ArticleMicrobial biotechnology2024
Design, construction and optimization of formaldehyde growth biosensors with broad application in biotechnology.
Article in Microbial biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- 2-Keto-3-deoxy-D-xylonate and 2-oxo-4-hydroxybutyrate as natural and artificial effectors of transcription factors regulating D-xylonate operons in E. coli.Applied microbiology and biotechnology · 2026Article
- Two highly specific growth-coupled biosensor for glycolaldehyde detection across micromolar and millimolar concentrations.Synthetic biology (Oxford, England) · 2025Article
- Overview of Phage Defense Systems in Bacteria and Their Applications.International journal of molecular sciences · 2024Review
- Design, construction and optimization of formaldehyde growth biosensors with broad application in biotechnology.Microbial biotechnology · 2024Article
- The Serine Shunt enables formate conversion to formaldehydeSustainable microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Formaldehyde is a key metabolite in natural and synthetic one-carbon metabolism. To facilitate the engineering of formaldehyde-producing enzymes, the development of sensitive, user-friendly, and cost-effective detection methods is required. In this study, we engineered Escherichia coli to serve as a cellular biosensor capable of detecting a broad range of formaldehyde concentrations. Using both natural and promiscuous formaldehyde assimilation enzymes, we designed three distinct E. coli growth biosensor strains that depend on formaldehyde for cell growth. These strains were engineered to be auxotrophic for one or several essential metabolites that could be produced through formaldehyde assimilation. The respective assimilating enzyme was expressed from the genome to compensate the auxotrophy in the presence of formaldehyde. We first predicted the formaldehyde dependency of the biosensors by flux balance analysis and then analysed it experimentally. Subsequent to strain engineering, we enhanced the formaldehyde sensitivity of two biosensors either through adaptive laboratory evolution or modifications at metabolic branch points. The final set of biosensors demonstrated the ability to detect formaldehyde concentrations ranging approximately from 30 μM to 13 mM. We demonstrated the application of the biosensors by assaying the in vivo activity of different methanol dehydrogenases in the most sensitive strain. The fully genomic nature of the biosensors allows them to be deployed as "plug-and-play" devices for high-throughput screenings of extensive enzyme libraries. The formaldehyde growth biosensors developed in this study hold significant promise for advancing the field of enzyme engineering, thereby supporting the establishment of a sustainable one-carbon bioeconomy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.