Garcia Martin, H. | Search bioRxiv

bioRxiv2023

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Engineering bacterial metabolism to efficiently produce chemicals and materials from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important metabolic engineering tools for programming multi-gene expression regulation. However, poor predictability of guide RNA folding can disrupt enzyme balance through unreliable expression control. We devised a set of computational parameters that can describe guide RNA folding, and we expect them to be broadly applicable across CRISPR-Cas9 systems. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a kinetic parameter describing folding rate into the active structure. This parameter also enables forward design of new scRNAs, with no observed failures in our screen. We use CRISPRa target sequences from this set to design a system of three synthetic promoters that can orthogonally activate and tune expression of chosen outputs over a >35-fold dynamic range. Independent activation tuning allows experimental exploration of a three-dimensional expression design space via a 64-member combinatorial triple-scRNA library. We apply these CRISPRa programs to two biosynthetic pathways, demonstrating production of valuable pteridine and human milk oligosaccharide products in E. coli. Profiling these design spaces indicated expression combinations producing up to 2.3-fold higher titer than that produced by maximal expression. Mapping production can also identify bottlenecks as targets for pathway redesign, improving titer of the oligosaccharide lacto-N-tetraose by 6-fold. Aided by computational scRNA efficacy prediction, the combinatorial CRISPRa strategy enables effective optimization of multi-step metabolic pathways. More broadly, the guide RNA design rules uncovered here may enable the routine design of effective multi-guide programs for a wide range of model- and data-driven applications of CRISPR gene regulation in bacterial hosts.

synthetic biology↗

bioRxiv2023

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane), and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

synthetic biology↗

bioRxiv2023

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 ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods. We provide an open source Python implementation of BayFlux at https://github.com/JBEI/bayflux. Author summary13C MFA practitioners know that modeling results can be sensitive to minor modifications of the metabolic model. Certain parts of the metabolic model that are not well mapped to a molecular mechanism (e.g. drains to biomass or ATP maintenance) can have an inordinate impact on the final fluxes. The only way to ascertain the validity of the model is by checking that the result does not significantly differ from previously observed flux profiles. However, that approach diminishes the possibility of discovering truly novel flux profiles. Because of this strong dependence on metabolic model details, it would be very useful to have a systematic and repeatable way to produce these metabolic models. And indeed there is one: genome-scale metabolic models can be systematically obtained from genomic sequences, and represent all the known genomically encoded metabolic information. However, these models are much larger than the traditionally used central carbon metabolism models. Hence, the number of degrees of freedom of the model (fluxes) significantly exceeds the number of measurements (metabolite labeling profiles and exchange fluxes). As a result, one expects many flux profiles compatible with the experimental data. The best way to represent these is by identifying all fluxes compatible with the experimental data. Our novel method BayFlux, based on Bayesian inference and Markov Chain Monte Carlo sampling, provides this capability. Interestingly, this approach leads to the observation that traditional optimization approaches can significantly overestimate flux uncertainty, and that genome-scale models of metabolism produce narrower flux distributions than the small core metabolic models that are traditionally used in 13C MFA. Furthermore, we show that the extra information provided by this approach allows us to improve knockout predictions, compared to traditional methods. Although the method scales well with more reactions, improvements will be needed to tackle the large metabolic models found in microbiomes and human metabolism.

systems biology↗