News Release

Novel sensor discovered that helps bacteria detect and respond to formaldehyde

The EfgA protein directly senses elevated levels of formaldehyde and stops growth to protect cells

Peer-Reviewed Publication


Novel sensor discovered that helps bacteria detect and respond to formaldehyde

image: A microcolony of Methylorubrum extorquens view more 

Credit: Nkrumah Grant

Bacteria called methylotrophs can use methane and methanol as fuel; in doing so, they produce large amounts of formaldehyde during growth, but until recently no one knew how they detected and responded to this toxic compound. Publishing on 26th May, 2021 in the Open Access journal PLOS Biology, Christopher Marx of the University of Idaho and colleagues describe their discovery of a novel formaldehyde sensor in the bacterium Methylorubrum extorquens, and other methylotrophs.

Some may remember the pungent smell of this toxic chemical from high school dissections of formaldehyde-preserved animals. From bacteria to humans, all organisms produce at least a little formaldehyde as a byproduct of their normal metabolic processes. Methylotrophs, however, make substantially higher amounts of formaldehyde while breaking down certain one-carbon compounds, like methane and methanol, which they use as a source of both carbon and energy.

Marx and his team found the new formaldehyde sensor by growing a methylotroph on increasingly higher formaldehyde concentrations. They sequenced the genomes of bacteria that had evolved to tolerate excess formaldehyde and saw mutations in a previously unknown gene they named efgA, for "enhanced formaldehyde growth." They found that this gene occurs almost exclusively in the genomes of methylotrophs, and that the EfgA protein the gene encodes can detect formaldehyde and quickly stop bacterial growth when levels of the toxic chemical get too high. The researchers also demonstrated that inserting the efgA gene into a non-methylotroph bacterium, E. coli, allowed it to survive at higher-than-normal formaldehyde levels.

Previously, scientists had identified enzymes in all domains of life that detoxify formaldehyde. But this is the first protein sensor described in methylotrophs that can detect formaldehyde and halt growth to prevent cell damage, all without involving detoxifying enzymes. The new discovery may have applications in biotechnology; bacteria engineered to withstand high formaldehyde concentrations with the efgA gene could potentially produce pharmaceuticals and other valuable compounds while growing on methanol, a readily available industrial material.

Dr. Marx notes, "This work has been a surprising outgrowth of a simple question: what does it take for cells to grow directly on formaldehyde? Remarkably, they needed to break a sensor system rather than crank up detoxification. Work is ongoing to further understand the binding specificity of EfgA and homologous proteins as well as to try to move from a hypothetical link between EfgA and translation suggested in several ways in this paper to establishing a direct protein-protein interaction."


Research article

Peer reviewed; Experimental study; Bacteria

In your coverage please use these URLs to provide access to the freely available articles in PLOS Biology:

Citation: Bazurto JV, Nayak DD, Ticak T, Davlieva M, Lee JA, Hellenbrand CN, et al. (2021) EfgA is a conserved formaldehyde sensor that leads to bacterial growth arrest in response to elevated formaldehyde. PLoS Biol 19(5): e3001208.

Funding: This work was supported by funding from the Army Research Office ( ) to CJM (W911NF-12-1-0390). CJM, JSP, CJQ, and FMY were supported by the Center for Modeling Complex Interactions ( sponsored by the National Institute of General Medical Sciences ( ) under award number P20 GM104420. TT, OJB, CJM, JSP and FMY were also supported by National Science Foundation ( ) EPSCoR Track-II under award number OIA1736253. JVB was supported from an award from the BEACON Center for the Study of Evolution in Action ( ) (parent NSF award DBI-0939454). DDN was supported by the Department of Organismic and Evolutionary Biology at Harvard University ( LBL was supported by an INBRE Undergraduate Research Fellowship ( (parent NIH award P20GM103408). Computer resources were provided in part by the Institute for Bioinformatics and Evolutionary Studies Computational Resources Core ( sponsored by the National Institutes of Health (NIH P30 GM103324). This research also made use of the computational resources provided by the high-performance computing center at Idaho National Laboratory, which is supported by the Office of Nuclear Energy of the U.S. DOE and the Nuclear Science User Facilities ( under Contract No. DE-AC07-05ID14517. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

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