Rapid metabolic pathway assembly and modification using serine integrase site-specific recombination

Synthetic biology requires effective methods to assemble DNA parts into devices and to modify these devices once made. Here we demonstrate a convenient rapid procedure for DNA fragment assembly using site-specific recombination by ϕC31 integrase. Using six orthogonal attP/attB recombination site pairs with different overlap sequences, we can assemble up to five DNA fragments in a defined order and insert them into a plasmid vector in a single recombination reaction. ϕC31 integrase-mediated assembly is highly efficient, allowing production of large libraries suitable for combinatorial gene assembly strategies. The resultant assemblies contain arrays of DNA cassettes separated by recombination sites, which can be used to manipulate the assembly by further recombination. We illustrate the utility of these procedures to (i) assemble functional metabolic pathways containing three, four or five genes; (ii) optimize productivity of two model metabolic pathways by combinatorial assembly with randomization of gene order or ribosome binding site strength; and (iii) modify an assembled metabolic pathway by gene replacement or addition

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

13C metabolic flux analysis (13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference S. cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1∆ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1∆ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria towards cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1∆ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium

Lower-Cost, Lower-Carbon Production of Circular Polydiketoenamine Plastics

The efficiency by which monomers may be recovered during the chemical recycling of plastic waste has thus far dominated the discussion over which future polymer chemistries might be more sustainable than those in use today. However, at scale, other factors emerge as equally important, such as the costs of primary versus secondary resin production as well as the energy and carbon intensity of circular manufacturing processes. We apply systems analysis to identify problematic chemical processes used for the primary production of plastics designed for infinite recyclability: polydiketoenamine (PDK) resins from novel triketone and amine monomers. Leveraging this knowledge, we advance a less intensive process for triketone production, which lowers the cost of primary PDK production by 57% and results in 66% less life-cycle greenhouse gas (GHG) emissions. Using the automotive sector as a case study, we discuss the impact of replacing nonrecyclable polyurethane with circular PDK over the next 60 years. We find that the cumulative GHG emissions associated with introducing PDK are half those of staying the course with polyurethane. However, the extent to which circularity is realized through targeted collection and sorting plays the dominant role in determining how much of those savings is practically achievable

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed

BayFlux: A Bayesian Method to Quantify Metabolic Fluxes and their Uncertainty at the Genome Scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty

Leveling the cost and carbon footprint of circular polymers that are chemically recycled to monomer

Mechanical recycling of polymers downgrades them such that they are unusable after a few cycles. Alternatively, chemical recycling to monomer offers a means to recover the embodied chemical feedstocks for remanufacturing. However, only a limited number of commodity polymers may be chemically recycled, and the processes remain resource intensive. We use systems analysis to quantify the costs and life-cycle carbon footprints of virgin and chemically recycled polydiketoenamines (PDKs), next-generation polymers that depolymerize under ambient conditions in strong acid. The cost of producing virgin PDK resin using unoptimized processes is ~30-fold higher than recycling them, and the cost of recycled PDK resin ($1.5 kg−1) is on par with PET and HDPE, and below that of polyurethanes. Virgin resin production is carbon intensive (86 kg CO2e kg−1), while chemical recycling emits only 2 kg CO2e kg−1. This cost and emissions disparity provides a strong incentive to recover and recycle future polymer waste

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes

Quantitative Proteomic Analysis of Nitrate Stress in Desulfovibrio vulgaris

Desulfovibrio vulgaris Hildenborough (DvH), a sulfate reducing bacterium, has historically been an environmentally important bacterium due its role in bio-corrosion of oil and gas pipelines and is one of the major sources of H2S that cause bio-fouling of petroleum. Another reason for interest in DvH is due to its ability to reduce toxic and radioactive metals to their lower oxidation and insoluble forms, and therefore its potential use in bioremediation. While sulfate typically serves as the electron acceptor in DvH, alternate candidates for electron acceptors such as nitrate also exist. Exposure to excess nitrate occurs frequently since it is a common co-contaminant along with metals such as uranium in many waste sites. Therefore our knowledge of DvH response to nitrate will undoubtedly be critical in developing bioremediation strategies. This poster presents the results from a quantitative proteomic analysis evaluating the response of DvH to nitrate stress. Control proteome was compared with proteome from cells exposed to NaNO3 levels that cause a 50% inhibition in growth. The ITRAQ peptide labeling strategy coupled with tandem liquid chromatography and mass spectrometry (triple-quad time of flight) was used. A total of 1166 unique proteins were identified, representing 34% of the total DvH proteome and spanning every functional category. Our results indicate that this was a mild stress, as confirmed by the lack of change observed in central metabolism or in the sulfate reduction pathway. Increases seen in transport systems for proline, glycine betaine and glutamate indicate that the NaNO3 exposure led to both salt stress and nitrate stress. Up-regulation observed in a large number of ABC transport systems as well as in iron-sulfur cluster containing proteins, however, appear to be specific to the exposure to nitrate. Finally, a number of hypothetical proteins are among the most significant changers, indicating that there may be unknown mechanisms initiated upon