Deconvolution of Gene Expression Noise into Spatial Dynamics of Transcription Factor–Promoter Interplay

Gene
expression noise is not only the mere consequence of stochasticity,
but also a signal that reflects the upstream physical dynamics of
the cognate molecular machinery. Soil bacteria facing recalcitrant
pollutants exploit noise of catabolic promoters to deploy beneficial
phenotypes such as metabolic bet-hedging and/or division of biochemical
labor. Although the role of upstream promoter–regulator interplay
in the origin of this noise is little understood, its specifications
are probably ciphered in flow cytometry data patterns. We studied Pm promoter activity of the environmental bacterium Pseudomonas putida and its cognate regulator XylS by following
expression of Pm-gfp fusions in single cells. Using
mathematical modeling and computational simulations, we determined
the kinetic properties of the system and used them as a baseline code
to interpret promoter activity in terms of upstream regulator dynamics.
Transcriptional noise was predicted to depend on the intracellular
physical distance between regulator source (where XylS is produced)
and the target promoter. Experiments with engineered bacteria in which
this distance is minimized or enlarged confirmed the predicted effects
of source/target proximity on noise patterns. This approach allowed
deconvolution of cytometry data into mechanistic information on gene
expression flow. It also provided a basis for selecting programmable
noise levels in synthetic regulatory circuits.