Biology subjects

Satanowski, A.

Publications and source records attributed to Satanowski, A..

7 recordsLinked to original sources

bioRxiv2026

Combining evolution and machine learning-guided pathway optimization to engineer a novel methylsuccinate module for synthetic C1 metabolism in vivo

De novo metabolic pathways open possibilities for sustainable biotransformations in microbes. However, the in vivo-implementation of such new-to-nature pathways is highly challenging and heavily relies on adaptive laboratory evolution (ALE) of the hosts native metabolic network. Here, we assess how much this need for host-centric ALE can be overcome and/or complemented through the informed design of the newly introduced pathway. Exemplifying for a synthetic CO2-fixation module via methyl-succinate, we established methylsuccinate-dependent growth of Escherichia coli over six months by ALE of E. colis native metabolism. In parallel, we developed a machine-learning guided workflow (MEVIS) for the automated engineering of the synthetic pathway, resulting in methylsuccinate-dependent growth within three weeks. Critically, performing MEVIS in the background of the ALE-evolved strain is necessary to further approach wild-type like growth, demonstrating how ALE in combination with machine-learning-guided lab automation holds great potential to accelerate and improve design-build-test-learn cycles in contemporary metabolic engineering.

bioengineering↗

bioRxiv2025

Continuous evolution of a halogenase enzyme with improved solubility and activity for sustainable bioproduction

Halogenation enhances the stability and function of pharmaceuticals, biomaterials, and industrial compounds. However, chemical halogenation lacks stereoselectivity and requires the use of toxic or expensive chemicals. Although enzymatic halogenation can improve selectivity and reduce environmental impact, current halogenases are inefficient and insoluble, leading to low yields that limit their applications. Here, we develop RebHEvo4, a soluble and highly active tryptophan halogenase, containing 12 mutations that confer 37-fold and 44-fold increases in 7-chloro and 7-bromotryptophan production respectively, in vivo. To create RebHEvo4, we devised an aminoacyl tRNA synthetase based halogenase biosensor and conducted over 500 hours of phage-assisted continuous evolution (PACE). Use of RebHEvo4 in a bioreactor resulted in the production of 2.7 g/L of halogenated tryptophan. When coupled with a downstream enzyme, RebHEvo4 allowed 36-fold increased yields of halogenated tryptamines compared to the wild-type enzyme. Additionally, RebHEvo4 enabled efficient production of genetically encoded antimicrobial halogenated peptides. The efficient, site-specific halogenation by our evolved halogenase will accelerate sustainable biomanufacturing of halogenated drugs.

biochemistry↗

bioRxiv2024

Rewiring Escherichia coli to Transform Formate into Methyl Groups

BackgroundBiotechnological applications are steadily growing and have become an important tool to reinvent the synthesis of chemicals and pharmaceuticals for lower dependence on fossil resources. In order to sustain this progression, new feedstocks for biotechnological hosts have to be explored. One-carbon (C1-)compounds, including formate, derived from CO2 or organic waste are accessible in large quantities with renewable energy, making them promising candidates. Previous studies showed that introduction of the formate assimilation machinery from Methylorubrum extorquens into Escherichia coli allows assimilation of formate into the established biotechnological host. Applying this established route for formate assimilation, we here investigated utilisation of formate for the production of value-added building blocks in E. coli using S-adenosylmethionine (SAM)-dependent methyltransferases. ResultsWe first analysed methylation activity in E. coli BL21 with a two-vector system to produce three different methyltransferases together with the formate assimilation machinery. Feeding isotopically labelled formate, products with 51 - 81% 13C-labelling could be obtained by maintaining in vivo methylation activity. Focussing on improvement of in vivo methylation, we analysed two further E. coli strains with an engineered C1-metabolism and, following condition optimisation, achieved a doubled methylation activity with a share of more than 70% formate-derived methyl groups. ConclusionsThis study demonstrates the efficient transformation of formate into methyl groups in E. coli. Our findings support that feeding formate can improve the availability of usable C1-compounds and, as a result, increase in vivo methylation activity in engineered E. coli.

biochemistry↗

bioRxiv2024

Expanding the biotechnological scope of metabolic sensors through computation-aided designs

Metabolic sensors are microbial strains modified so that biomass formation correlates with the availability of specific metabolites. These sensors are essential for bioengineering (e.g. in growth-coupled designs) but creating them is often a time-consuming and low-throughput process that can potentially be streamlined by in silico analysis. Here, we present the systematic workflow of designing, implementing, and testing versatile Escherichia coli metabolic sensor strains. Glyoxylate, a key metabolite in (synthetic) CO2 fixation and carbon-conserving pathways, served as the test molecule. Through iterative screening of a compact metabolic model, we identified non-trivial growth-coupled designs that resulted in six metabolic sensors with a wide sensitivity range for glyoxylate, spanning three orders of magnitude in detected concentrations. We further adapted these E. coli strains for sensing glycolate and demonstrated their utility in both pathway engineering (testing a key metabolic module via glyoxylate) and applications in environmental monitoring (quantifying glycolate produced by photosynthetic microalgae). The versatility and ease of implementation of this workflow make it suitable for designing and building multiple metabolic sensors for diverse biotechnological applications. TeaserA streamlined workflow enables the rapid design of versatile E. coli metabolic sensors for detecting key metabolites in bioengineering and monitoring.

synthetic biology↗

bioRxiv2021

Activating silent glycolysis bypasses in Escherichia coli

All living organisms share similar reactions within their central metabolism to provide precursors for all essential building blocks and reducing power. To identify whether alternative metabolic routes of glycolysis can operate in E. coli, we complementarily employed in silico design, rational engineering, and adaptive laboratory evolution. First, we used a genome-scale model and identified two potential pathways within the metabolic network of this organism replacing canonical Embden-Meyerhof-Parnas (EMP) glycolysis to convert phosphosugars into organic acids. One of these glycolytic routes proceeds via methylglyoxal, the other via serine biosynthesis and degradation. Then, we implemented both pathways in E. coli strains harboring defective EMP glycolysis. Surprisingly, the pathway via methylglyoxal immediately operated in a triosephosphate isomerase deletion strain cultivated on glycerol. By contrast, in a phosphoglycerate kinase deletion strain, the overexpression of methylglyoxal synthase was necessary for implementing a functional methylglyoxal pathway. Furthermore, we engineered the serine shunt which converts 3-phosphoglycerate via serine biosynthesis and degradation to pyruvate, bypassing an enolase deletion. Finally, to explore which of these alternatives would emerge by natural selection we performed an adaptive laboratory evolution study using an enolase deletion strain. The evolved mutants were shown to use the serine shunt. Our study reveals the flexible repurposing of metabolic pathways to create new metabolite links and rewire central metabolism.

synthetic biology↗

bioRxiv2021

Novel biochemical, structural and systems insights into inflammatory signaling revealed by contextual interaction proteomics

Protein-protein interactions (PPI) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components, but mostly lack the context of cellular states. However, the generation of contextual representations of PPI networks is essential for structural and systems-level modeling of biological processes and remains an unsolved challenge. In this study we describe an integrated experimental/computational strategy to achieve a contextualized modeling of PPI. This strategy defines the composition, stoichiometry, spatio-temporal organization and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function. Significance StatementIn this work, we propose a critical shift in the way we analyze, and think the study of, protein-protein interactions (PPI), and present an experimental and computational framework to model them in the cellular context. We applied this framework to the signalosome tumor necrosis factor receptor signaling complex (TNF-RSC), and generated an integrated model of its formation and architecture that provides new insights and resolved controversies regarding its organization and regulation. To achieve a contextual modelling of PPIs, we first optimized and developed, and then combined, approaches to map the composition of a target complex, its absolute stoichiometry, its spatial organization and assembly/disassembly dynamics, its temporal dependence on signaling, and its reliance on cellular resources.

systems biology↗

bioRxiv2020

Awakening a latent carbon fixation cycle in Escherichia coli

Carbon fixation is one of the most important biochemical processes. Most natural carbon fixation pathways are thought to have emerged from enzymes that originally performed other metabolic tasks. Can we recreate the emergence of a carbon fixation pathway in a heterotrophic host by recruiting only endogenous enzymes? In this study, we address this question by systematically analyzing possible carbon fixation pathways composed only of Escherichia coli native enzymes. We identify the GED (Gnd-Entner-Doudoroff) cycle as the simplest pathway that can operate with high thermodynamic driving force. This autocatalytic route is based on reductive carboxylation of ribulose 5-phosphate (Ru5P) by 6-phosphogluconate dehydrogenase (Gnd), followed by reactions of the Entner-Doudoroff pathway, gluconeogenesis, and the pentose phosphate pathway. We demonstrate the in vivo feasibility of this new-to-nature pathway by constructing E. coli gene deletion strains whose growth on pentose sugars depends on the GED shunt, a linear variant of the GED cycle which does not require the regeneration of Ru5P. Several metabolic adaptations, most importantly the increased production of NADPH, assist in establishing sufficiently high flux to sustain this growth. Our study exemplifies a trajectory for the emergence of carbon fixation in a heterotrophic organism and demonstrates a synthetic pathway of biotechnological interest.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗