Reconciling molecular regulatory mechanisms with noise patterns of bacterial metabolic promoters in induced and repressed states

• DOI: 10.1073/pnas.1110541108
• Publication Date: 2011-12-22T05:56:38Z
• Authors: Matthew L. Ferguson; Dominique Le Coq; Matthieu Jules; Stéphane Aymerich; Ovidiu Radulescu; Nathalie Declerck; Catherine A. Royer
• Keywords: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences; 570; stochastic gene expression; PROTEIN; promoter activity; central carbon metabolism; COOPERATIVE BINDING; BACILLUS-SUBTILIS; 03 medical and health sciences; gene expression control; Bacterial Proteins; B. subtilis;central carbon metabolism;promoter activity;stochastic gene expression;gene expression control; CCPN; B. subtilis; CENTRAL GLYCOLYTIC GENES; EXPRESSION NOISE; CELL; TRANSCRIPTION; Promoter Regions, Genetic; 0303 health sciences; Models, Genetic; CGGR; Gene Expression Regulation, Bacterial; Carbon; Repressor Proteins; GLUCONEOGENIC GENES; Bacillus subtilis

Assessing gene expression noise in order to obtain mechanistic insights requires accurate quantification of gene expression on many individual cells over a large dynamic range. We used a unique method based on 2-photon fluorescence fluctuation microscopy to measure directly, at the single cell level and with single-molecule sensitivity, the absolute concentration of fluorescent proteins produced from the two Bacillus subtilis promoters that control the switch between glycolysis and gluconeogenesis. We quantified cell-to-cell variations in GFP concentrations in reporter strains grown on glucose or malate, including very weakly transcribed genes under strong catabolite repression. Results revealed strong transcriptional bursting, particularly for the glycolytic promoter. Noise pattern parameters of the two antagonistic promoters controlling the nutrient switch were differentially affected on glycolytic and gluconeogenic carbon sources, discriminating between the different mechanisms that control their activity. Our stochastic model for the transcription events reproduced the observed noise patterns and identified the critical parameters responsible for the differences in expression profiles of the promoters. The model also resolved apparent contradictions between in vitro operator affinity and in vivo repressor activity at these promoters. Finally, our results demonstrate that negative feedback is not noise-reducing in the case of strong transcriptional bursting.