Genome-Scale Metabolic Modeling of Escherichia coli and Its Chassis Design for Synthetic Biology Applications

Intracellular accumulation of c-di-GMP and its regulation on self-flocculation of the bacterial cells of Zymomonas mobilis

Zymomonas mobilis is an emerging chassis for being engineered to produce bulk products due to its unique glycolysis through the Entner-Doudoroff pathway with less ATP produced for lower biomass accumulation and higher product yield. When self-flocculated, the bacterial cells are more productive, since they can self-immobilize within bioreactors for high density, and are more tolerant to stresses for higher product titers, but this morphology needs to be controlled properly to avoid internal mass transfer limitation associated with their strong self-flocculation. Herewith we explored the regulation of cyclic diguanosine monophosphate (c-di-GMP) on self-flocculation of the bacterial cells through activating cellulose biosynthesis. While ZMO1365 and ZMO0919 with GGDEF domains for diguanylate cyclase activity catalyze c-di-GMP biosynthesis, ZMO1487 with an EAL domain for phosphodiesterase activity catalyzes c-di-GMP degradation, but ZMO1055 and ZMO0401 contain the dual domains with phosphodiesterase activity predominated. Since c-di-GMP is synthesized from GTP, the intracellular accumulation of this signal molecule through deactivating phosphodiesterase activity is preferred for activating cellulose biosynthesis to flocculate the bacterial cells, because such a strategy exerts less perturbance on intracellular processes regulated by GTP. These discoveries are significant for not only engineering unicellular Z. mobilis strains with the self-flocculating morphology to boost production but also understanding mechanism underlying c-di-GMP biosynthesis and degradation in the bacterium.

Sustainable metabolic engineering requires a perfect trifecta

The versatility of cellular metabolism in converting various substrates to products inspires sustainable alternatives to conventional chemical processes. Metabolism can be engineered to maximize the yield, rate, and titer of product generation. However, the numerous combinations of substrate, product, and organism make metabolic engineering projects difficult to navigate. A perfect trifecta of substrate, product, and organism is prerequisite for an environmentally and economically sustainable metabolic engineering endeavor. As a step toward this endeavor, we propose a reverse engineering strategy that starts with product selection, followed by substrate and organism pairing. While a large bioproduct space has been explored, the top-ten compounds have been synthesized mainly using glucose and model organisms. Unconventional feedstocks (e.g. hemicellulosic sugars and CO2) and non-model organisms are increasingly gaining traction for advanced bioproduct synthesis due to their specialized metabolic modes. Judicious selection of the substrate-organism-product combination will illuminate the untapped territory of sustainable metabolic engineering.

Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae

Heme availability in the cell enables the proper folding and function of enzymes, which carry heme as a cofactor. Using genome-scale modeling, we identified metabolic fluxes and genes that limit heme production. Our study experimentally validates ecYeast8 model predictions. Moreover, we developed an approach to predict gene combinations, which provides an in silico design of a viable strain able to overproduce the metabolite of interest. Using our approach, we constructed a yeast strain that produces 70-fold-higher levels of intracellular heme. With its high-capacity metabolic subnetwork, our engineered strain is a suitable platform for the production of additional heme enzymes. The heme ligand-binding biosensor (Heme-LBB) detects the cotranslational incorporation of heme into the heme-protein hemoglobin.

Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme.

Rhythm of the Night (and Day): Predictive Metabolic Modeling of Diurnal Growth in Chlamydomonas

Economical production of photosynthetic organisms requires the use of natural day/night cycles. These induce strong circadian rhythms that lead to transient changes in the cells, requiring complex modeling to capture. In this study, we coupled times series transcriptomic data from the model green alga Chlamydomonas reinhardtii to a metabolic model of the same organism in order to develop the first transient metabolic model for diurnal growth of algae capable of predicting phenotype from genotype. We first transformed a set of discrete transcriptomic measurements (D. Strenkert, S. Schmollinger, S. D. Gallaher, P. A. Salomé, et al., Proc Natl Acad Sci U S A 116:2374–2383, 2019, https://doi.org/10.1073/pnas.1815238116) into continuous curves, producing a complete database of the cell’s transcriptome that can be interrogated at any time point. We also decoupled the standard biomass formation equation to allow different components of biomass to be synthesized at different times of the day. The resulting model was able to predict qualitative phenotypical outcomes of a starchless mutant. We also extended this approach to simulate all single-knockout mutants and identified potential targets for rational engineering efforts to increase productivity. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition, and intracellular fluxes for diurnal growth.

IMPORTANCE We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii. The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.

Present and Future Perspectives on Therapeutic Options for Carbapenemase-Producing Enterobacterales Infections

Carbapenem-resistant Enterobacterales (CRE) are included in the list of the most threatening antibiotic resistance microorganisms, being responsible for often insurmountable therapeutic issues, especially in hospitalized patients and immunocompromised individuals and patients in intensive care units. The enzymatic resistance to carbapenems is encoded by different β-lactamases belonging to A, B or D Ambler class. Besides compromising the activity of last-resort antibiotics, CRE have spread from the clinical to the environmental sectors, in all geographic regions. The purpose of this review is to present present and future perspectives on CRE-associated infections treatment.