Nattermann, M. | Search bioRxiv

bioRxiv2023

Design, construction and optimization of formaldehyde growth biosensors with broad application in Biotechnology

Formaldehyde is a key metabolite in natural and synthetic one-carbon metabolism as well as an important environmental toxin with high toxicity at low concentrations. To engineer efficient formaldehyde producing enzymes and to detect formaldehyde in industrial or environmental samples, it is important to establish highly sensitive, easy to use and affordable formaldehyde detection methods. Here, we transformed the workhorse bacterium Escherichia coli into biosensors that can detect a broad range of formaldehyde concentrations. Based on natural and promiscuous formaldehyde assimilation enzymes, we designed and engineered three different E. coli strains that depend on formaldehyde assimilation for cellular growth. After in depth characterization of these biosensors, we show that the formaldehyde sensitivity can be improved through adaptive laboratory evolution or modification of metabolic branch points. The metabolic engineering strategy presented in this work allowed the creation of E. coli biosensors that can detect formaldehyde in a concentration range from [~]30 M to [~]13 mM. Using the most sensitive strain, we benchmarked the in vivo activities of different, widely used NAD-dependent methanol dehydrogenases, the rate-limiting enzyme in synthetic methylotrophy. We also show that the strains can grow upon external addition of formaldehyde indicating their potential use for applications beyond enzyme engineering. The formaldehyde biosensors developed in this study are fully genomic and can be used as plug and play devices for screening large enzyme libraries. Thus, they have the potential to greatly advance enzyme engineering and might even be used for environmental monitoring or analysis of industrial probes. Highlights- Conversion of E. coli into three different formaldehyde growth biosensors - Biosensors are fully genomic and grow robustly when formaldehyde is present - Biosensors can detect formaldehyde concentrations ranging from [~]30 M to [~]13 mM - Benchmarking of biotechnological relevant methanol dehydrogenases reveals potential of biosensors for enzyme engineering - Biosensors grow upon direct addition of formaldehyde indicating potential use in environmental or industrial settings

bioengineering↗

bioRxiv2021

A versatile active learning workflow for optimization of genetic and metabolic networks

The study, engineering and application of biological networks require practical and efficient approaches. Current optimization efforts of these systems are often limited by wet lab labor and cost, as well as the lack of convenient, easily adoptable computational tools. Aimed at democratization and standardization, we describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets. We demonstrate our workflow for various applications, from simple to complex gene circuits and metabolic networks, including several cell-free transcription and translation systems, a LacI-based multi-level controller and a 27-variable synthetic CO2-fixation cycle (CETCH cycle). Using METIS, we could improve above systems between one and two orders of magnitude compared to their original setup with minimal experimental efforts. For the CETCH cycle, we explored the combinatorial space of [~]1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system. This allows to identify so far unknown interactions and bottlenecks in complex systems, which paves the way for their hypothesis-driven improvement, which we demonstrate for the LacI multi-level controller that we were able to improve by 34-fold after having identified resource competition as limiting factor. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

synthetic biology↗