BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

<p>Abstract</p> <p>Background</p> <p>Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.</p> <p>Description</p> <p>We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.</p> <p>Conclusions</p> <p>BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at <url>http://bigg.ucsd.edu</url>.</p

Metabolite concentrations, fluxes and free energies imply efficient enzyme usage.

In metabolism, available free energy is limited and must be divided across pathway steps to maintain a negative ΔG throughout. For each reaction, ΔG is log proportional both to a concentration ratio (reaction quotient to equilibrium constant) and to a flux ratio (backward to forward flux). Here we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and ΔG for each organism. In glycolysis, we find that free energy is partitioned so as to mitigate unproductive backward fluxes associated with ΔG near zero. Across metabolism, we observe that absolute metabolite concentrations and ΔG are substantially conserved and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site dissociation constant (Km or Ki). The observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing’s syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing’s syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional approaches frequently require hand-crafted speech features and segmentation of speaker turns. In this work, we design end-to-end deep learning methods to recognize each person's affective expression in an audio stream with two speakers, automatically discovering features and time regions relevant to the target speaker's affect. We integrate a local attention mechanism into the end-to-end architecture and compare the performance of three attention implementations -- one mean pooling and two weighted pooling methods. Our results show that the proposed weighted-pooling attention solutions are able to learn to focus on the regions containing target speaker's affective information and successfully extract the individual's valence and arousal intensity. Here we introduce and use a "dyadic affect in multimodal interaction - parent to child" (DAMI-P2C) dataset collected in a study of 34 families, where a parent and a child (3-7 years old) engage in reading storybooks together. In contrast to existing public datasets for affect recognition, each instance for both speakers in the DAMI-P2C dataset is annotated for the perceived affect by three labelers. To encourage more research on the challenging task of multi-speaker affect sensing, we make the annotated DAMI-P2C dataset publicly available, including acoustic features of the dyads' raw audios, affect annotations, and a diverse set of developmental, social, and demographic profiles of each dyad.Comment: Accepted by the 2020 International Conference on Multimodal Interaction (ICMI'20

Synergistic substrate cofeeding stimulates reductive metabolism

Advanced bioproduct synthesis via reductive metabolism requires coordinating carbons, ATP and reducing agents, which are generated with varying efficiencies depending on metabolic pathways. Substrate mixtures with direct access to multiple pathways may optimally satisfy these biosynthetic requirements. However, native regulation favouring preferential use precludes cells from co-metabolizing multiple substrates. Here we explore mixed substrate metabolism and tailor pathway usage to synergistically stimulate carbon reduction. By controlled cofeeding of superior ATP and NADPH generators as ‘dopant’ substrates to cells primarily using inferior substrates, we circumvent catabolite repression and drive synergy in two divergent organisms. Glucose doping in Moorella thermoacetica stimulates CO2 reduction (2.3 g gCDW−1 h−1) into acetate by augmenting ATP synthesis via pyruvate kinase. Gluconate doping in Yarrowia lipolytica accelerates acetate-driven lipogenesis (0.046 g gCDW−1 h−1) by obligatory NADPH synthesis through the pentose cycle. Together, synergistic cofeeding produces CO2-derived lipids with 38% energy yield and demonstrates the potential to convert CO2 into advanced bioproducts. This work advances the systems-level control of metabolic networks and CO2 use, the most pressing and difficult reduction challenge

Integration of metabolomics and fluxomics via nonequilibrium thermodynamics

Metabolism is the process of converting nutrients into usable energy (e.g., ATP) and biomass building blocks (e.g., amino acids, nucleotides, and lipids). The biochemistry of metabolic reactions and the structure of metabolic networks bear impressive resemblance across widely divergent organisms. Like all chemical networks, metabolism must obey the second law of thermodynamics: each pathway step must generate entropy and cost free energy (ΔG). As available free energy is limited, a fundamental challenge is partitioning the requisite free energy loss across pathway steps. For each reaction, ΔG is log-proportional both to a concentration ratio (reaction quotient-to-equilibrium constant) and to a flux ratio (backward-to-forward flux). With 13C isotope labeling, absolute metabolite concentrations and fluxes can be measured in various cell types and organisms including E. coli, yeast, and mammalian cells. Then, the concentration and flux ratios can be integrated via ΔG using the non-equilibrium thermodynamic principle. The integrative analysis yields internally consistent and comprehensive sets of metabolite concentrations and ΔG in each organism. In glycolysis, free energy is partitioned so as to mitigate unproductive backwards fluxes associated with ΔG near zero. Across metabolism, absolute metabolite concentrations and ΔG are substantially conserved such that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site affinity. The observed conservation of metabolite concentrations may reflect an evolutionary drive to simultaneously satisfy thermodynamic constraints and efficiently utilize enzyme active sites. Modulation of metabolic fluxes allows cells to meet their energetic and material demand in various conditions. In pathway upregulation, cells must coordinate the pathway steps as any one reaction may become limiting. A combination of 2H and 13C labeling reveals the extent of reversibility of glycolytic reactions. In E. coli, glycolysis under nitrogen limitation is substantially closer to equilibrium than in nutrient rich condition. Upon N-upshift, glycolysis immediately restores sufficient driving force to increase glucose consumption and growth rate. In mammalian cells cultured in a rich-nutrient condition, upregulation of glycolysis by Ras or Akt oncogene activation is achieved without substantial alteration of reversibility. These observations suggest that, under nutrient limitation, cells sacrifice enzyme efficiency for fast adaptation

Light-Independent Biological Conversion of CO2.

Sevcan Erşan is a postdoctoral researcher at UCLA. Previously, she conducted postdoctoral research at the University of Hohenheim in Germany. She received her PhD in biotechnology from Yeditepe University, Turkey, and her bachelor's and master's degrees in food engineering from Istanbul Technical University, Turkey. She is experienced in waste utilization, bioprocessing technologies, and biological activities associated with phytochemicals. Her current research focuses on natural product chemistry and sustainable biotechnology. Junyoung Park is an assistant professor of Chemical and Biomolecular Engineering and co-director of the Metabolomics Center at UCLA. His research group focuses on systems-level analysis of metabolic networks to elucidate regulatory mechanisms and engineer metabolism. He aims to apply this knowledge to solving energy and environmental problems and curing human diseases such as cancer and diabetes. Before moving to Los Angeles, he conducted postdoctoral research at MIT. He received his bachelor's degrees in mathematics and bioengineering from UC San Diego and a master's and PhD in chemical engineering from Princeton University

RNA Futile Cycling in Model Persisters Derived from MazF Accumulation.

UnlabelledMetabolism plays an important role in the persister phenotype, as evidenced by the number of strategies that perturb metabolism to sabotage this troublesome subpopulation. However, the absence of techniques to isolate high-purity populations of native persisters has precluded direct measurement of persister metabolism. To address this technical challenge, we studied Escherichia coli populations whose growth had been inhibited by the accumulation of the MazF toxin, which catalyzes RNA cleavage, as a model system for persistence. Using chromosomally integrated, orthogonally inducible promoters to express MazF and its antitoxin MazE, bacterial populations that were almost entirely tolerant to fluoroquinolone and β-lactam antibiotics were obtained upon MazF accumulation, and these were subjected to direct metabolic measurements. While MazF model persisters were nonreplicative, they maintained substantial oxygen and glucose consumption. Metabolomic analysis revealed accumulation of all four ribonucleotide monophosphates (NMPs). These results are consistent with a MazF-catalyzed RNA futile cycle, where the energy derived from catabolism is dissipated through continuous transcription and MazF-mediated RNA degradation. When transcription was inhibited, oxygen consumption and glucose uptake decreased, and nucleotide triphosphates (NTPs) and NTP/NMP ratios increased. Interestingly, the MazF-inhibited cells were sensitive to aminoglycosides, and this sensitivity was blocked by inhibition of transcription. Thus, in MazF model persisters, futile cycles of RNA synthesis and degradation result in both significant metabolic demands and aminoglycoside sensitivity.ImportanceMetabolism plays a critical role in controlling each stage of bacterial persistence (shutdown, stasis, and reawakening). In this work, we generated an E.&nbsp;coli strain in which the MazE antitoxin and MazF toxin were artificially and independently inducible, and we used this strain to generate model persisters and study their metabolism. We found that even though growth of the model persisters was inhibited, they remained highly metabolically active. We further uncovered a futile cycle driven by continued transcription and MazF-mediated transcript degradation that dissipated the energy derived from carbon catabolism. Interestingly, the existence of this futile cycle acted as an Achilles' heel for MazF model persisters, rendering them vulnerable to killing by aminoglycosides