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Open Access Publications from the University of California

Scholarly Works (9 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2018)

Global respiratory balance is maintained by photosynthetic organisms, yet the importance of this contribution is not commensurate with our understanding of light-driven metabolic processes. Meanwhile, there has been a increase in the desire to engineer phototrophic microorganisms. Excessive demands by modern society have been depleting nature's resources over the past centuries. Exploring and developing new sustainable resources to counter increasing consumption has therefore been the focus of research efforts in the academic and private sectors. The emphasis has partly been on using phototrophic organisms that fix carbon dioxide by utilizing light energy to produce energy-dense products. Recent efforts to characterize metabolic capabilities of photosynthetic species as well as engineer attractive candidates require a framework for discovery, data analysis and reconfiguring of existing metabolic networks. The systems biology approach of constraint-based reconstruction and analysis coupled with flux balance analysis has a proven record of contextualizing organism specific information and characterizing cellular metabolism. However, a persistent challenge in the modeling of photoautotrophy has been a mechanistic incorporation of light uptake. As the light environment dictates cell physiology, such as growth rate, biomass composition and metabolic pathway usage, constraining photon flux is a prerequisite for biologically accurate results. Here we describe efforts to address this challenge and the resulting insights into photoautotrphic biology. First, a proper accounting of light uptake and shading coupled with a high-quality genome-scale model of the cyanobacteria

Synechococcus elongatus sp. PCC7942 resulted in accurate growth and flux predictions. Additionally,

we concluded despite an incomplete TCA cycle in the organism studied, there was no impact to fitness due to the metabolic network configuration. Next, in an attempt to extend the methodology to eukaryotic microalgae, we generated a genome-scale model of the diatom Phaeodactylum tricornutum. The systems biology perspective elucidated metabolic capabilities in this organism conferred by its unique phylogeny. Applying an improved set of photophysiology constraints, we simulated circadian dynamics and characterized photoprotective mechanisms resulting from the previously elucidated metabolic capabilities; highlighting the importance of the broader metabolic network in dissipating excess light energy. Finally, photophysiology constraints coupled to chlorophyll fluorescence measurements and genome-scale modeling enabled a comparative analysis of light environment acclimation across the phototrophic clades cyanobacteria, green algae and diatoms and quantified the fraction of excess light energy absorbed by the cells and its metabolic fate. Overall, coupling mechanistic constraints on photophysiology with genome-scale modeling accurately characterized the cellular response to the light environment. This work is relevant to understanding species-specific metabolic capabilities and adaptations of interest to biological and bioengineering communities.

    Cover page: Employing Systems Biology for Discovery and Engineering in Phototrophic Microorganisms
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2019)
    There is great interest in engineering photoautotrophic metabolism to generate bioproducts of societal importance. Despite the success in employing genome-scale modeling coupled with flux balance analysis to engineer heterotrophic metabolism, the lack of proper constraints necessary to generate biologically realistic predictions has hindered broad application of this methodology to phototrophic metabolism. Here we describe a methodology for constraining genome-scale models of photoautotrophy in the cyanobacteria Synechococcus elongatus PCC 7942. Experimental photophysiology parameters coupled to genome-scale flux balance analysis resulted in accurate predictions of growth rates and metabolic reaction fluxes at low and high light conditions. Additionally, by constraining photon uptake fluxes, we characterized the metabolic cost of excess excitation energy. The predicted energy fluxes were consistent with known light-adapted phenotypes in cyanobacteria. Finally, we leveraged the modeling framework to characterize existing photoautotrophic and photomixtotrophic engineering strategies for 2,3-butanediol production in S. elongatus. This methodology, applicable to genome-scale modeling of all phototrophic microorganisms, can facilitate the use of flux balance analysis in the engineering of light-driven metabolism.
      Cover page: Predicting the metabolic capabilities of Synechococcus elongatus PCC 7942 adapted to different light regimes.
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2016)
      The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.
        Cover page: Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2022)
        Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.
          Cover page: Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2016)
          Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.
            Cover page: Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2019)
            S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. Despite growing knowledge of S. aureus metabolic capabilities, our understanding of its systems-level responses to different media types remains incomplete. Here, we develop a manually reconstructed genome-scale model (GEM-PRO) of metabolism with 3D protein structures for S. aureus USA300 str. JE2 containing 854 genes, 1,440 reactions, 1,327 metabolites and 673 3-dimensional protein structures. Computations were in 85% agreement with gene essentiality data from random barcode transposon site sequencing (RB-TnSeq) and 68% agreement with experimental physiological data. Comparisons of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metabolic genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; 3) the essentiality of purine and amino acid biosynthesis in synthetic physiological media; and 4) a switch to aerobic fermentation upon exposure to extracellular glucose elucidated as a result of integrating time-course of quantitative exo-metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus' metabolic response to its environment.
              Cover page: A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2017)
              The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.
                Cover page: Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2019)
                Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.
                  Cover page: Cellular responses to reactive oxygen species are predicted from molecular mechanisms
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2019)
                  Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa.
                    Cover page: Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom
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