Shankar Mukherji, Margaret S. Ebert, Grace X. Zheng, John...

Shankar Mukherji, Margaret S. Ebert, Grace X. Zheng, John S. Tsang, Phillip A. Sharp, and Alexander van Oudenaarden

MicroRNAs can generate thresholds in target gene expression Supplementary Figures and Legends

Supplementary Figure 1 Data processing. (a), Each cell’s raw eYFP and mCherry intensities, either from fluorescence microscopy (not shown) or flow cytometry (shown here), are plotted. (b), Then the background, autofluorescent levels of eYFP and mCherry are subtracted. The background-corrected correlation data are then binned according to eYFP levels, and for each eYFP bin the mean mCherry signal is calculated; this binned curve is depicted in (c).

Nature Genetics: doi:10.1038/ng.905

Supplementary Figure 2 miR-20 expression in Tet-On HeLa cells. (a) Absolute miR-20 expression measured by northern blot. Total RNA from Tet-On HeLa cells transfected with various reporter constructs was probed for miR-20 expression compared to a standard curve of miR-20 mimic spiked into yeast RNA. tRNAgln serves as a loading control. (b) Relative miR-20 expression above and below the threshold measured by RT-PCR. Cells transfected with the N = 7 target reporter or the N = 0 control reporter were sorted into low and high fractions. Total RNA was assayed for miR-20 and normalized to miR-31 as a loading control. Bar height and error bars represent the average relative normalized miR-20 value in the high fraction compared to the low fraction and the s.e.m. of three RT-PCR assays.


Supplementary Figure 3 Dye swap control. The binding site region from the N = 7 reporter was fused onto the 3’ UTR of eYFP instead of mCherry. Cells transfected with this construct were assayed by flow cytometry at 48hr post-transfection.


Supplementary Figure 4 Inhibition of endogenous miR-20 using miRNA sponges. Reporter plasmids were cotransfected with Pol III-driven sponges containing seven CXCR4 control sites or seven bulged miR-20 binding sites. Samples were assayed by flow cytometry at 48hr post-transfection. (a) N = 0 reporter. (b) N = 1 reporter. (c) N = 1 perfect reporter. (d) N = 4 reporter. (e) N = 7 reporter.


Supplementary Figure 5 Fold repression as a function of miRNA abundance. (a) Schematic depicting dual luciferase assay used to measure fold repression in mES cells. The 3’ UTR of Renilla luciferase is re-engineered for each measurement to contain two binding sites for different miRNAs. (b) Fold repression as a function of miRNA concentration in copies per cell.


Supplementary Figure 6 mRNA quantitation above and below the threshold. (a) Cells transfected with the N = 7 target reporter were sorted into low and high fractions separated by the threshold. Plots are in log-log scale. (b) Corresponding low and high fractions were collected from cells transfected with the N = 0 control reporter. (c) RT-PCR from the N = 0 control reporter’s sorted fractions. The absolute mCherry mRNA levels were estimated by making a standard curve using a DNA oligo spanning the mCherry cDNA’s amplicon, spiked into cDNA from untransfected cells. Shown are the average mCherry mRNAs per cell +/- s.e.m. from three RT-PCR assays. The threshold for the N = 7 target reporter is represented by the average mCherry mRNAs per cell present in the corresponding low fraction of the untargeted control reporter. (d) mRNA knockdown above and below the threshold. The relative mCherry mRNA expression in the N = 0 and N = 7 low and high fractions was calculated by normalizing the mCherry RT-PCR signal to the eYFP RT-PCR signal in each fraction. Bar height and error bars indicate the average relative mCherry to eYFP value and s.e.m. of four RT-PCR assays.



MicroRNAs can generate thresholds in target gene expression Supplementary Table List of primers used

Nameofprimer PrimerSequence(5’‐3’)eYFPforwardprimer cggaattcaccatgggccctaaaaagaagcgtaaagtcatggtgagcaagggcgaggeYFPreverseprimer ggaattccatatgttacttgtacagctcgtccatgcmCherryforwardprimer cgggatccaccatgggccctaaaaagaagcgtaaagtcatggtgagcaagggcgaggmCherryreverseprimer ccatcgatttacttgtacagctcgtccatN=1insertreverseprimer atcgattaaagtgcgcgagtgcaggtaccggctcgagttacttgtacagctcgtccatN=4,7insertforwardprimer aataaaagcttccgcctcgagccgN=4,7insertreverseprimer aataagtcgacgcccgcgatccgN=1pfinserttopoligo PagcttctacctgcactataagcactttagN=1pfinsertbottomoligo PtcgactaaagtgcttatagtgcaggtagaN=7eYFPinsertforwardprimer aataacatatgccgcctcgagccgN=7eYFPinsertreverseprimer aataatctagagcccgcgatccgHMGA2UTRforwardprimer aataaatcgatggggcgccgacattcaatttHMGA2UTRreverseprimer aataagatatcactgttccattggacacaagcctttSLC6A1UTRforwardprimer aataaatcgatgccattctgattggacctggSLC6A1UTRreverseprimer aataagatatccttggtgttctgagaataaatccttattgcRT‐PCReYFPforwardprimer ccacctacggcaagctgaccRT‐PCReYFPreverseprimer ggtagcgggcgaagcactRT‐PCRmCherryforwardprimer gaacggccacgagttcgagaRT‐PCRmCherryreverseprimer cttggagccgtacatgaactgagg



MicroRNAs can generate thresholds in target gene expression SUPPLEMENTARY NOTE Molecular titration model of miRNA-mediated gene regulation In order to describe our data, we devised a simple mathematical model of the biochemistry of miRNA-mediated gene regulation. The model is largely similar to models of protein-protein interactions proposed by Buchler and Louis1 as well as models of sRNA regulation of expression proposed by Levine et al.2. The model describes the time evolution of the target mRNA free of miRNA (r) and the target mRNA bound by miRNA (r*) and assumes that the turnover of miRNA is slow compared to the timescale of gene expression so that it can be held constant. The model consists of the following set of coupled, first-order, ordinary differential equations and the conservation relation for miRNA:

rrkmiRNA][r kkdtdr

R*

offonR γ−+−= (1)

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dr*

dt= konr [miRNA]− koffr

* − γR*r* (2)

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[miRNA]T = [miRNA]+ r* (3) For the sake of simplicity, we assume that no translation can occur from the miRNA-bound target mRNA such that for the purposes of protein production it is sufficient to track only the free target mRNA (r). Solving for the steady-state level of r yields:

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r =12[r0 − λ −θ + (r0 − λ −θ)

2 + 4λr0 ] (4)

where:

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r0 =kRγR

(5)


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λ =γR* + koffkon

(6)

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θ =γR*γR[miRNA]total (7)

When the dissociation constant (here denoted by λ) is small – meaning that the interaction strength is high between the miRNA and its target – it is possible to achieve a threshold-linear relationship between the free target mRNA and the total amount of mRNA (denoted by r0, which in the experiments is reported by the eYFP signal). In our case, because we allow recycling of the miRNA following destruction of its bound target mRNA, the titration effect only becomes apparent when the rate at which free miRNAs are removed from the system (kon) is much larger than the rate at which they reappear in the system, which itself consists of two parts: unbinding of the miRNA from its target (koff) and destruction of the target (γR*). In the most extreme case, for example, where kon >> (koff + γR*) such that λ 0 one obtains:

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r =12[r0 −θ + (r0 −θ)

2 ] (8)

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=12

[r0 −θ + r0 −θ ] (9)

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=0 if r0 < θr0 if r0 > θ⎧ ⎨ ⎩

(10)

In this limit, we see that the constant θ sets the level of expression at which the threshold takes place. In order to gain some insight into the effects of a competing pool of miRNA targets p on a single target r, we numerically simulated a set of differential equations similar to equations (1)-(2) but with equations and terms added to reflect binding of the miRNA to the alternative target p. We also modified the conservation relation in (3) to account for the fact that miRNAs could be present as free molecules and bound to either the single target as before or to the alternative target:

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dpdt

= kP − kon,pp [miRNA]+ koff,pp* − γPp (11)


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dp*

dt= kon,pp [miRNA]− koff,pp

* − γP*p* (12)

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[miRNA]T = [miRNA]+ r* + p* (13) From the simulations we could extract the steady state level of the free single target r as a function of r0 as before. We then fit the resulting single target transfer functions to (4) to observe which parameters would be most affected by the presence of alternative targets for the miRNA. We observed that when the relative affinity of the alternative target compared to the single target for the miRNA is changed with the alternate pool size held fixed (as is experimentally done in Fig. 3a), both λ and θ are changed from the values expected from considering a single target in isolation. Thus in the fitting results presented in Figs. 3e and Fig. 3f, we can use the single target model even in the presence of alternative targets but treating λ as the effective single target dissociation constant and and θ as proportional to the free miRNA abundance given the presence of endogenous miR-20 targets. Supplementary References: 1. Buchler, N. & Louis, M. Molecular titration and ultrasensitivity in regulatory networks. J. Mol.

Biol. 384, 1106-1119 (2008). 2. Levine, E., Zhang, Z., Kuhlman, & T. Hwa, T. Quantitative characteristics of gene regulation

by small RNA. PLoS Biol. 5, e229 (2007).


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