Back

A rationally designed c-di-AMP FRET biosensor to monitor nucleotide dynamics

Pollock, A. J.; Choi, P. H.; Zaver, S. A.; Tong, L.; Woodward, J. J.

2021-02-11 bioengineering
10.1101/2021.02.10.430713 bioRxiv
Show abstract

33-cyclic di-adenosine monophosphate (c-di-AMP) is an important nucleotide second messenger found throughout the bacterial domain of life. C-di-AMP is essential in many bacteria and regulates a diverse array of effector proteins controlling pathogenesis, cell wall homeostasis, osmoregulation, and central metabolism. Despite the ubiquity and importance of c-di-AMP, methods to detect this signaling molecule are limited, particularly at single cell resolution. In this work, crystallization of the Listeria monocytogenes c-di-AMP effector protein Lmo0553 enabled structure guided design of a Forster resonance energy transfer (FRET) based biosensor, which we have named CDA5. CDA5 is a fully genetically encodable, specific, and reversible biosensor which allows for the detection of c-di-AMP dynamics both in vitro and within live single cells in a nondestructive manner. Our initial studies identify a unimodal distribution of c-di-AMP in Bacillus subtilis which decreases rapidly when cells are grown in diluted Luria Broth. Furthermore, we find that B. subtilis mutants lacking either a c-di-AMP phosphodiesterase or cyclase have respectively higher and lower FRET responses, again in a unimodal manner. These findings provide novel insight into c-di-AMP distribution within bacterial populations and establish CDA5 as a powerful platform for characterizing new aspects of c-di-AMP regulation. ImportanceC-di-AMP is an important nucleotide second messenger for which detection methods are severely limited. In this work we engineer and implement a c-di-AMP specific FRET biosensor to remedy this dearth. We present this biosensor, CDA5, as a versatile tool to investigate previously intractable facets of c-di-AMP biology.

Matching journals

The top 7 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.