Michael C. Jewett

Precise Manipulation of Chromosomes in Vivo Enables Genome-Wide Codon Replacement

Template-mediated, genome construction and assembly created a strain with 80 precise codon changes. We present genome engineering technologies that are capable of fundamentally reengineering genomes from the nucleotide to the megabase scale. We used multiplex automated genome engineering (MAGE) to site-specifically replace all 314 TAG stop codons with synonymous TAA codons in parallel across 32 Escherichia coli strains. This approach allowed us to measure individual recombination frequencies, confirm viability for each modification, and identify associated phenotypes. We developed hierarchical conjugative assembly genome engineering (CAGE) to merge these sets of codon modifications into genomes with 80 precise changes, which demonstrate that these synonymous codon substitutions can be combined into higher-order strains without synthetic lethal effects. Our methods treat the chromosome as both an editable and an evolvable template, permitting the exploration of vast genetic landscapes.

Cell-free protein synthesis: Applications come of age

Cell-free protein synthesis has emerged as a powerful technology platform to help satisfy the growing demand for simple and efficient protein production. While used for decades as a foundational research tool for understanding transcription and translation, recent advances have made possible cost-effective microscale to manufacturing scale synthesis of complex proteins. Protein yields exceed grams protein produced per liter reaction volume, batch reactions last for multiple hours, costs have been reduced orders of magnitude, and reaction scale has reached the 100-liter milestone. These advances have inspired new applications in the synthesis of protein libraries for functional genomics and structural biology, the production of personalized medicines, and the expression of virus-like particles, among others. In the coming years, cell-free protein synthesis promises new industrial processes where short protein production timelines are crucial as well as innovative approaches to a wide range of applications.

Cell-free synthetic biology: Thinking outside the cell

Cell-free synthetic biology is emerging as a powerful approach aimed to understand, harness, and expand the capabilities of natural biological systems without using intact cells. Cell-free systems bypass cell walls and remove genetic regulation to enable direct access to the inner workings of the cell. The unprecedented level of control and freedom of design, relative to in vivo systems, has inspired the rapid development of engineering foundations for cell-free systems in recent years. These efforts have led to programmed circuits, spatially organized pathways, co-activated catalytic ensembles, rational optimization of synthetic multi-enzyme pathways, and linear scalability from the micro-liter to the 100-liter scale. It is now clear that cell-free systems offer a versatile test-bed for understanding why natures designs work the way they do and also for enabling biosynthetic routes to novel chemicals, sustainable fuels, and new classes of tunable materials. While challenges remain, the emergence of cell-free systems is poised to open the way to novel products that until now have been impractical, if not impossible, to produce by other means.

An integrated cell‐free metabolic platform for protein production and synthetic biology

Cell‐free systems offer a unique platform for expanding the capabilities of natural biological systems for useful purposes, i.e. synthetic biology. They reduce complexity, remove structural barriers, and do not require the maintenance of cell viability. Cell‐free systems, however, have been limited by their inability to co‐activate multiple biochemical networks in a single integrated platform. Here, we report the assessment of biochemical reactions in an Escherichia coli cell‐free platform designed to activate natural metabolism, the Cytomim system. We reveal that central catabolism, oxidative phosphorylation, and protein synthesis can be co‐activated in a single reaction system. Never before have these complex systems been shown to be simultaneously activated without living cells. The Cytomim system therefore promises to provide the metabolic foundation for diverse ab initio cell‐free synthetic biology projects. In addition, we describe an improved Cytomim system with enhanced protein synthesis yields (up to 1200 mg/l in 2 h) and lower costs to facilitate production of protein therapeutics and biochemicals that are difficult to make in vivo because of their toxicity, complexity, or unusual cofactor requirements.

The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: A scaffold to query lipid metabolism

BackgroundUp to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism.ResultsThe reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.ConclusionPerforming integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein–protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental 13C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional 13C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.

Cell‐free metabolic engineering: Biomanufacturing beyond the cell

Industrial biotechnology and microbial metabolic engineering are poised to help meet the growing demand for sustainable, low‐cost commodity chemicals and natural products, yet the fraction of biochemicals amenable to commercial production remains limited. Common problems afflicting the current state‐of‐the‐art include low volumetric productivities, build‐up of toxic intermediates or products, and byproduct losses via competing pathways. To overcome these limitations, cell‐free metabolic engineering (CFME) is expanding the scope of the traditional bioengineering model by using in vitro ensembles of catalytic proteins prepared from purified enzymes or crude lysates of cells for the production of target products. In recent years, the unprecedented level of control and freedom of design, relative to in vivo systems, has inspired the development of engineering foundations for cell‐free systems. These efforts have led to activation of long enzymatic pathways (>8 enzymes), near theoretical conversion yields, productivities greater than 100 mg L–1 h–1, reaction scales of >100 L, and new directions in protein purification, spatial organization, and enzyme stability. In the coming years, CFME will offer exciting opportunities to: (i) debug and optimize biosynthetic pathways; (ii) carry out design‐build‐test iterations without re‐engineering organisms; and (iii) perform molecular transformations when bioconversion yields, productivities, or cellular toxicity limit commercial feasibility.

Intraventricular flow during isovolumic relaxation: Description and characterization by Doppler echocardiography

This study describes the characteristics of a prominent Doppler flow velocity signal representing intraventricular flow during left ventricular isovolumic relaxation. The flow during the isovolumic relaxation period was demonstrated in 60 subjects, including 7 with a normal heart, 26 with hypertrophic cardiomyopathy, 10 with aortic valve disease, 9 with a transplanted heart and 8 others. All had normal to hyperdynamic left ventricular systolic function with some degree of cavity obliteration as seen in the apical two-dimensional echocardiographic views. In contrast, this isovolumic relaxation period flow could not be demonstrated in the absence of cavity obliteration in any of 20 patients with either normal or diminished left ventricular systolic function. Isovolumic relaxation period flow was best recorded from the apical transducer position and was directed toward the apex in all patients. By pulsed wave, and with two-dimensional Doppler ultrasound, the isovolumic relaxation period flow originated within a narrow area in the medial portion of the left ventricle along the middle or basal segments of the interventricular septum, but was recorded over a larger area toward the apex. The peak isovolumic relaxation period flow velocity was recorded just basal to the area of cavity obliteration, usually at the level of the papillary muscles, and ranged from 0.4 to 2.3 m/s (mean of 1.0 m/s). This isovolumic relaxation period flow started with aortic valve closure and, in 50 of the 60 patients, it lasted throughout isovolumic relaxation until mitral valve opening. In the other 10 patients (all with hypertrophic cardiomyopathy), it lasted for only a part (mean 63%) of this period.(ABSTRACT TRUNCATED AT 250 WORDS)

Growth Temperature Exerts Differential Physiological and Transcriptional Responses in Laboratory and Wine Strains of Saccharomyces cerevisiae

ABSTRACT Laboratory strains of Saccharomyces cerevisiae have been widely used as a model for studying eukaryotic cells and mapping the molecular mechanisms of many different human diseases. Industrial wine yeasts, on the other hand, have been selected on the basis of their adaptation to stringent environmental conditions and the organoleptic properties that they confer to wine. Here, we used a two-factor design to study the responses of a standard laboratory strain, CEN.PK113-7D, and an industrial wine yeast strain, EC1118, to growth temperatures of 15°C and 30°C in nitrogen-limited, anaerobic, steady-state chemostat cultures. Physiological characterization revealed that the growth temperature strongly impacted the biomass yield of both strains. Moreover, we found that the wine yeast was better adapted to mobilizing resources for biomass production and that the laboratory yeast exhibited higher fermentation rates. To elucidate mechanistic differences controlling the growth temperature response and underlying adaptive mechanisms between the strains, DNA microarrays and targeted metabolome analysis were used. We identified 1,007 temperature-dependent genes and 473 strain-dependent genes. The transcriptional response was used to identify highly correlated gene expression subnetworks within yeast metabolism. We showed that temperature differences most strongly affect nitrogen metabolism and the heat shock response. A lack of stress response element-mediated gene induction, coupled with reduced trehalose levels, indicated that there was a decreased general stress response at 15°C compared to that at 30°C. Differential responses among strains were centered on sugar uptake, nitrogen metabolism, and expression of genes related to organoleptic properties. Our study provides global insight into how growth temperature affects differential physiological and transcriptional responses in laboratory and wine strains of S. cerevisiae.

Cell-free protein synthesis from a release factor 1 deficient Escherichia coli activates efficient and multiple site-specific nonstandard amino acid incorporation.

Site-specific incorporation of nonstandard amino acids (NSAAs) into proteins enables the creation of biopolymers, proteins, and enzymes with new chemical properties, new structures, and new functions. To achieve this, amber (TAG codon) suppression has been widely applied. However, the suppression efficiency is limited due to the competition with translation termination by release factor 1 (RF1), which leads to truncated products. Recently, we constructed a genomically recoded Escherichia coli strain lacking RF1 where 13 occurrences of the amber stop codon have been reassigned to the synonymous TAA codon (rEc.E13.ΔprfA). Here, we assessed and characterized cell-free protein synthesis (CFPS) in crude S30 cell lysates derived from this strain. We observed the synthesis of 190 ± 20 μg/mL of modified soluble superfolder green fluorescent protein (sfGFP) containing a single p-propargyloxy-l-phenylalanine (pPaF) or p-acetyl-l-phenylalanine. As compared to the parent rEc.E13 strain with RF1, this results in a modified sfGFP synthesis improvement of more than 250%. Beyond introducing a single NSAA, we further demonstrated benefits of CFPS from the RF1-deficient strains for incorporating pPaF at two- and five-sites per sfGFP protein. Finally, we compared our crude S30 extract system to the PURE translation system lacking RF1. We observed that our S30 extract based approach is more cost-effective and high yielding than the PURE translation system lacking RF1, ∼1000 times on a milligram protein produced/