Back

A hybrid nitrogenase with regulatory elasticity in Azotobacter vinelandii

Rivier, A. J.; Myers, K. S.; Garcia, A. K.; Sobol, M. S.; Kacar, B.

2023-06-03 microbiology
10.1101/2023.06.02.543473 bioRxiv
Show abstract

Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs restrict the available sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred, ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to nitrogenase sequence-level perturbations. Rather, physiological compatibility with the ancestral nitrogenase variant is restored by reducing trace metal and electron resource allocation to nitrogenase. Our results spotlight cellular processes adjacent to nitrogen fixation as productive engineering targets to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs. IMPORTANCEAzotobacter vinelandii is a key model bacterium for the study of biological nitrogen fixation, an important metabolic process catalyzed by nitrogenase enzymes. Here, we demonstrate that compatibilities between engineered A. vinelandii strains and remodeled nitrogenase variants can be modulated at the regulatory level. Engineered cells respond by adjusting expression of proteins involved in cellular processes adjacent to nitrogen fixation, rather than that of nitrogenase proteins themselves. These insights can inform future strategies to transfer nitrogenase variants to non-native hosts.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.