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

Abstract

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.

Fig. 1.

(A) Schematic of the central carbon metabolism showing the switch between glycolysis and gluconeogenesis controlled by the repressors CggR and CcpN. Important metabolites are in squares, regulatory proteins are ellipses, and the genes coding for the enzymes are in small italic letters. When glucose is available for cell growth, fructose-1,6-biphosphate (FBP) accumulates and blocks the repressive action that CggR exerts on the transcription of gapA and four other central glycolytic genes (pgk, pgm, eno, and tpi). Because CggR is transcribed from the same gapA operon that it represses, it is also an autorepressor. Inversely, when cells are grown on malate or other nonglycolytic carbon sources, the CcpN repressor is inhibited by an unknown mechanism involving YqfL, allowing expression of the essential gluconeogenic genes gapB and pckA. (B) Schematic of 2psN&B experiments. A stack of 50 raster scans of agarose immobilized live cells of B. subtilis expressing gfpmut3 are recorded using infrared (930 nm) laser excitation and a dwell time of 50 μs at each pixel (faster than GFP diffusion); full scale of fluorescence intensity (F) is 10 photon counts/pixel/50 μs laser dwell time. The fluorescence fluctuations relative to the mean at each pixel are used to calculate the pixel-based maps of the true (shot noise corrected) molecular brightness (ϵ, full scale 1 photon/molecule/50 μs dwell time) and the number (n_pix) of the fluorescent particles detected in the 2-photon excitation volume (vol_ex = 0.07 fL inside B. subtilis); a 3D surface plot of n_pix is shown for the white-delineated area of the above intensity panel. Bottom right: Cartoon representation of the individual cells auto-detected using PaTrack (40) and showing the 50% central pixels used for averaging the particles number in each cell (n_cell); the full scale for the n_pix and n_cell maps is 180 molecules/vol_ex.

Fig. 2.

Cell-by-cell quantification of catabolite regulation in B. subtilis by 2psN&B. (A) Pixel-based fluorescent particles number maps of B. subtilis cells expressing gfpmut3 transcriptional fusion from P_ccgR, P_gapB (results are similar for P_pckA; not shown), and P_ccpN. Cells harvested from liquid cultures containing 0.5% glucose (G) or 0.5% malate (M) as the sole carbon source were immobilized on agarose pads for 2psN&B analysis as described in Fig. 1B. The full scale is 360 molecules/vol_ex. (B) Cell-based particles number (n_cell) distributions for the indicated promoter-gfpmut3 fusion strains grown on glucose (black) or malate (gray). Inset in the first panel shows the expanded histogram of the probability density function P(n_cell) measured in malate for P_cggR. Insets in panel 2 and 3 show the expanded histogram of the probability density function P(n_cell) observed in glucose for the P_gapB and P_pckA promoters in black, and that observed for the background BSB168 strain in gray.

Fig. 3.

Changes in promoter activity levels and noise patterns upon a switch of carbon source. (A) The average number of GFPmut3 molecules per vol_ex ( expressed in micromolar concentration) and its coefficient of variation (the standard deviation over the mean, ) in the cell populations grown on glucose (black bars) or malate (gray bars), estimated from the cell-based particles number distributions shown in Fig. 2B and considering a fixed auto-fluorescence background contribution as determined in the BSB168 receiver strain under identical experimental conditions. (B) Effect of nutrient switch on promoter activity noise patterns. The parameter of stochastic gene expression, the Fano factor b , related to GFP production burst size is plotted against a related to the GFP production burst frequency for the activity of the indicated promoters on glucose (black square) or malate (gray diamonds). The single arrows indicate the sense of repression for the regulated promoters.

Fig. 4.

Model of gene regulation by CggR and CcpN. (A) Scheme describing the architecture of the B. subtilis P_ccgR and P_gapB promoter region (boxed -10 and -35 RNAP recognition sequences) and tandem operator sites (black or gray upward triangles) for the CggR or CcpN repressors. Under glycolytic conditions, CcpN is thought to prevent promoter clearance by the RNA polymerase whereas under gluconeogenic conditions CggR acts as a roadblock to the transcribing polymerase when bound as a compact tetramer. (B) General model of prokaryotic gene expression and regulation applied to both repressors. RNAP-D is the RNAP-bound DNA, R the active repressor, tRNAP the elongating transcription complex, RBS the ribosome binding site on the transcribed mRNA, ElRib the elongating translation complex, and MdGFP the folded and matured green fluorescent protein. According to the above mechanistic models of regulation, besides changes in DNA affinity constants (), CcpN repression affects primarily k₂, the rate at which the elongation complex is formed, whereas CggR would affect the transcription rate in the mRNA leader region (k₃), thereby increasing the dissociation rate of the (paused) polymerase (k₄). In the gfpmut3 reporter system used in this study, all steps past RBS production are identical for all promoter constructs and all conditions. The GFPmut3 variant has been shown to be fast-maturing (within a few minutes) and slow degrading (stable for several hours) in B. subtilis (3), therefore the degradation rate k_deg corresponds to slow dilution whereas the lifetime of the mRNA is much shorter (i.e., k₅≫k_deg). (C) Results of the model compared to the experimental data for the stochastic expression of P_gapBgfp and P_cggRgfp transcriptional fusions under glucose (red) or malate (blue). Lines correspond to the continuous distributions obtained from the model parameters reported in Table S1. The histogram from the P_gapBgfp promoter fusion data was not corrected for the BSB168 background contribution, as the deconvolution cannot be done reliably for experimentally reasonable dataset sizes; instead, a Gamma random variable having the same first two moments as the background contribution has been added to the model predictions.