus yeasts do not exhibit any significant ethanol formation, while Kluyveromyces lactis behaves as an intermediate yeast, and Lachancea,Torulaspora,Vanderwaltozyma and Saccharomyces yeasts exhibit rapid ethanol accumulation. Based on the present data and our previous data relating to the presence of the long-term Crabtree effect in over 40 yeast species, we speculate that the origin of the short-term effect may coincide with the origin of the long-term Crabtree effect in the Saccharomycetales lineage, occurring ∼ 150 million years ago. PMID:25161062

A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

PubMed Central

2011-01-01

Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. PMID:22041191
A Study on the Fundamental Mechanism and the Evolutionary Driving Forces behind Aerobic Fermentation in Yeast

PubMed Central

Hagman, Arne; Piškur, Jure

2015-01-01

Baker’s yeast Saccharomyces cerevisiae rapidly converts sugars to ethanol and carbon dioxide at both anaerobic and aerobic conditions. The later phenomenon is called Crabtree effect and has been described in two forms, long-term and short-term effect. We have previously studied under fully controlled aerobic conditions forty yeast species for their central carbon metabolism and the presence of long-term Crabtree effect. We have also studied ten steady-state yeast cultures, pulsed them with glucose, and followed the central carbon metabolism and the appearance of ethanol at dynamic conditions. In this paper we analyzed those wet laboratory data to elucidate possible mechanisms that determine the fate of glucose in different yeast species that cover approximately 250 million years of evolutionary history. We determine overflow metabolism to be the fundamental mechanism behind both long- and short-term Crabtree effect, which originated approximately 125–150 million years ago in the Saccharomyces lineage. The “invention” of overflow metabolism was the first step in the evolution of aerobic fermentation in yeast. It provides a general strategy to increase energy production rates, which we show is positively correlated to growth. The “invention” of overflow has also simultaneously enabled rapid glucose consumption in yeast, which is a trait that could have been selected for, to “starve” competitors in nature. We also show that glucose repression of respiration is confined mainly among S. cerevisiae and closely related species that diverged after the whole genome duplication event, less than 100 million years ago. Thus, glucose repression of respiration was apparently “invented” as a second step to further increase overflow and ethanol production, to inhibit growth of other microbes. The driving force behind the initial evolutionary steps was most likely competition with other microbes to faster consume and convert sugar into biomass, in niches that
A study on the fundamental mechanism and the evolutionary driving forces behind aerobic fermentation in yeast.

PubMed

Hagman, Arne; Piškur, Jure

2015-01-01

Baker's yeast Saccharomyces cerevisiae rapidly converts sugars to ethanol and carbon dioxide at both anaerobic and aerobic conditions. The later phenomenon is called Crabtree effect and has been described in two forms, long-term and short-term effect. We have previously studied under fully controlled aerobic conditions forty yeast species for their central carbon metabolism and the presence of long-term Crabtree effect. We have also studied ten steady-state yeast cultures, pulsed them with glucose, and followed the central carbon metabolism and the appearance of ethanol at dynamic conditions. In this paper we analyzed those wet laboratory data to elucidate possible mechanisms that determine the fate of glucose in different yeast species that cover approximately 250 million years of evolutionary history. We determine overflow metabolism to be the fundamental mechanism behind both long- and short-term Crabtree effect, which originated approximately 125-150 million years ago in the Saccharomyces lineage. The "invention" of overflow metabolism was the first step in the evolution of aerobic fermentation in yeast. It provides a general strategy to increase energy production rates, which we show is positively correlated to growth. The "invention" of overflow has also simultaneously enabled rapid glucose consumption in yeast, which is a trait that could have been selected for, to "starve" competitors in nature. We also show that glucose repression of respiration is confined mainly among S. cerevisiae and closely related species that diverged after the whole genome duplication event, less than 100 million years ago. Thus, glucose repression of respiration was apparently "invented" as a second step to further increase overflow and ethanol production, to inhibit growth of other microbes. The driving force behind the initial evolutionary steps was most likely competition with other microbes to faster consume and convert sugar into biomass, in niches that were semi-anaerobic.
Human-Centered Development of an Online Social Network for Metabolic Syndrome Management.

PubMed

Núñez-Nava, Jefersson; Orozco-Sánchez, Paola A; López, Diego M; Ceron, Jesus D; Alvarez-Rosero, Rosa E

2016-01-01

According to the International Diabetes Federation (IDF), a quarter of the world's population has Metabolic Syndrome (MS). To develop (and assess the users' degree of satisfaction of) an online social network for patients who suffer from Metabolic Syndrome, based on the recommendations and requirements of the Human-Centered Design. Following the recommendations of the ISO 9241-210 for Human-Centered Design (HCD), an online social network was designed to promote physical activity and healthy nutrition. In order to guarantee the active participation of the users during the development of the social network, a survey, an in-depth interview, a focal group, and usability tests were carried out with people suffering from MS. The study demonstrated how the different activities, recommendations, and requirements of the ISO 9241-210 are integrated into a traditional software development process. Early usability tests demonstrated that the user's acceptance and the effectiveness and efficiency of the social network are satisfactory.
Nitrogen and carbon source balance determines longevity, independently of fermentative or respiratory metabolism in the yeast Saccharomyces cerevisiae.

PubMed

Santos, Júlia; Leitão-Correia, Fernanda; Sousa, Maria João; Leão, Cecília

2016-04-26

Dietary regimens have proven to delay aging and age-associated diseases in several eukaryotic model organisms but the input of nutritional balance to longevity regulation is still poorly understood. Here, we present data on the role of single carbon and nitrogen sources and their interplay in yeast longevity. Data demonstrate that ammonium, a rich nitrogen source, decreases chronological life span (CLS) of the prototrophic Saccharomyces cerevisiae strain PYCC 4072 in a concentration-dependent manner and, accordingly, that CLS can be extended through ammonium restriction, even in conditions of initial glucose abundance. We further show that CLS extension depends on initial ammonium and glucose concentrations in the growth medium, as long as other nutrients are not limiting. Glutamine, another rich nitrogen source, induced CLS shortening similarly to ammonium, but this effect was not observed with the poor nitrogen source urea. Ammonium decreased yeast CLS independently of the metabolic process activated during aging, either respiration or fermentation, and induced replication stress inhibiting a proper cell cycle arrest in G0/G1 phase. The present results shade new light on the nutritional equilibrium as a key factor on cell longevity and may contribute for the definition of interventions to promote life span and healthy aging.
Coordinated regulation of sulfur and phospholipid metabolism reflects the importance of methylation in the growth of yeast.

PubMed

Hickman, Mark J; Petti, Allegra A; Ho-Shing, Olivia; Silverman, Sanford J; McIsaac, R Scott; Lee, Traci A; Botstein, David

2011-11-01

A yeast strain lacking Met4p, the primary transcriptional regulator of the sulfur assimilation pathway, cannot synthesize methionine. This apparently simple auxotroph did not grow well in rich media containing excess methionine, forming small colonies on yeast extract/peptone/dextrose plates. Faster-growing large colonies were abundant when overnight cultures were plated, suggesting that spontaneous suppressors of the growth defect arise with high frequency. To identify the suppressor mutations, we used genome-wide single-nucleotide polymorphism and standard genetic analyses. The most common suppressors were loss-of-function mutations in OPI1, encoding a transcriptional repressor of phospholipid metabolism. Using a new system that allows rapid and specific degradation of Met4p, we could study the dynamic expression of all genes following loss of Met4p. Experiments using this system with and without Opi1p showed that Met4 activates and Opi1p represses genes that maintain levels of S-adenosylmethionine (SAM), the substrate for most methyltransferase reactions. Cells lacking Met4p grow normally when either SAM is added to the media or one of the SAM synthetase genes is overexpressed. SAM is used as a methyl donor in three Opi1p-regulated reactions to create the abundant membrane phospholipid, phosphatidylcholine. Our results show that rapidly growing cells require significant methylation, likely for the biosynthesis of phospholipids.
Theoretical Basis for Dynamic Label Propagation in Stationary Metabolic Networks under Step and Periodic Inputs

PubMed Central

Sokol, Serguei; Portais, Jean-Charles

2015-01-01

The dynamics of label propagation in a stationary metabolic network during an isotope labeling experiment can provide highly valuable information on the network topology, metabolic fluxes, and on the size of metabolite pools. However, major issues, both in the experimental set-up and in the accompanying numerical methods currently limit the application of this approach. Here, we propose a method to apply novel types of label inputs, sinusoidal or more generally periodic label inputs, to address both the practical and numerical challenges of dynamic labeling experiments. By considering a simple metabolic system, i.e. a linear, non-reversible pathway of arbitrary length, we develop mathematical descriptions of label propagation for both classical and novel label inputs. Theoretical developments and computer simulations show that the application of rectangular periodic pulses has both numerical and practical advantages over other approaches. We applied the strategy to estimate fluxes in a simulated experiment performed on a complex metabolic network (the central carbon metabolism of Escherichia coli), to further demonstrate its value in conditions which are close to those in real experiments. This study provides a theoretical basis for the rational interpretation of label propagation curves in real experiments, and will help identify the strengths, pitfalls and limitations of such experiments. The cases described here can also be used as test cases for more general numerical methods aimed at identifying network topology, analyzing metabolic fluxes or measuring concentrations of metabolites. PMID:26641860
A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

PubMed

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.
Global Metabolic Responses to Salt Stress in Fifteen Species

PubMed Central

Pollak, Georg R.; Kuehne, Andreas; Sauer, Uwe

2016-01-01

Cells constantly adapt to unpredictably changing extracellular solute concentrations. A cornerstone of the cellular osmotic stress response is the metabolic supply of energy and building blocks to mount appropriate defenses. Yet, the extent to which osmotic stress impinges on the metabolic network remains largely unknown. Moreover, it is mostly unclear which, if any, of the metabolic responses to osmotic stress are conserved among diverse organisms or confined to particular groups of species. Here we investigate the global metabolic responses of twelve bacteria, two yeasts and two human cell lines exposed to sustained hyperosmotic salt stress by measuring semiquantitative levels of hundreds of cellular metabolites using nontargeted metabolomics. Beyond the accumulation of osmoprotectants, we observed significant changes of numerous metabolites in all species. Global metabolic responses were predominantly species-specific, yet individual metabolites were characteristically affected depending on species’ taxonomy, natural habitat, envelope structure or salt tolerance. Exploiting the breadth of our dataset, the correlation of individual metabolite response magnitudes across all species implicated lower glycolysis, tricarboxylic acid cycle, branched-chain amino acid metabolism and heme biosynthesis to be generally important for salt tolerance. Thus, our findings place the global metabolic salt stress response into a phylogenetic context and provide insights into the cellular phenotype associated with salt tolerance. PMID:26848578
Cellular and molecular effects of yeast probiotics on cancer.

PubMed

Saber, Amir; Alipour, Beitollah; Faghfoori, Zeinab; Yari Khosroushahi, Ahmad

2017-02-01

The cancer is one of the main causes of human deaths worldwide. The exact mechanisms of initiation and progression of malignancies are not clear yet, but there is a common agreement about the role of colonic microbiota in the etiology of different cancers. Probiotics have been examined for their anti-cancer effects, and different mechanisms have been suggested about their antitumor functions. Nonpathogenic yeasts, as members of probiotics family, can be effective on gut microbiota dysbiosis. Generally safe yeasts have shown so many beneficial effects on human health. Probiotic yeasts influence physiology, metabolism, and immune homeostasis in the colon and contribute to cancer treatment due to possessing anti-inflammatory, anti-proliferative and anti-cancer properties. This study reviews some of the health-beneficial effects of probiotic yeasts and their biological substances like folic acid and β-glucan on cancer and focuses on the possible cellular and molecular mechanisms of probiotic yeasts such as influencing pathogenic bacteria, inactivation of carcinogenic compounds, especially those derived from food, improvement of intestinal barrier function, modulation of immune responses, antitoxic function, apoptosis, and anti-proliferative effects.
Dietary glucose regulates yeast consumption in adult Drosophila males.

PubMed

Lebreton, Sébastien; Witzgall, Peter; Olsson, Marie; Becher, Paul G

2014-01-01

The adjustment of feeding behavior in response to hunger and satiety contributes to homeostatic regulation in animals. The fruit fly Drosophila melanogaster feeds on yeasts growing on overripe fruit, providing nutrients required for adult survival, reproduction and larval growth. Here, we present data on how the nutritional value of food affects subsequent yeast consumption in Drosophila adult males. After a period of starvation, flies showed intensive yeast consumption. In comparison, flies stopped feeding after having access to a nutritive cornmeal diet. Interestingly, dietary glucose was equally efficient as the complex cornmeal diet. In contrast, flies fed with sucralose, a non-metabolizable sweetener, behaved as if they were starved. The adipokinetic hormone and insulin-like peptides regulate metabolic processes in insects. We did not find any effect of the adipokinetic hormone pathway on this modulation. Instead, the insulin pathway was involved in these changes. Flies lacking the insulin receptor (InR) did not respond to nutrient deprivation by increasing yeast consumption. Together these results show the importance of insulin in the regulation of yeast consumption in response to starvation in adult D. melanogaster males.
Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

PubMed Central

Ye, Ping; Peyser, Brian D; Spencer, Forrest A; Bader, Joel S

2005-01-01

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two
Metabolic network failures in Alzheimer’s disease: A biochemical road map

PubMed Central

Toledo, Jon B.; Arnold, Matthias; Kastenmüuller, Gabi; Chang, Rui; Baillie, Rebecca A.; Han, Xianlin; Thambisetty, Madhav; Tenenbaum, Jessica D.; Suhre, Karsten; Thompson, J. Will; St. John-Williams, Lisa; MahmoudianDehkordi, Siamak; Rotroff, Daniel M.; Jack, John R.; Motsinger-Reif, Alison; Risacher, Shannon L.; Blach, Colette; Lucas, Joseph E.; Massaro, Tyler; Louie, Gregory; Zhu, Hongjie; Dallmann, Guido; Klavins, Kristaps; Koal, Therese; Kim, Sungeun; Nho, Kwangsik; Shen, Li; Casanova, Ramon; Varma, Sudhir; Legido-Quigley, Cristina; Moseley, M. Arthur; Zhu, Kuixi; Henrion, Marc Y. R.; van der Lee, Sven J.; Harms, Amy C.; Demirkan, Ayse; Hankemeier, Thomas; van Duijn, Cornelia M.; Trojanowski, John Q.; Shaw, Leslie M.; Saykin, Andrew J.; Weiner, Michael W.; Doraiswamy, P. Murali; Kaddurah-Daouk, Rima

2018-01-01

Introduction The Alzheimer’s Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer’s disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods Fasting serum samples from the Alzheimer’s Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. PMID:28341160
Type 1 diabetes in children is not a predisposing factor for oral yeast colonization.

PubMed

Costa, Ana L; Silva, Branca M A; Soares, Rui; Mota, Diana; Alves, Vera; Mirante, Alice; Ramos, João C; Maló de Abreu, João; Santos-Rosa, Manuel; Caramelo, Francisco; Gonçalves, Teresa

2017-06-01

Type 1 diabetes mellitus (T1D) is considered a risk factor associated with oral yeast infections. The aim of this study was to evaluate the yeast oral carriage (in saliva and mucosal surface) of children with T1D and potential relation with host factors, particularly the subset of CD4+ T cells. Yeasts were quantified and identified in stimulated saliva and in cheek mucosal swabs of 133 diabetic T1D and 72 healthy control subjects. Salivary lymphocytes were quantified using flow cytometry. The presence of yeasts in the oral cavity (60% of total patients) was not affected by diabetes, metabolic control, duration of the disease, salivary flow rate or saliva buffer capacity, by age, sex, place of residence, number of daily meals, consumption of sweets or frequency of tooth brushing. Candida albicans was the most prevalent yeast species, but a higher number of yeast species was isolated in nondiabetics. T1D children with HbA1c ≤ 7.5 (metabolically controlled) presented higher number of CD4+ T salivary subsets when compared with the other groups of children (non-diabetic and nonmetabolically controlled) and also presented the highest number of individuals without oral yeast colonization. In conclusion, T1D does not predisposes for increased oral yeast colonization and a higher number of salivary CD4+T cells seems to result in the absence of oral colonization by yeasts. © The Author 2016. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Robust Metabolic Responses to Varied Carbon Sources in Natural and Laboratory Strains of Saccharomyces cerevisiae

PubMed Central

Van Voorhies, Wayne A.

2012-01-01

Understanding factors that regulate the metabolism and growth of an organism is of fundamental biologic interest. This study compared the influence of two different carbon substrates, dextrose and galactose, on the metabolic and growth rates of the yeast Saccharomyces cerevisiae. Yeast metabolic and growth rates varied widely depending on the metabolic substrate supplied. The metabolic and growth rates of a yeast strain maintained under long-term laboratory conditions was compared to strain isolated from natural condition when grown on different substrates. Previous studies had determined that there are numerous genetic differences between these two strains. However, the overall metabolic and growth rates of a wild isolate of yeast was very similar to that of a strain that had been maintained under laboratory conditions for many decades. This indicates that, at in least this case, metabolism and growth appear to be well buffered against genetic differences. Metabolic rate and cell number did not co-vary in a simple linear manner. When grown in either dextrose or galactose, both strains showed a growth pattern in which the number of cells continued to increase well after the metabolic rate began a sharp decline. Previous studied have reported that O2 consumption in S. cerevisiae grown in reduced dextrose levels were elevated compared to higher levels. Low dextrose levels have been proposed to induce caloric restriction and increase life span in yeast. However, there was no evidence that reduced levels of dextrose increased metabolic rates, measured by either O2 consumption or CO2 production, in the strains used in this study. PMID:22253874
Yeast “Make-Accumulate-Consume” Life Strategy Evolved as a Multi-Step Process That Predates the Whole Genome Duplication

PubMed Central

Hagman, Arne; Säll, Torbjörn; Compagno, Concetta; Piskur, Jure

2013-01-01

When fruits ripen, microbial communities start a fierce competition for the freely available fruit sugars. Three yeast lineages, including baker’s yeast Saccharomyces cerevisiae, have independently developed the metabolic activity to convert simple sugars into ethanol even under fully aerobic conditions. This fermentation capacity, named Crabtree effect, reduces the cell-biomass production but provides in nature a tool to out-compete other microorganisms. Here, we analyzed over forty Saccharomycetaceae yeasts, covering over 200 million years of the evolutionary history, for their carbon metabolism. The experiments were done under strictly controlled and uniform conditions, which has not been done before. We show that the origin of Crabtree effect in Saccharomycetaceae predates the whole genome duplication and became a settled metabolic trait after the split of the S. cerevisiae and Kluyveromyces lineages, and coincided with the origin of modern fruit bearing plants. Our results suggest that ethanol fermentation evolved progressively, involving several successive molecular events that have gradually remodeled the yeast carbon metabolism. While some of the final evolutionary events, like gene duplications of glucose transporters and glycolytic enzymes, have been deduced, the earliest molecular events initiating Crabtree effect are still to be determined. PMID:23869229
Herbicide glufosinate inhibits yeast growth and extends longevity during wine fermentation.

PubMed

Vallejo, Beatriz; Picazo, Cecilia; Orozco, Helena; Matallana, Emilia; Aranda, Agustín

2017-09-29

Glufosinate ammonium (GA) is a widely used herbicide that inhibits glutamine synthetase. This inhibition leads to internal amino acid starvation which, in turn, causes the activation of different nutrient sensing pathways. GA also inhibits the enzyme of the yeast Saccharomyces cerevisiae in such a way that, although it is not used as a fungicide, it may alter yeast performance in industrial processes like winemaking. We describe herein how GA indeed inhibits the yeast growth of a wine strain during the fermentation of grape juice. In turn, GA extends longevity in a variety of growth media. The biochemical analysis indicates that GA partially inhibits the nutrient sensing TORC1 pathway, which may explain these phenotypes. The GCN2 kinase mutant is hypersensitive to GA. Hence the control of translation and amino acid biosynthesis is required to also deal with the damaging effects of this pesticide. A global metabolomics analysis under winemaking conditions indicated that an increase in amino acid and in polyamines occurred. In conclusion, GA affects many different biochemical processes during winemaking, which provides us with some insights into both the effect of this herbicide on yeast physiology and into the relevance of the metabolic step for connecting nitrogen and carbon metabolism.
Kinetics of growth and sugar consumption in yeasts.

PubMed

van Dijken, J P; Weusthuis, R A; Pronk, J T

1993-01-01

An overview is presented of the steady- and transient state kinetics of growth and formation of metabolic byproducts in yeasts. Saccharomyces cerevisiae is strongly inclined to perform alcoholic fermentation. Even under fully aerobic conditions, ethanol is produced by this yeast when sugars are present in excess. This so-called 'Crabtree effect' probably results from a multiplicity of factors, including the mode of sugar transport and the regulation of enzyme activities involved in respiration and alcoholic fermentation. The Crabtree effect in S. cerevisiae is not caused by an intrinsic inability to adjust its respiratory activity to high glycolytic fluxes. Under certain cultivation conditions, for example during growth in the presence of weak organic acids, very high respiration rates can be achieved by this yeast. S. cerevisiae is an exceptional yeast since, in contrast to most other species that are able to perform alcoholic fermentation, it can grow under strictly anaerobic conditions. 'Non-Saccharomyces' yeasts require a growth-limiting supply of oxygen (i.e. oxygen-limited growth conditions) to trigger alcoholic fermentation. However, complete absence of oxygen results in cessation of growth and therefore, ultimately, of alcoholic fermentation. Since it is very difficult to reproducibly achieve the right oxygen dosage in large-scale fermentations, non-Saccharomyces yeasts are therefore not suitable for large-scale alcoholic fermentation of sugar-containing waste streams. In these yeasts, alcoholic fermentation is also dependent on the type of sugar. For example, the facultatively fermentative yeast Candida utilis does not ferment maltose, not even under oxygen-limited growth conditions, although this disaccharide supports rapid oxidative growth.
ocsESTdb: a database of oil crop seed EST sequences for comparative analysis and investigation of a global metabolic network and oil accumulation metabolism.

PubMed

Ke, Tao; Yu, Jingyin; Dong, Caihua; Mao, Han; Hua, Wei; Liu, Shengyi

2015-01-21

Oil crop seeds are important sources of fatty acids (FAs) for human and animal nutrition. Despite their importance, there is a lack of an essential bioinformatics resource on gene transcription of oil crops from a comparative perspective. In this study, we developed ocsESTdb, the first database of expressed sequence tag (EST) information on seeds of four large-scale oil crops with an emphasis on global metabolic networks and oil accumulation metabolism that target the involved unigenes. A total of 248,522 ESTs and 106,835 unigenes were collected from the cDNA libraries of rapeseed (Brassica napus), soybean (Glycine max), sesame (Sesamum indicum) and peanut (Arachis hypogaea). These unigenes were annotated by a sequence similarity search against databases including TAIR, NR protein database, Gene Ontology, COG, Swiss-Prot, TrEMBL and Kyoto Encyclopedia of Genes and Genomes (KEGG). Five genome-scale metabolic networks that contain different numbers of metabolites and gene-enzyme reaction-association entries were analysed and constructed using Cytoscape and yEd programs. Details of unigene entries, deduced amino acid sequences and putative annotation are available from our database to browse, search and download. Intuitive and graphical representations of EST/unigene sequences, functional annotations, metabolic pathways and metabolic networks are also available. ocsESTdb will be updated regularly and can be freely accessed at http://ocri-genomics.org/ocsESTdb/ . ocsESTdb may serve as a valuable and unique resource for comparative analysis of acyl lipid synthesis and metabolism in oilseed plants. It also may provide vital insights into improving oil content in seeds of oil crop species by transcriptional reconstruction of the metabolic network.
Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

PubMed

Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-03-01

"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

Non-Saccharomyces Yeasts Nitrogen Source Preferences: Impact on Sequential Fermentation and Wine Volatile Compounds Profile

PubMed Central

Gobert, Antoine; Tourdot-Maréchal, Raphaëlle; Morge, Christophe; Sparrow, Céline; Liu, Youzhong; Quintanilla-Casas, Beatriz; Vichi, Stefania; Alexandre, Hervé

2017-01-01

Nitrogen sources in the must are important for yeast metabolism, growth, and performance, and wine volatile compounds profile. Yeast assimilable nitrogen (YAN) deficiencies in grape must are one of the main causes of stuck and sluggish fermentation. The nitrogen requirement of Saccharomyces cerevisiae metabolism has been described in detail. However, the YAN preferences of non-Saccharomyces yeasts remain unknown despite their increasingly widespread use in winemaking. Furthermore, the impact of nitrogen consumption by non-Saccharomyces yeasts on YAN availability, alcoholic performance and volatile compounds production by S. cerevisiae in sequential fermentation has been little studied. With a view to improving the use of non-Saccharomyces yeasts in winemaking, we studied the use of amino acids and ammonium by three strains of non-Saccharomyces yeasts (Starmerella bacillaris, Metschnikowia pulcherrima, and Pichia membranifaciens) in grape juice. We first determined which nitrogen sources were preferentially used by these yeasts in pure cultures at 28 and 20°C (because few data are available). We then carried out sequential fermentations at 20°C with S. cerevisiae, to assess the impact of the non-Saccharomyces yeasts on the availability of assimilable nitrogen for S. cerevisiae. Finally, 22 volatile compounds were quantified in sequential fermentation and their levels compared with those in pure cultures of S. cerevisiae. We report here, for the first time, that non-Saccharomyces yeasts have specific amino-acid consumption profiles. Histidine, methionine, threonine, and tyrosine were not consumed by S. bacillaris, aspartic acid was assimilated very slowly by M. pulcherrima, and glutamine was not assimilated by P. membranifaciens. By contrast, cysteine appeared to be a preferred nitrogen source for all non-Saccharomyces yeasts. In sequential fermentation, these specific profiles of amino-acid consumption by non-Saccharomyces yeasts may account for some of the
Non-Saccharomyces Yeasts Nitrogen Source Preferences: Impact on Sequential Fermentation and Wine Volatile Compounds Profile.

PubMed

Gobert, Antoine; Tourdot-Maréchal, Raphaëlle; Morge, Christophe; Sparrow, Céline; Liu, Youzhong; Quintanilla-Casas, Beatriz; Vichi, Stefania; Alexandre, Hervé

2017-01-01

Nitrogen sources in the must are important for yeast metabolism, growth, and performance, and wine volatile compounds profile. Yeast assimilable nitrogen (YAN) deficiencies in grape must are one of the main causes of stuck and sluggish fermentation. The nitrogen requirement of Saccharomyces cerevisiae metabolism has been described in detail. However, the YAN preferences of non- Saccharomyces yeasts remain unknown despite their increasingly widespread use in winemaking. Furthermore, the impact of nitrogen consumption by non- Saccharomyces yeasts on YAN availability, alcoholic performance and volatile compounds production by S. cerevisiae in sequential fermentation has been little studied. With a view to improving the use of non- Saccharomyces yeasts in winemaking, we studied the use of amino acids and ammonium by three strains of non- Saccharomyces yeasts ( Starmerella bacillaris, Metschnikowia pulcherrima , and Pichia membranifaciens ) in grape juice. We first determined which nitrogen sources were preferentially used by these yeasts in pure cultures at 28 and 20°C (because few data are available). We then carried out sequential fermentations at 20°C with S. cerevisiae , to assess the impact of the non- Saccharomyces yeasts on the availability of assimilable nitrogen for S. cerevisiae . Finally, 22 volatile compounds were quantified in sequential fermentation and their levels compared with those in pure cultures of S. cerevisiae . We report here, for the first time, that non- Saccharomyces yeasts have specific amino-acid consumption profiles. Histidine, methionine, threonine, and tyrosine were not consumed by S. bacillaris , aspartic acid was assimilated very slowly by M. pulcherrima , and glutamine was not assimilated by P. membranifaciens . By contrast, cysteine appeared to be a preferred nitrogen source for all non- Saccharomyces yeasts. In sequential fermentation, these specific profiles of amino-acid consumption by non- Saccharomyces yeasts may account for
Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.

PubMed

Al-Anzi, Bader; Arpp, Patrick; Gerges, Sherif; Ormerod, Christopher; Olsman, Noah; Zinn, Kai

2015-05-01

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.
Inferring the effective TOR-dependent network: a computational study in yeast

PubMed Central

2013-01-01

Background Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR. Results In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies. Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition. Conclusions Our approach, unlike experimental methods, is not limited to specific aspects of cellular response
Connexin 43-Mediated Astroglial Metabolic Networks Contribute to the Regulation of the Sleep-Wake Cycle.

PubMed

Clasadonte, Jerome; Scemes, Eliana; Wang, Zhongya; Boison, Detlev; Haydon, Philip G

2017-09-13

Astrocytes produce and supply metabolic substrates to neurons through gap junction-mediated astroglial networks. However, the role of astroglial metabolic networks in behavior is unclear. Here, we demonstrate that perturbation of astroglial networks impairs the sleep-wake cycle. Using a conditional Cre-Lox system in mice, we show that knockout of the gap junction subunit connexin 43 in astrocytes throughout the brain causes excessive sleepiness and fragmented wakefulness during the nocturnal active phase. This astrocyte-specific genetic manipulation silenced the wake-promoting orexin neurons located in the lateral hypothalamic area (LHA) by impairing glucose and lactate trafficking through astrocytic networks. This global wakefulness instability was mimicked with viral delivery of Cre recombinase to astrocytes in the LHA and rescued by in vivo injections of lactate. Our findings propose a novel regulatory mechanism critical for maintaining normal daily cycle of wakefulness and involving astrocyte-neuron metabolic interactions. Copyright © 2017 Elsevier Inc. All rights reserved.
Bidirectionality and compartmentation of metabolic fluxes are revealed in the dynamics of isotopomer networks.

PubMed

Schryer, David W; Peterson, Pearu; Paalme, Toomas; Vendelin, Marko

2009-04-17

Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.
An efficient graph theory based method to identify every minimal reaction set in a metabolic network

PubMed Central

2014-01-01

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal
Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network1[OPEN

PubMed Central

Kim, Taehyong; Dreher, Kate; Nilo-Poyanco, Ricardo; Lee, Insuk; Fiehn, Oliver; Lange, Bernd Markus; Nikolau, Basil J.; Sumner, Lloyd; Welti, Ruth; Wurtele, Eve S.; Rhee, Seung Y.

2015-01-01

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes. PMID:25670818
Engineering strategy of yeast metabolism for higher alcohol production.

PubMed

Matsuda, Fumio; Furusawa, Chikara; Kondo, Takashi; Ishii, Jun; Shimizu, Hiroshi; Kondo, Akihiko

2011-09-08

While Saccharomyces cerevisiae is a promising host for cost-effective biorefinary processes due to its tolerance to various stresses during fermentation, the metabolically engineered S. cerevisiae strains exhibited rather limited production of higher alcohols than that of Escherichia coli. Since the structure of the central metabolism of S. cerevisiae is distinct from that of E. coli, there might be a problem in the structure of the central metabolism of S. cerevisiae. In this study, the potential production of higher alcohols by S. cerevisiae is compared to that of E. coli by employing metabolic simulation techniques. Based on the simulation results, novel metabolic engineering strategies for improving higher alcohol production by S. cerevisiae were investigated by in silico modifications of the metabolic models of S. cerevisiae. The metabolic simulations confirmed that the high production of butanols and propanols by the metabolically engineered E. coli strains is derived from the flexible behavior of their central metabolism. Reducing this flexibility by gene deletion is an effective strategy to restrict the metabolic states for producing target alcohols. In contrast, the lower yield using S. cerevisiae originates from the structurally limited flexibility of its central metabolism in which gene deletions severely reduced cell growth. The metabolic simulation demonstrated that the poor productivity of S. cerevisiae was improved by the introduction of E. coli genes to compensate the structural difference. This suggested that gene supplementation is a promising strategy for the metabolic engineering of S. cerevisiae to produce higher alcohols which should be the next challenge for the synthetic bioengineering of S. cerevisiae for the efficient production of higher alcohols.
Improved sake metabolic profile during fermentation due to increased mitochondrial pyruvate dissimilation.

PubMed

Agrimi, Gennaro; Mena, Maria C; Izumi, Kazuki; Pisano, Isabella; Germinario, Lucrezia; Fukuzaki, Hisashi; Palmieri, Luigi; Blank, Lars M; Kitagaki, Hiroshi

2014-03-01

Although the decrease in pyruvate secretion by brewer's yeasts during fermentation has long been desired in the alcohol beverage industry, rather little is known about the regulation of pyruvate accumulation. In former studies, we developed a pyruvate under-secreting sake yeast by isolating a strain (TCR7) tolerant to ethyl α-transcyanocinnamate, an inhibitor of pyruvate transport into mitochondria. To obtain insights into pyruvate metabolism, in this study, we investigated the mitochondrial activity of TCR7 by oxigraphy and (13) C-metabolic flux analysis during aerobic growth. While mitochondrial pyruvate oxidation was higher, glycerol production was decreased in TCR7 compared with the reference. These results indicate that mitochondrial activity is elevated in the TCR7 strain with the consequence of decreased pyruvate accumulation. Surprisingly, mitochondrial activity is much higher in the sake yeast compared with CEN.PK 113-7D, the reference strain in metabolic engineering. When shifted from aerobic to anaerobic conditions, sake yeast retains a branched mitochondrial structure for a longer time than laboratory strains. The regulation of mitochondrial activity can become a completely novel approach to manipulate the metabolic profile during fermentation of brewer's yeasts. © 2013 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.
THE ROLE FUNGI AND YEAST IN MONITORED NATURAL ATTENUATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brigmon, R.; Abe, M.; Johnson, B.

Fungi and yeast have been characterized as important components in the bioremediation of organic contaminants in soil and water including polyaromatic hydrocarbons (PAHs); however, research into their ability to metabolize these compounds in extreme environments has been limited. In this work forty-three fungi and yeasts were isolated from a PAH-contaminated sludge waste lagoon in Poland. The lagoon was part of a monitored natural attenuation (MNA) study where natural reduction of PAHs and associated toxicity over time in non-disturbed areas of the sludge lagoon indicated MNA activity. The microorganisms were initially isolated on minimal medium containing naphthalene as the sole carbonmore » and energy source. Fungal isolates were then maintained on MEA and identified based on microscopic examination and BIOLOG{reg_sign}. The analysis identified several of the fungal isolates as belonging to the genera Penicillium, Paecilomyces, Aspergillus, and Eupenicillium. Yeasts included Candida parapsilosis and C. fluvialitis. Further microbial characterization revealed that several isolates were capable of rowing on acidified media of pH 4, 3, and 2.5. Over twenty percent of the fungi demonstrated growth as low as pH 2.5. Of the 43 isolates examined, 24 isolates exhibited growth at 5 C. Nine of the fungal isolates exhibiting growth at 5 C were then examined for metabolic activity using a respirometer testing metabolic activity at pH 3. Microcosm studies confirmed the growth of the fungi on PAH contaminated sediment as the sole carbon and energy source with elevated metabolic rates indicating evidence of MNA. Our findings suggest that many of the Poland fungal isolates may be of value in the bioremediation processes in acidic waste sites in northern climates typical of Northern Europe.« less
Mass Conservation and Inference of Metabolic Networks from High-Throughput Mass Spectrometry Data

PubMed Central

Bandaru, Pradeep; Bansal, Mukesh

2011-01-01

Abstract We present a step towards the metabolome-wide computational inference of cellular metabolic reaction networks from metabolic profiling data, such as mass spectrometry. The reconstruction is based on identification of irreducible statistical interactions among the metabolite activities using the ARACNE reverse-engineering algorithm and on constraining possible metabolic transformations to satisfy the conservation of mass. The resulting algorithms are validated on synthetic data from an abridged computational model of Escherichia coli metabolism. Precision rates upwards of 50% are routinely observed for identification of full metabolic reactions, and recalls upwards of 20% are also seen. PMID:21314454
Functional analysis of the global repressor Tup1 for maltose metabolism in Saccharomyces cerevisiae: different roles of the functional domains.

PubMed

Lin, Xue; Yu, Ai-Qun; Zhang, Cui-Ying; Pi, Li; Bai, Xiao-Wen; Xiao, Dong-Guang

2017-11-09

Tup1 is a general transcriptional repressor of diverse gene families coordinately controlled by glucose repression, mating type, and other mechanisms in Saccharomyces cerevisiae. Several functional domains of Tup1 have been identified, each of which has differing effects on transcriptional repression. In this study, we aim to investigate the role of Tup1 and its domains in maltose metabolism of industrial baker's yeast. To this end, a battery of in-frame truncations in the TUP1 gene coding region were performed in the industrial baker's yeasts with different genetic background, and the maltose metabolism, leavening ability, MAL gene expression levels, and growth characteristics were investigated. The results suggest that the TUP1 gene is essential to maltose metabolism in industrial baker's yeast. Importantly, different domains of Tup1 play different roles in glucose repression and maltose metabolism of industrial baker's yeast cells. The Ssn6 interaction, N-terminal repression and C-terminal repression domains might play roles in the regulation of MAL transcription by Tup1 for maltose metabolism of baker's yeast. The WD region lacking the first repeat could influence the regulation of maltose metabolism directly, rather than indirectly through glucose repression. These findings lay a foundation for the optimization of industrial baker's yeast strains for accelerated maltose metabolism and facilitate future research on glucose repression in other sugar metabolism.
Metabolic gene clusters encoding the enzymes of two branches of the 3-oxoadipate pathway in the pathogenic yeast Candida albicans.

PubMed

Gérecová, Gabriela; Neboháčová, Martina; Zeman, Igor; Pryszcz, Leszek P; Tomáška, Ľubomír; Gabaldón, Toni; Nosek, Jozef

2015-05-01

The pathogenic yeast Candida albicans utilizes hydroxyderivatives of benzene via the catechol and hydroxyhydroquinone branches of the 3-oxoadipate pathway. The genetic basis and evolutionary origin of this catabolic pathway in yeasts are unknown. In this study, we identified C. albicans genes encoding the enzymes involved in the degradation of hydroxybenzenes. We found that the genes coding for core components of the 3-oxoadipate pathway are arranged into two metabolic gene clusters. Our results demonstrate that C. albicans cells cultivated in media containing hydroxybenzene substrates highly induce the transcription of these genes as well as the corresponding enzymatic activities. We also found that C. albicans cells assimilating hydroxybenzenes cope with the oxidative stress by upregulation of cellular antioxidant systems such as alternative oxidase and catalase. Moreover, we investigated the evolution of the enzymes encoded by these clusters and found that most of them share a particularly sparse phylogenetic distribution among Saccharomycotina, which is likely to have been caused by extensive gene loss. We exploited this fact to find co-evolving proteins that are suitable candidates for the missing enzymes of the pathway. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models.

PubMed

Bakker, Barbara M; van Eunen, Karen; Jeneson, Jeroen A L; van Riel, Natal A W; Bruggeman, Frank J; Teusink, Bas

2010-10-01

Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these 'lessons from glycolysis', we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.
Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis

PubMed Central

2018-01-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that ‘leftover’ proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module
Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism

PubMed Central

Zhu, Yanping; Cameron, Elizabeth; Pudlo, Nicholas A.; Porter, Nathan T.; Urs, Karthik; Thompson, Andrew J.; Cartmell, Alan; Rogowski, Artur; Hamilton, Brian S.; Chen, Rui; Tolbert, Thomas J.; Piens, Kathleen; Bracke, Debby; Vervecken, Wouter; Hakki, Zalihe; Speciale, Gaetano; Munōz-Munōz, Jose L.; Day, Andrew; Peña, Maria J.; McLean, Richard; Suits, Michael D.; Boraston, Alisdair B.; Atherly, Todd; Ziemer, Cherie J.; Williams, Spencer J.; Davies, Gideon J.; Abbott, D. Wade; Martens, Eric C.; Gilbert, Harry J.

2016-01-01

Yeasts, which have been a component of the human diet for at least 7000 years, possess an elaborate cell wall α-mannan. The influence of yeast mannan on the ecology of the human microbiota is unknown. Here we show that yeast α-mannan is a viable food source for Bacteroides thetaiotaomicron (Bt), a dominant member of the microbiota. Detailed biochemical analysis and targeted gene disruption studies support a model whereby limited cleavage of α-mannan on the surface generates large oligosaccharides that are subsequently depolymerized to mannose by the action of periplasmic enzymes. Co-culturing studies showed that metabolism of yeast mannan by Bt presents a ‘selfish’ model for the catabolism of this recalcitrant polysaccharide. This report shows how a cohort of highly successful members of the microbiota has evolved to consume sterically-restricted yeast glycans, an adaptation that may reflect the incorporation of eukaryotic microorganisms into the human diet. PMID:25567280
Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism.

PubMed

Cuskin, Fiona; Lowe, Elisabeth C; Temple, Max J; Zhu, Yanping; Cameron, Elizabeth; Pudlo, Nicholas A; Porter, Nathan T; Urs, Karthik; Thompson, Andrew J; Cartmell, Alan; Rogowski, Artur; Hamilton, Brian S; Chen, Rui; Tolbert, Thomas J; Piens, Kathleen; Bracke, Debby; Vervecken, Wouter; Hakki, Zalihe; Speciale, Gaetano; Munōz-Munōz, Jose L; Day, Andrew; Peña, Maria J; McLean, Richard; Suits, Michael D; Boraston, Alisdair B; Atherly, Todd; Ziemer, Cherie J; Williams, Spencer J; Davies, Gideon J; Abbott, D Wade; Martens, Eric C; Gilbert, Harry J

2015-01-08

Yeasts, which have been a component of the human diet for at least 7,000 years, possess an elaborate cell wall α-mannan. The influence of yeast mannan on the ecology of the human microbiota is unknown. Here we show that yeast α-mannan is a viable food source for the Gram-negative bacterium Bacteroides thetaiotaomicron, a dominant member of the microbiota. Detailed biochemical analysis and targeted gene disruption studies support a model whereby limited cleavage of α-mannan on the surface generates large oligosaccharides that are subsequently depolymerized to mannose by the action of periplasmic enzymes. Co-culturing studies showed that metabolism of yeast mannan by B. thetaiotaomicron presents a 'selfish' model for the catabolism of this difficult to breakdown polysaccharide. Genomic comparison with B. thetaiotaomicron in conjunction with cell culture studies show that a cohort of highly successful members of the microbiota has evolved to consume sterically-restricted yeast glycans, an adaptation that may reflect the incorporation of eukaryotic microorganisms into the human diet.
CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Noise effect in metabolic networks

NASA Astrophysics Data System (ADS)

Li, Zheng-Yan; Xie, Zheng-Wei; Chen, Tong; Ouyang, Qi

2009-12-01

Constraint-based models such as flux balance analysis (FBA) are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term 'noise', and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.
Phenotypic Diagnosis of Lineage and Differentiation During Sake Yeast Breeding

PubMed Central

Ohnuki, Shinsuke; Okada, Hiroki; Friedrich, Anne; Kanno, Yoichiro; Goshima, Tetsuya; Hasuda, Hirokazu; Inahashi, Masaaki; Okazaki, Naoto; Tamura, Hiroyasu; Nakamura, Ryo; Hirata, Dai; Fukuda, Hisashi; Shimoi, Hitoshi; Kitamoto, Katsuhiko; Watanabe, Daisuke; Schacherer, Joseph; Akao, Takeshi; Ohya, Yoshikazu

2017-01-01

Sake yeast was developed exclusively in Japan. Its diversification during breeding remains largely uncharacterized. To evaluate the breeding processes of the sake lineage, we thoroughly investigated the phenotypes and differentiation of 27 sake yeast strains using high-dimensional, single-cell, morphological phenotyping. Although the genetic diversity of the sake yeast lineage is relatively low, its morphological diversity has expanded substantially compared to that of the Saccharomyces cerevisiae species as a whole. Evaluation of the different types of breeding processes showed that the generation of hybrids (crossbreeding) has more profound effects on cell morphology than the isolation of mutants (mutation breeding). Analysis of phenotypic robustness revealed that some sake yeast strains are more morphologically heterogeneous, possibly due to impairment of cellular network hubs. This study provides a new perspective for studying yeast breeding genetics and micro-organism breeding strategies. PMID:28642365

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