scholarly journals Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolites ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 368
Author(s):  
Huan Jin ◽  
Joshua M. Mitchell ◽  
Hunter N. B. Moseley
Keyword(s):  
Metabolic Network ◽  
Graph Coloring ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Enzyme Commission ◽  
Metabolic Model ◽  
Metabolic Reprogramming ◽  
Automatic Identification ◽  
Flux Analysis ◽  
Database Curation

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

scholarly journals Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases

2020 ◽  
Author(s):  
Huan Jin ◽  
Joshua M. Mitchell ◽  
Hunter N.B. Moseley
Keyword(s):  
Metabolic Network ◽  
Graph Coloring ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Enzyme Commission ◽  
Metabolic Model ◽  
Metabolic Reprogramming ◽  
Automatic Identification ◽  
Flux Analysis ◽  
Database Curation

AbstractMetabolic flux analysis requires both a reliable metabolic model and metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

scholarly journals Metabolic control of nitrogen fixation in rhizobium-legume symbioses

2021 ◽  
Vol 7 (31) ◽  
pp. eabh2433
Author(s):  
Carolin C. M. Schulte ◽  
Khushboo Borah ◽  
Rachel M. Wheatley ◽  
Jason J. Terpolilli ◽  
Gerhard Saalbach ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Tricarboxylic Acid Cycle ◽  
Oxygen Demand ◽  
Metabolic Model ◽  
Redox Balance ◽  
Ammonia Assimilation ◽  
Nodule Formation ◽  
Flux Analysis ◽  
Carbon And Nitrogen ◽  
Genome Scale

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N2 into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD+ ratio than sugars. Modeling and 13C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N2 fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 252-259
Author(s):  
Zhitao Mao ◽  
Hongwu Ma
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Reaction Rates ◽  
Marker Genes ◽  
Published Data ◽  
Flux Analysis ◽  
Prediction Errors ◽  
Balance Analysis

Background:Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results:We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion:Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

scholarly journals Mutant strains of Escherichia coli lacking global regulators, arcA and fis, demonstrate better growth fitness by pathway reprogramming under acetate metabolism

2021 ◽  
Author(s):  
Shikha Jindal ◽  
Mahesh S. Iyer ◽  
Poonam Jyoti ◽  
Shyam Kumar Masakapalli ◽  
K V Venkatesh
Keyword(s):  
Growth Rate ◽  
Metabolic Flux ◽  
Microbial Metabolism ◽  
Metabolic Reprogramming ◽  
Acetate Metabolism ◽  
Flux Analysis ◽  
Wild Type ◽  
Global Regulators ◽  
Acetate Uptake ◽  
Mutant Strains

Global regulatory transcription factors play a significant role in controlling microbial metabolism under genetic and environmental perturbations. A systems-level effect of carbon sources such as acetate on microbial metabolism under disrupted global regulators has not been well established. Acetate is one of the substrates available in a range of nutrient niches such as the mammalian gut and high-fat diet. Therefore, investigating the study on acetate metabolism is highly significant. It is well known that the global regulators arcA and fis regulate acetate uptake genes in E. coli under glucose condition. In this study, we deciphered the growth and flux distribution of E.coli transcription regulatory knockout mutants ΔarcA, Δfis and double deletion mutant, ΔarcAfis under acetate using 13C-Metabolic Flux Analysis which has not been investigated before. We observed that the mutants exhibited an expeditious growth rate (~1.2-1.6 fold) with a proportionate increase in acetate uptake rates compared to the wild-type. 13C-MFA displayed the distinct metabolic reprogramming of intracellular fluxes, which conferred an advantage of faster growth with better carbon usage in all the mutants. Under acetate metabolism, the mutants exhibited higher fluxes in the TCA cycle (~18-90%) and lower gluconeogenesis flux (~15-35%) with the proportional increase in growth rate. This study reveals a novel insight by stating the sub-optimality of the wild-type strain grown under acetate substrate aerobically. These mutant strains efficiently oxidize acetate to acetyl-CoA and therefore are potential candidates that can serve as a precursor for the biosynthesis of isoprenoids, biofuels, vitamins and various pharmaceutical products.

scholarly journals A FACS-Free Purification Method to Study Estrogen Signaling, Organoid Formation, and Metabolic Reprogramming in Mammary Epithelial Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Aurélie Lacouture ◽  
Cynthia Jobin ◽  
Cindy Weidmann ◽  
Line Berthiaume ◽  
Dominic Bastien ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Metabolic Flux ◽  
Mammary Epithelial Cells ◽  
Aerobic Glycolysis ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Mammary Epithelial ◽  
Metabolic Reprogramming ◽  
Phenotypic Characterization ◽  
Flux Analysis

Few in vitro models are used to study mammary epithelial cells (MECs), and most of these do not express the estrogen receptor α (ERα). Primary MECs can be used to overcome this issue, but methods to purify these cells generally require flow cytometry and fluorescence-activated cell sorting (FACS), which require specialized instruments and expertise. Herein, we present in detail a FACS-free protocol for purification and primary culture of mouse MECs. These MECs remain differentiated for up to six days with >85% luminal epithelial cells in two-dimensional culture. When seeded in Matrigel, they form organoids that recapitulate the mammary gland’s morphology in vivo by developing lumens, contractile cells, and lobular structures. MECs express a functional ERα signaling pathway in both two- and three-dimensional cell culture, as shown at the mRNA and protein levels and by the phenotypic characterization. Extracellular metabolic flux analysis showed that estrogens induce a metabolic switch favoring aerobic glycolysis over mitochondrial respiration in MECs grown in two-dimensions, a phenomenon known as the Warburg effect. We also performed mass spectrometry (MS)-based metabolomics in organoids. Estrogens altered the levels of metabolites from various pathways, including aerobic glycolysis, citric acid cycle, urea cycle, and amino acid metabolism, demonstrating that ERα reprograms cell metabolism in mammary organoids. Overall, we have optimized mouse MEC isolation and purification for two- and three-dimensional cultures. This model represents a valuable tool to study how estrogens modulate mammary gland biology, and particularly how these hormones reprogram metabolism during lactation and breast carcinogenesis.

scholarly journals Red blood cells in hemorrhagic shock: a critical role for glutaminolysis in fueling alanine transamination in rats

2017 ◽  
Vol 1 (17) ◽  
pp. 1296-1305 ◽  
Author(s):  
Julie A. Reisz ◽  
Anne L. Slaughter ◽  
Rachel Culp-Hill ◽  
Ernest E. Moore ◽  
Christopher C. Silliman ◽  
...  
Keyword(s):  
Hemorrhagic Shock ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Metabolic Flux ◽  
Critical Role ◽  
Metabolic Reprogramming ◽  
Flux Analysis ◽  
Sprague Dawley ◽  
Life Years

Abstract Red blood cells (RBCs) are the most abundant host cell in the human body and play a critical role in oxygen transport and systemic metabolic homeostasis. Hypoxic metabolic reprogramming of RBCs in response to high-altitude hypoxia or anaerobic storage in the blood bank has been extensively described. However, little is known about the RBC metabolism following hemorrhagic shock (HS), the most common preventable cause of death in trauma, the global leading cause of total life-years lost. Metabolomics analyses were performed through ultra-high pressure liquid chromatography–mass spectrometry on RBCs from Sprague-Dawley rats undergoing HS (mean arterial pressure [MAP], <30 mm Hg) in comparison with sham rats (MAP, >80 mm Hg). Steady-state measurements were accompanied by metabolic flux analysis upon tracing of in vivo–injected 13C15N-glutamine or inhibition of glutaminolysis using the anticancer drug CB-839. RBC metabolic phenotypes recapitulated the systemic metabolic reprogramming observed in plasma from the same rodent model. Results indicate that shock RBCs rely on glutamine to fuel glutathione (GSH) synthesis and pyruvate transamination, whereas abrogation of glutaminolysis conferred early mortality and exacerbated lactic acidosis and systemic accumulation of succinate, a predictor of mortality in the military and civilian critically ill populations. Glutamine is here identified as an essential amine group donor in HS RBCs, plasma, liver, and lungs, providing additional rationale for the central role glutaminolysis plays in metabolic reprogramming and survival following severe hemorrhage.

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