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Open Access Publications from the University of California

Scholarly Works (46 results)

  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2016)
Genome-scale models require an objective function representing what an organism strives for. A method has been developed to infer this fundamental biological function from data.Please see related Research article: www.dx.doi.org/10.1186/s13059-016-0968-2.
    Cover page: What do cells actually want?Creative Commons 'BY' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2023)

    Thermoplastic polyurethanes (TPUs) are a widely-used synthetic polymer that are involved in the production of many items. Due to its widespread applications, the ensuing industrial production of TPUs has led to a worldwide increase of TPU waste. However, there is currently no available commercialized recycling process for TPU, resulting in TPU waste either being incinerated or accumulating in landfills. These main methods of dealing with TPUs pose environmental and health issues, necessitating a novel approach to safely remove TPU waste. A promising solution is the biodegradation of TPU via microorganisms, however the mechanisms of TPU biodegradation and TPU-degrading microorganisms are not well-known. The focus of this thesis is the screening and characterization of TPU-degrading microorganisms, in particular the Bacillus species, as they are known to have TPU-degrading capabilities. Growth in compost, capability of TPU degradation, and genetic tractability were assessed from over 140 Bacillus species. In this thesis, it was determined that i). most screening approaches in evaluating characteristics of potential host strain candidates were unable to produce consistent data to verify the strain growth phenotypes in terms of growth rates and final cellular densities, and ii). experiments screening growth on TPUs indicate that strains have preferences in degrading certain TPUs, requiring approaches like adaptive laboratory evolution (ALE) to optimize degradation. In addition, characterization of screened candidates will be utilized as starting platform strains for further TPU degradation research and optimization of the degradation pathway. Ultimately, these evolved strains will be utilized as an environmentally-friendly way of removing TPU waste.

      Cover page: Host Strain Selection for Thermoplastic Polyurethane Degradation
      • Thesis
      • Peer Reviewed
      UC San Diego Electronic Theses and Dissertations (2018)

      Antibiotic-resistant Staphylococcus aureus is one of the most common causes of bacterial infections in humans and is responsible for a wide range of infections in healthcare and community settings. Methicillin-resistant S. aureus, in particular, has proven to be resistant to β-lactams and many other classes of antibiotics. The standard for evaluation of antibiotic efficacy involves testing on cation-adjusted Mueller Hinton broth or CA-MHB. However, this is not reflective of the human host environment. Changes in media conditions and environment have led to differences in antibiotic efficacy outcomes and have major implications in treatment. To further elucidate differential antibiotic resistance mechanisms, adaptive laboratory evolution was utilized to generate strains of the S. aureus clinical isolate, USA300 TCH1516, under an increasing antibiotic pressure of nafcillin in CA-MHB as well as Roswell Park Memorial Institute medium (RPMI). Evolutionary paths for strains were characterized at the physiological and genetic level utilizing whole genome sequencing to understand the genetic and metabolic basis of antibiotic resistance. Improvements in growth rate were observed for media adaptation to RPMI but not CA-MHB. Improved fitness in stressful conditions were identified and linked to mutated copies of apt. Key reproducibly occurring mutations were compared between the two environments after exposure to nafcillin showing mutations in common regions within the vraRST operon, a regulator of cell wall metabolism genes, and in mgt, a nonessential transglycosylase. Unique susceptible phenotypes to gentamicin and azithromycin were identified after tolerance to nafcillin. Mutated genes yybT and ybbP, codY, and oatA related to regulation of nucleotide and branched chain amino acid metabolism as well as peptidoglycan biosynthesis and modification were identified for tolerance to nafcillin in RPMI only. Mutations were subsequently compared across literature and presented here.

      • 2 supplemental files
      Cover page: Determining Media Dependent Mechanisms of Antibiotic Resistance in Laboratory Evolved Staphylococcus aureus
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2017)
      The occurrence of mutations is a cornerstone of the evolutionary theory of adaptation, capitalizing on the rare chance that a mutation confers a fitness benefit. Natural selection is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining when to consider an experiment complete and for balancing outcomes with available resources (i.e., laboratory supplies, personnel, and time). To design and to better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to the optimization of ALE experiments. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim, to explain differences in experimental outcomes. Furthermore, a beneficial mutation rate of 10-6.9 to 10-8.4 mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in serial passage batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results described here will aid in the design of ALE experiments to fit the exact needs of a project while taking into account the resources required and will lower the barriers to entry for this experimental technique.IMPORTANCE ALE is a widely used scientific technique to increase scientific understanding, as well as to create industrially relevant organisms. The manner in which ALE experiments are conducted is highly manual and uniform, with little optimization for efficiency. Such inefficiencies result in suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in an optimized fashion and can design experiments to generate greater fitness in an accelerated time frame, thereby pushing the limits of what adaptive laboratory evolution can achieve.
        Cover page: A Model for Designing Adaptive Laboratory Evolution Experiments
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2017)
        Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution.
          Cover page: Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2018)
          Unraveling the mechanisms of microbial adaptive evolution following genetic or environmental challenges is of fundamental interest in biological science and engineering. When the challenge is the loss of a metabolic enzyme, adaptive responses can also shed significant insight into metabolic robustness, regulation, and areas of kinetic limitation. In this study, whole-genome sequencing and high-resolution 13C-metabolic flux analysis were performed on 10 adaptively evolved pgi knockouts of Escherichia coliPgi catalyzes the first reaction in glycolysis, and its loss results in major physiological and carbon catabolism pathway changes, including an 80% reduction in growth rate. Following adaptive laboratory evolution (ALE), the knockouts increase their growth rate by up to 3.6-fold. Through combined genomic-fluxomic analysis, we characterized the mutations and resulting metabolic fluxes that enabled this fitness recovery. Large increases in pyridine cofactor transhydrogenase flux, correcting imbalanced production of NADPH and NADH, were enabled by direct mutations to the transhydrogenase genes sthA and pntAB The phosphotransferase system component crr was also found to be frequently mutated, which corresponded to elevated flux from pyruvate to phosphoenolpyruvate. The overall energy metabolism was found to be strikingly robust, and what have been previously described as latently activated Entner-Doudoroff and glyoxylate shunt pathways are shown here to represent no real increases in absolute flux relative to the wild type. These results indicate that the dominant mechanism of adaptation was to relieve the rate-limiting steps in cofactor metabolism and substrate uptake and to modulate global transcriptional regulation from stress response to catabolism.
            Cover page: Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2011)
            Abstract 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.
            • 6 supplemental files
            Cover page: A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2014)

            Background

            Membranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models.

            Results

            The recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass.

            Conclusion

            iJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models.
              Cover page: Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scaleCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2017)
              Adaptive laboratory evolution (ALE) experiments are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the observed distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.IMPORTANCE Evolution and natural selection inexorably lead to an organism's improved fitness in a given environment, whether in a laboratory or natural setting. However, despite the frequent natural occurrence of complex and dynamic growth environments, laboratory evolution experiments typically maintain simple, static culturing environments so as to reduce selection pressure complexity. In this study, we investigated the adaptive strategies underlying evolution to fluctuating environments by evolving Escherichia coli to conditions of frequently switching growth substrate. Characterization of evolved strains via a number of different data types revealed the various genetic and phenotypic changes implemented in pursuit of growth optimality and how these differed across the different growth substrates and switching protocols. This work not only helps to establish general principles of adaptation to complex environments but also suggests strategies for experimental design to achieve desired evolutionary outcomes.
                Cover page: Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2018)
                Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint-based reconstruction and analysis. Filtering for high confidence in silico designs is critical because in vivo construction and testing of strains is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production when considering the biosynthetic costs of the proteome and variability in enzyme kinetic parameters using a genome-scale model of metabolism and gene expression (ME-model). A collection of 2632 unfiltered knockout designs in Escherichia coli was evaluated by the workflow. A ME-model was used in the workflow to test the designs' growth-coupled production in addition to a less complex genome-scale metabolic model (M-model). The workflow identified 634 M-model growth-coupled designs which met the filtering criteria and 42 robust designs, which met growth-coupled production criteria using both M and ME-models. Knockouts were found to follow a pattern of controlling intermediate metabolite consumption such as pyruvate consumption and high flux subsystems such as glycolysis. Kinetic parameter sampling using the ME-model revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes.
                  Cover page: Identification of growth-coupled production strains considering protein costs and kinetic variability
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