Kinetic modeling reveals additional regulation at co-transcriptional level by post-transcriptional sRNA regulators

Abstract

Small RNAs (sRNAs) are important gene regulators in bacteria. Many sRNAs act post-transcriptionally by affecting translation and degradation of the target mRNAs upon base-pairing interactions. Here we present a general approach combining imaging and mathematical modeling to determine kinetic parameters at different levels of sRNA-mediated gene regulation that contribute to overall regulation efficacy. Our data reveal that certain sRNAs previously characterized as post-transcriptional regulators can regulate some targets co-transcriptionally, leading to a revised model that sRNA-mediated regulation can occur early in an mRNA's lifetime, as soon as the sRNA binding site is transcribed. This co-transcriptional regulation is likely mediated by Rho-dependent termination when transcription-coupled translation is reduced upon sRNA binding. Our data also reveal several important kinetic steps that contribute to the differential regulation of mRNA targets by an sRNA. Particularly, binding of sRNA to the target mRNA may dictate the regulation hierarchy observed within an sRNA regulon.

Keywords: bacterial small RNA; fluorescence microscopy; gene regulation; mathematical modeling.

Figure 1.. Model of sRNA-mediated regulation in vivo

(A) Kinetic model describing sRNA-mediated, post-transcriptional regulation. (B) Ordinary differential equation (ODE) for post-transcriptional regulation model. (C) Kinetic model for co-transcriptional regulation. RNAP refers to RNA polymerase. (D) ODE for co-transcriptional regulation model. Parameters are described in the text. Dashed blue and red boxes enclose the pathways in the absence and presence of sRNA, respectively.

Figure 2.. Illustration of experimental setup and representative results

(A) Illustration of the target mRNA, including the SgrS binding site region from the endogenous mRNA target and a coding region for sfGFP reporter. Scale bar represents 5 μm. (B) Representative images of SgrS (red), ptsG-sfGFP mRNA (green), and sfGFP signal (blue) in the absence (upper) or presence (lower) of sRNA induction. (C) Measured sRNA, mRNA, and protein levels from images in (B). Points with error bars represent standard deviation (SD) from 2–3 biological replicates, with each replicate containing ~500–1,000 cells.

Figure 3.. Fitting of SgrS regulation of ptsG expression with post-transcriptional and co-transcriptional regulation models

(A and B) Time-dependent changes of SgrS, ptsG-sfGFP mRNA, and sfGFP levels in the presence or absence of SgrS and in the WT rne background or rne701 background, fit with (A) post-transcriptional regulation model using one-step transcription module, or (B) co-transcriptional regulation model with two-step transcription module. Points with error bars represent SD of experimental data from 2–3 biological replicates, each containing ~500–1,000 cells. Black lines represent best fits. Shaded, colored regions represent predicted error of the fitting, calculated by sampling 100 sets of kinetic parameters from the posterior distribution and plotting the associated curves over the observed data.

Figure 4.. qRT-PCR measurement of the D/U ratio

(A) Schematic illustration of the qPCR primer binding sites relative to SgrS binding site on the mRNA. (B) Schematic illustration of total RNAs extracted at different time points of mRNA induction, which contain different ratios of nascent mRNAs to fully transcribed mRNAs. (C) Reduction in the D/U ratio of ptsG-sfGFP mRNA affected by SgrS (D/U(+/−), defined by the ratio of D/U(+) [in the presence of SgrS] to D/U(−) [in the absence of the SgrS]). (D) D/U(+/−) of ptsG-sfGFP mRNA is unaffected by RyhB. (E) D/U(+/−) of ptsG-sfGFP mRNA affected by SgrS with addition of bicyclomycin (BCM). 50 μg/mL BCM was added 15 min before the time of cell collection, i.e., at t = −14 min relative to mRNA induction for cells collected at t = 1 min, and t = 0 relative to mRNA induction for cells collected at t = 15 min. For each dataset, the SDs of D/U(−) and D/U(+) were calculated, each from 3–8 biological replicates. The reported error bars for D/U(+/−) were propagated SD, calculated using standard error propagation formulas. Significance values from two-sample t tests are reported above error bars.

Figure 5.. Co-transcriptional regulation model outperforms the co-transcriptional model in predicting the kinetics of sRNA regulation

(A and B) Simulated prediction (black curve with shaded, colored region) using co-transcriptional regulation model for validation dataset with (A) reduced αMG concentration for SgrS induction, and (B) pre-induced mRNA, overlaid with experimental data (points with error bars representing SD from 2–3 biological replicates, each containing ~500–1,000 cells). (C and D) Simulated prediction using post-transcriptional regulation model for validation dataset with (C) reduced αMG concentration for SgrS induction and (D) pre-induced mRNA.

Figure 6.. Kinetic parameters that contribute to regulation efficiency of sRNA over different targets

(A) Parameters of sRNA regulation over different mRNA targets. Error bars represent SD from 2–3 biological replicates, each containing ~500–1,000 cells. p values for two-sample t tests are provided for pairwise comparisons. *p < 0.0001. (B) Protein-level repression heatmap, calculated by screening across the listed parameters. Repression level of 1 represents complete repression of protein expression; 0 means no repression. For the left panel, β_e = 1.0 × 10⁻³ S⁻¹ and p = 0.32; for the middle panel, k_xs/k_x = 0.5 and p = 0.32; for the right panel, k_xs/k_x = 0.5 and β_e = 1.0 × 10⁻³ S⁻¹. For all simulations, k_init, k_x, k_off, β_m, β_ms, β_s, and α_s are set to the measured or maximum a posteriori (MAP) values for ptsG (Tables S2 and S3). (C) k_on versus k_x for all mRNA targets. Error bars represent SD of calculated MAP values (Tables S2 and S3).

Figure 7.. Model for co-transcriptional regulation by sRNAs

(A) sRNAs can freely diffuse into the nucleoid region of bacterial cells and bind to the target mRNAs as soon as the sRNA binding site is transcribed. Binding of sRNA affects transcriptional-coupled translation and increases the binding of Rho, thereby terminating transcription. Recruitment of RNase E through its C-terminal scaffold region positively contributes to the efficiency of co-transcriptional regulation. (B–E) Representative SMLM images of SgrS in the absence of ptsG-sfGFP mRNA induction (30 min after sRNA induction, before mRNA induced) (B) and in the presence ptsG-sfGFP mRNA induction (54 min after sRNA induction, 24 min after mRNA induction) (C). Representative SMLM images of RyhB in the absence (D) and presence (E) of sodB₁₃₀-sfGFP induction. Red spots are sRNA signals detected by SMLM imaging. Blue area represents DAPI-stained nucleoid region. (F) Enrichment of SgrS and RyhB in the absence and presence of the target mRNA in the nucleoid and cytoplasm regions. Error bars represents SD from 2–3 biological replicates, each containing ~100 cells.