System-level characterization of engineered and evolved formatotrophic E. coli strains

Yilmaz, S.; Kanis, B.; Hogers, R.; Benito-Vaquerizo, S.; Kahnt, J.; Glatter, T.; Dronsella, B.; Erb, T. J.; Suarez-Diez, M.; Claassens, N. J.

doi:10.1101/2024.11.11.622960

bioRxiv · 10.1101/2024.11.11.622960

System-level characterization of engineered and evolved formatotrophic E. coli strains

Yilmaz, S.·Kanis, B.·Hogers, R.·Benito-Vaquerizo, S.·Kahnt, J.·Glatter, T.·Dronsella, B.·Erb, T. J.·Suarez-Diez, M.·Claassens, N. J.

View original source ↗Export metadata ↓

Abstract

One-carbon compounds, such as formate, are promising and sustainable feedstocks for microbial bioproduction of fuels and chemicals. Growth of Escherichia coli on formate was recently achieved by introducing the reductive glycine pathway (rGlyP) into its genome, which is theoretically the most energy-efficient aerobic formate assimilation pathway. While adaptive laboratory evolution was used to enhance the growth rate and biomass yield significantly, still the best performing formatotrophic E. coli strain did not approach the theoretical optimal biomass yield of the rGlyP. In this study, we investigated these previously engineered formatotrophic E. coli strains to find out why the biomass yield was sub-optimal and how it may be improved. Through a combination of metabolic modelling, genomic and proteomic analysis, we identified several potential metabolic bottlenecks and future targets for optimization. This study also reveals further insights in the evolutionary mutations and related changes in proteome allocation that supported the already substantially improved growth of formatotrophic E. coli strain. This systems-level analysis provides key insights to realize high-yield, fast growing formatotrophic strains for future bioproduction.

Explore related subjects

synthetic biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yilmaz, S., Kanis, B., Hogers, R., Benito-Vaquerizo, S., Kahnt, J., Glatter, T., Dronsella, B., Erb, T. J., Suarez-Diez, M., Claassens, N. J.. 2024-11-12. System-level characterization of engineered and evolved formatotrophic E. coli strains. https://doi.org/10.1101/2024.11.11.622960

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Gene expression noise is reduced in communicating synthetic cell populations

A major goal in bottom-up synthetic biology is the construction of multicellular synthetic systems capable of coordinated and robust collective behaviours. However, robustness is often limited by noise and variability arising from increased molecular complexity. Whilst communication has been implemented in synthetic multi-cellular systems, the ability for communication to suppress cell free gene expression variability in populations of synthetic cells remain unexplored. To address this, we encapsulated the Lux and Las quorum sensing gene circuits in lipid vesicles under cell-free conditions to test the effect of communication on reducing cell-free gene expression variability across the population. Our results show that communication, limiting expression resources, and membrane surface effects can reduce gene expression variability. Resource limited Gillespie simulations for transcription and translation show that communication-mediated coupling reduces population-level expression noise under constrained and excess resource conditions. Together, our work provides simple strategies to reduce gene expression variability and thereby improve robustness in synthetic multicellular systems, an important criteria for the future applications of synthetic cells.

synthetic biology↗

bioRxiv2026

Boolean Logic-responsive FRET Biosensors via Genetically Encoded Autonomous Compilation

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

synthetic biology↗

bioRxiv2026

AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

synthetic biology↗