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A quantitative framework for the forward design of synthetic miRNA circuits

Nature Methods volume 11, pages 1147–1153 (2014)Cite this article

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Abstract

Synthetic genetic circuits incorporating regulatory components based on RNA interference (RNAi) have been used in a variety of systems. A comprehensive understanding of the parameters that determine the relationship between microRNA (miRNA) and target expression levels is lacking. We describe a quantitative framework supporting the forward engineering of gene circuits that incorporate RNAi-based regulatory components in mammalian cells. We developed a model that captures the quantitative relationship between miRNA and target gene expression levels as a function of parameters, including mRNA half-life and miRNA target-site number. We extended the model to synthetic circuits that incorporate protein-responsive miRNA switches and designed an optimized miRNA-based protein concentration detector circuit that noninvasively measures small changes in the nuclear concentration of β-catenin owing to induction of the Wnt signaling pathway. Our results highlight the importance of methods for guiding the quantitative design of genetic circuits to achieve robust, reliable and predictable behaviors in mammalian cells.

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Figure 1: Quantitative relationship between miRNA concentration and gene silencing.
Figure 2: miRNA silencing can be affected by several experimentally determined parameters.
Figure 3: Effect of Gmax on miRNA silencing.
Figure 4: Effect of Km and relative decrease in a on miRNA silencing.
Figure 5: Design and modeling of a protein-responsive miRNA circuit.
Figure 6: A noninvasive sensor for nuclear β-catenin concentration based on a protein-responsive miRNA circuit.

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Acknowledgements

We thank J. Vowles for help with the quantitative western blot protocol, D. Kennedy and J. Liang for help with SPR measurements, and K. Wei and M. Mathur for feedback in the preparation of the manuscript. This work was supported by the US Defense Advanced Research Projects Agency (C.D.S.), the US National Science Foundation (R.J.B.), and the Achievement Rewards for College Scientists Fellowship (R.J.B.).

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Authors and Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, California, USA

    Ryan J Bloom, Sally M Winkler & Christina D Smolke

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  1. Ryan J Bloom
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Contributions

R.J.B. and C.D.S. conceived the project, designed the experiments, analyzed the results and wrote the manuscript. R.J.B. conducted the experiments. S.M.W. performed nuclear extractions and western blots.

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Correspondence to Christina D Smolke.

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Competing interests

C.D.S. has filed a US patent application (number 12/753,778) covering the method described in this paper.

Integrated supplementary information

Supplementary Figure 1 System-level view of a miRNA circuit.

A view of the components and processes involved in a miRNA-based circuit. Various parameters, including mRNA level, miRNA level, and transcript half-life, can affect the levels of miRNA silencing observed. A quantitative framework for predicting the influence of these parameters on the overall target gene silencing is needed for optimizing the design of genetic systems incorporating miRNAs.

Supplementary Figure 2 Overview of method to design and characterize synthetic circuits incorporating microRNAs.

An initial experiment measuring target gene expression with various levels of miRNAs in the cell is performed (Step 1). This data is used to fit the model parameters Km and a (Step 2a) and an additional experiment can be performed to determine ka or Kd between the miRNA switch and the protein of interest (if unknown) (Step 2b). The model can then be used to guide the design of a genetic circuit and inform which parameters need to be modified to achieve the desired quantitative function (Step 3). The genetic circuits can be characterized in a cell line of interest (Step 4).

Supplementary Figure 3 Correlation between transcript and reporter fluorescence levels.

(a) Standard curve for GFP transcript levels. The standard curve was generated by performing qPCR on known amounts of an RNA oligonucleotide corresponding to the portion of the GFP transcript used as the amplicon in qPCR experiments in b. (b) Relationship between GFP transcript and fluorescent reporter levels. qPCR was performed on RNA purified from cells expressing various levels of the GFP reporter gene. The standard curve in a was used to convert the Ct levels determined from the qPCR experiments into numbers of GFP transcripts per reaction. These numbers were divided by the number of cells in each reaction to arrive at the number of transcripts per cell. These values are plotted against the corresponding GFP fluorescence values obtained through flow cytometry analysis of the same cell population. In both graphs, error bars represent ±1 s.d. of three independent experiments.

Supplementary Figure 4 Standard curves for miR Taqman qPCR assays.

(a–c) The standard curves, created by Taqman custom small RNA assays, are shown for miR-GFP (a), miR-BFP (b) and miR-DsRed (c). Standard curves were generated using chemically synthesized RNA oligonucleotides corresponding to the mature miRNA sequence of the indicated miRNAs. Dilutions of each oligonucleotide were made in lysate from cells lacking the miRNA of interest, such that 5 μl of the control oligonucleotide was added into each RT reaction. The data represent measured Ct values for different dilutions of the control. The line indicates the best-fit line to the experimental data. Error bars represent s.d. from at least three independent experiments.

Supplementary Figure 5 Catalytic ribozymes alter the kdeg value of the target transcript.

Engineered hammerhead ribozymes were used to control the rate of degradation (kdeg) of the GFP reporter transcript. In one construct (Rz1), a ribozyme was placed in the 3′ UTR of a GFP expression cassette that incorporated the promoter P2 (P2-GFP-Rz1). In the second construct (Rz2), three copies of Rz1 were placed in the 3′ UTR of a GFP expression cassette that incorporated the promoter P3 (P3-GFP-Rz2). By taking the ratio of GFP levels with an inactive control ribozyme to the active ribozyme construct, we calculated 1.94- and 4.07-fold increases in kdeg for Rz1 and Rz2 constructs corresponding to an a of 0.039 and 0.082, respectively. Error bars represent ±1 s.d. of at least three independent experiments.

Supplementary Figure 6 Protein-responsive miRNA switch design.

(a–c) The sequence and secondary structures are shown for the control GFP-targeting miRNA (miR-EGFP) (a), the MS2-responsive GFP-targeting miRNA (miR-EGFP-MS2) (b), and the β-catenin-responsive GFP-targeting miRNA (miR-EGFP-β-cat) (c). Protein-responsive miRNAs were designed based on a modification of a previously outlined strategy for small molecule-responsive miRNAs. In this work, we developed an improved integration strategy that imparted a reduced effect on the processing of the pri-miRNA and greater flexibility to the incorporation of diverse aptamer structures. Specifically, RNA aptamers responsive to the protein ligands were integrated into the side of the basal loop of miR-EGFP-M1. Aptamer-protein binding interactions sterically inhibit proper biogenesis by the Microprocessor complex and subsequent gene silencing.

Supplementary Figure 7 Binding kinetics of protein-responsive miRNAs.

Representative response curves associated with the surface plasmon resonance (SPR)-based in vitro binding assay for the protein-responsive miRNA switches. The kinetic binding constants of the miRNA switch with the protein ligand are determined from a 1:1 binding model fit to the response curves. (a) Response curves between the miR-EGFP-MS2 and the MS2 ligand across a range of ligand concentrations. Binding and dissociation constants were determined as ka: 6.10 × 106 M min-1, kd: 1.34 × 10–2 min–1, Kd: 2.2 nM (b). Response curves between the miR-EGFP-β-cat and the β-catenin ligand across a range of ligand concentrations. Binding and dissociation constants were determined as ka: 8.4 × 105 M–1 min–1, kd: 2.66 × 10–3 min–1, KD: 3.17 nM. (c) Response curves between the MS2 aptamer and the MS2 ligand across a range of ligand concentrations. Binding and dissociation constants were determined as ka: 7.70 × 106 M min–1, kd: 1.07 × 10–2 min–1, KD: 1.4 nM. (d) Response curves between the β-catenin aptamer and the β-catenin ligand across a range of ligand concentrations. Binding and dissociation constants were determined as ka: 7.5 × 105 M–1 min–1, kd: 2.52 × 10–3 min–1, KD: 3.36 nM. Graphs are representative of at least two independent experiments.

Supplementary Figure 8 Representative western blot images.

(a) Representative western blot image and analysis to quantify MS2 levels in the nuclear fraction of HEK293 cells. At least four lanes of serially diluted protein standard and three lanes of serially diluted nuclear lysate samples were analyzed in each experiment. The number of MS2 molecules per cell is obtained by dividing the slope of the experimental line (intensity/cell) by the slope of the standard line (intensity/mg). The MS2 levels are calculated as 0.091 pM/cell for the data shown. (b–e) Representative images for quantitative western blots are shown for cells expressing the miRNA switch ligand MS2 induced with 50 ng/mL (b), 3.2 ng/mL (c), 0.8 ng/mL (d), and 0.2 ng/mL (e) doxycycline. (f,g) Representative images for quantitative western blots are shown cells expressing the miRNA switch ligand β-catenin with no Wnt3A (f) and Wnt3A induction (300 ng/mL) (g). Wedges represent increasing amounts of protein standard (Standard) or cell lysate (Lysate). Standards contained at least five serial dilutions. Images are representative of at least two independent experiments for each condition.

Supplementary Figure 9 Model predictions and experimental data demonstrating the role of Km on the signal-to-noise output of the nuclear protein concentration sensor.

(a–c) Predicted relationship between sensor signal (% GFP expression) and mature miRNA levels for different Km values. The relative change in miRNA levels (132 pM to 87 pM) and corresponding sensor signal from the experimental data are indicated on each graph. Red delta indicates sensor signal from model prediction. (d–f) Experimental determination of the change in sensor signal (% GFP expression) upon induction of the Wnt signaling pathway by the addition of Wnt3A to the cell culture media. Black bars: 0 ng/mL Wnt3A; grey bars: 300 ng/mL Wnt3A. T = 1, Km = 4,750 pM (a,d), T = 3, Km = 1,580 pM (b,e); T = 5, Km = 950 pM (c,f). Black delta represents experimentally measured signal change in GFP expression after addition of Wnt3A. P values for the experimental data are: P = 0.0148 (a), P = 0.068 (b) and P = 0.168 (c). Error bars represent ±1 s.d. of at least three independent experiments.

Supplementary Figure 10 Base plasmids used to create all plasmids stably integrated into HEK293 T-REx cells.

pCS2407 and pCS2892 were used to create the vectors which were subsequently stably integrated into HEK293 T-REx cells. Each of the plasmids contained a hygromycin resistance gene flanked by FRT recombination sites. miRNAs were cloned behind the DsRed-Express coding region using the AgeI and ClaI restriction sites.

Supplementary Figure 11 Flow cytometry plots for HEK293 T-REx cells stably expressing miRNA constructs.

(a) Plot showing side scatter vs. electronic volume for HEK293 cells which was used to initially screen for viability. (b) A cell line lacking GFP was used to set the gate between the GFP negative population and GFP positive population. The GFP positive population was used in further data analysis. (c–f) Representative histograms depicting GFP expression from HEK293 T-REx cell lines used in MS2-responsive miRNA circuits. Black: cell population expressing a MS2-responsive miRNA (pCS2892); red: cell population expressing no miRNA (pCS2891). Doxycycline was added to the growth media to induce MS2 expression in the cell lines at the following concentrations: 0 ng/mL (c),.08 ng/mL (d), 3.2 ng/mL (e), 50 ng/mL (f) doxycycline. (g–y) Representative flow cytometry histograms for pCS2407 (g), pCS2873 (h), pCS2874 (i), pCS2879 (j), pCS2880 (k), pCS2881 (l), pCS2882 (m), pCS2883 (n), pCS2884 (o), pCS2885 (p), pCS2886 (q), pCS2887 (r), pCS2888 (s), pCS2889 (t), pCS2890 (u), pCS2894 (v), pCS2895 (w), pCS2875 (x), pCS2876 (y).

Supplementary Figure 12 Time course data.

(a) Time course data for miR-GFP silencing. Circuit design is identical to that shown in Figure 2a. Doxycycline (50 ng/mL) was added to the media on day 0 to induce miRNA expression. (b) Time course data for MS2-responsive miRNA. Circuit design is identical to that shown in Figure 6a. Doxycycline (50 ng/mL) was added to the media on day 0 to induce protein ligand expression. GFP levels were measured using flow cytometry. Error bars represent ±1 s.d. of at least three biological replicates.

Supplementary Figure 13 Un-normalized miRNA silencing data.

Un-normalized fluorescent data are presented for all constructs presented in the main text. In all plots, black markers represent fluorescent expression with the indicated microRNA circuit and grey markers represent a control construct with no miRNA. Normalized data was calculated by dividing the GFP levels obtained from the circuit with the miRNA by the GFP levels in the corresponding circuit with no miRNA for each concentration of doxycycline. Doxycycline concentrations are shown on the x-axis on a log2 scale. The graphs shown represents data from one copy of miR-GFP (a), four copies of miR-GFP (b), miR-DsRed (c), miR-BFP (d), miR-GFP with T = 3 (e), miR-GFP with T = 5 (f), miR-GFP with Rz1 in the 3′ UTR of GFP (g), miR-GFP with Rz2 in the 3′ UTR of GFP (h), miR-MS2-GFP (i).

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Bloom, R., Winkler, S. & Smolke, C. A quantitative framework for the forward design of synthetic miRNA circuits. Nat Methods 11, 1147–1153 (2014). https://doi.org/10.1038/nmeth.3100

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