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Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

The expression of correctly folded and functional heterologous proteins is important in many biotechnological production processes, whether it is enzymes, biopharmaceuticals or biosynthetic pathways for production of sustainable chemicals. For industrial applications, bacterial platform organisms, such as E. coli, are still broadly used due to the availability of tools and proven suitability at industrial scale. However, expression of heterologous proteins in these organisms can result in protein aggregation and low amounts of functional protein. This review provides an overview of the cellular mechanisms that can influence protein folding and expression, such as co-translational folding and assembly, chaperone binding, as well as protein quality control, across different model organisms. The knowledge of these mechanisms is then linked to different experimental methods that have been applied in order to improve functional heterologous protein folding, such as codon optimization, fusion tagging, chaperone co-production, as well as strain and protein engineering strategies.

L-serine is a promising building block biochemical with a high theoretical production yield from glucose. Toxicity of L-serine is however prohibitive for high-titer production in E. coli. Here, E. coli lacking L-serine degradation pathways was evolved for improved tolerance by gradually increasing L-serine concentration from 3 to 100g/L using adaptive laboratory evolution (ALE). Genome sequencing of isolated clones revealed multiplication of genetic regions, as well as mutations in thrA, thereby showing a potential mechanism of serine inhibition. Additional mutations were evaluated by MAGE combined with amplicon sequencing, revealing role of rho, lrp, pykF, eno, and rpoB on tolerance and fitness in minimal medium. Production using the tolerant strains resulted in 37g/L of L-serine with a 24% mass yield. The resulting titer is similar to the highest production reported for any organism thereby highlighting the potential of ALE for industrial biotechnology.

Microbial cell factories have been engineered to produce a variety of biochemicals, ranging from biofuels, food addtives to pharmaceuticals. However, for most compounds, the production yield is far from reaching economical targets. Accumulation of excess biomass contributes to decreasing production yields, and a method for limiting biomass formation while allowing for continued production of biochemicals is therefore desirable. In this study, we investigated eight different culturing setups aiming at inhibiting biomass formation of Escherichia coli, based on nutrient limitations or the addition of growth inhibitors. The ability to control cell growth and the production of biochemicals, exemplified by mevalonate and tyrosine, was characterized. An increased mass yield of both mevalonate and tyrosine was achieved by limiting phosphate, sulfate or magnesium in the media. Sulfate limitation, in particular, resulted in an increase in mass yield of mevalonate and tyrosine by 80% and 50%, respectively. By tracking production and biomass concentrations, it was observed that the production was maintained for more than 10 h after inhibition of cell growth, despite cell maintenance requirements. The outlined method serves as promising approach for increasing production yield of a range of different biochemicals.

A standard protocol for analysis of microbial factories requires the screening of several populations in order to find the best performing ones. Standard analytical methods usually include high performance liquid chromatography (HPLC), thin layer chromatography (TLC) or spectrophotometry, which are expensive and time-consuming processes. Surface Enhanced Raman Spectroscopy (SERS), instead, is a highly sensitive spectroscopic technique for specific, fast and real-time sensing of biological samples. Here we demonstrate the use of SERS to discriminate between two different bacterial populations based on detection of p-coumaric acid (pHCA) in cell supernatant. SERS active substrates, based on leaning gold-capped silicon nanopillars, were used for detection. They were successfully used to detect culture medium spiked with pHCA, and the effect of medium dilution was studied. For analysis of biological production of pHCA, triplicate cultures of E. coli strains expressing a pHCA-forming enzyme (P) as well as of a non-producing strain (C) were grown. Then, supernatant samples were collected and their pHCA content was measured using SERS and HPLC for comparison. The intensity of the pHCA Raman mode at 1169 cm-1 (CH-rocking motion) showed different trends for P and C strains, similar to the results obtained using the HPLC method. Results illustrate that SERS can be used for quick and semi- quantitative discrimination of pHCA concentrations in cell supernatant medium.

Production of proteins and biochemicals in microbial cell factories is often limited by carbon and energy spent on excess biomass formation. To address this issue, we developed several genetic growth switches based on CRISPR interference technology. We demonstrate that growth of Escherichia coli can be controlled by repressing the DNA replication machinery, by targeting dnaA and oriC, or by blocking nucleotide synthesis through pyrF or thyA. This way, total GFP-protein production could be increased by up to 2.2-fold. Single-cell dynamic tracking in microfluidic systems was used to confirm functionality of the growth switches. Decoupling of growth from production of biochemicals was demonstrated for mevalonate, a precursor for isoprenoid compounds. Mass yield of mevalonate was increased by 41%, and production was maintained for more than 45 h after activation of the pyrF-based growth switch. The developed methods represent a promising approach for increasing production yield and titer for proteins and biochemicals.