Post trancriptional regulation by microrna s
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Post-trancriptional Regulation by microRNA’s. Herbert Levine Center for Theoretical Biological Physics, UCSD with: E. Levine , P. Mchale, and E. Ben Jacob (Tel-Aviv) Outline: Introduction Basic model Spatial sharpening Temporal Sequencing. What are MicroRNA’s?.

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Post-trancriptional Regulation by microRNA’s

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Post trancriptional regulation by microrna s

Post-trancriptional Regulation by microRNA’s

Herbert Levine

Center for Theoretical Biological Physics, UCSD

with: E. Levine, P. Mchale, and E. Ben Jacob (Tel-Aviv)

Outline: Introduction

Basic model

Spatial sharpening

Temporal Sequencing


What are microrna s

What are MicroRNA’s?

  • MicroRNA’s (miRNA’s) are small noncoding RNA molecules that regulate eukaryotic gene expression at the translation level

RISC = RNA-induced Silencing Complex


Microrna formation

MicroRNA formation

miRNA’s are processed from several precursor stages

Mammalian genomes seem to have 100’s of miRNA’s


This talk

This talk

  • Basic molecular model

  • Local vs global parameters

  • Spatial sharpening

  • Temporal control


Basic silencing model

Basic silencing model

Bare messenger RNA

Bound miRNA-mRNA

Processed state

Second step reflects the fact that complex is not just degraded directly, but is targeted to a specialized location (a cytoplasmic P-body) to stop translation

Binding- local rates; transport - global rates


Basic silencing model ii

Basic silencing model II

  • Simple to analyze this in steady-state

  • Critical parameter q - how much miRna is degraded per degraded mRNA (in processed state)

    • q=0 miRNA is completely recycled (catalytic mode)

    • q>0 miRNA is partially degraded (stoichiometeric)

    • q<0 amplification (occurs for siRNA)


Results

Results

Effective equations:

with

Effective silencing requires that αs > Qαm+, where

=ms/.

Sharp silencing threshold


Threshold effect

Threshold Effect

Cartoon vs Reality

  • RyhB miRNA regulation of sodB

  • Threshold-linear units, similar to some neuron models

  • Also, fluctuations reduced in silenced state

  • From E. Levine, T. Hwa lab


Local vs global parameters

Local vs. Global parameters

  • Data on silencing has been very controversial, with disagreements as to whether there is both mRNA and protein repression or only protein repression

  • In our model, the repression ratio can be altered by cell state (global) variables such as the transport into and out of the processed state, and miRNA loss (q)


Local vs global parameters1

Local vs. Global parameters

Global control through the effective parameter

Gives different repression ratios for same system of miRNA and target, different cellular context


Local vs global parameters2

Local vs. Global parameters

  • Different protocols can give opposite answers if these are not carefully controlled

    • Simple physics but complex biology

Complex interplay of local

and global parameters


Spatial sharpening

Spatial sharpening

  • What happens if we have a miRNA expressed with the opposite spatial pattern from its target mRNA?

    • Motivation: Complementary expression patterns

  • And, the miRNA might diffuse from cell to cell

    • Motivation - intercellular transport of siRNA in plants

    • Could this be an actively maintained front with q<0?

Voinnet

(2005)

D Kosman et al, Science (2006)

Iba4 vs Hoxb8 - Ronshaugen et al. Genes Dev. 2005;


Conceptual idea

Sharpening the target expression pattern.

Conceptual idea

The model predicts that mobile microRNA (red) fine-tune this pattern by establishing a sharp interface in the target expression profile (green).

Morphogens set up a poorly defined expression domain, where mRNA levels (green) vary smoothly across the length of the embryo.


Spatial model

Spatial model

  • Note - eq has been rescaled using

    We will assume that the transcription profiles are 1d functions, decaying in opposite directions, and investigate what are the resultant mRNA and miRNA

    The relevant parameters are the annihilation rate k and the miRNA diffusion constant D (compared to the scale established by transcription)


Zero diffusion large k

Zero diffusion, large k

Crossing point at


Adding mirna diffusion

Adding miRNA diffusion

  • K=10000

  • Dark line is analytic calculation

  • Interface is sharpened

  • Crossing point is shifted to left


Effect of increasing rate k

Effect of increasing rate k

In the large k and/or small D limit, there is a sharp transition layer

Diffusion of miRNA eats into m profile, and m has a sharp drop


Analytic solution

Analytic solution

No miRNA flux is allowed into the region x<xt

The zero flux Green’s function is clearly

The miRNA profile is given by

And the interface is determined by setting miRNa = 0 (no fluctuations). Once this position is determined, we still have to the left


Comments

Comments

  • Sharp stripes are also possible


Comments1

Comments

  • Can be tested with genetic mosaics


Stability analysis

Stability Analysis

  • Can extend analysis to time-dependent case

  • Now, miRNA equation becomes

  • Linearizing around steady-state gives simple result

implies


Response to 2d quenched noise

Response to 2d quenched noise

Analytically: Low-pass filter due to diffusion


C elegans development

C. Elegans development

Lin4 and Let7 miRNAs control differentiation

As usual, they act by silencing targets

Is there any good reason why miRNA’s are used for this task?


Mirna as temporal regulator

miRNA as temporal regulator

  • Lin-28 needed for start of L2 phase; needs to be turned off later than Lin-14

  • Basic idea - one miRNA target has 5 binding sites (lin-14) and one has only 1 (lin-28)

  • If miRNA act stoichiometrically, first target will soak up all the miRNA’s and the second one will not be repressed until later


The complete circuit

The complete circuit

  • Direct positive feedback

  • Indirect positive feedback

  • Double-negative feedback

  • miRNA switches g5 into off state and this then makes g1 also switch to off state

  • This works better in stoichiometric mode, as g1 is not repressed until g5 stops absorbing s

Experimentally, lin-14 inhibits an inhibitor of lin-28 which is independent of lin-4; and vice versa


Positive feedback

Positive feedback

Stoichiometric mode

Catalytic mode

Thin lines - simple miRNA repression

Thick lines - with bistable behavior due to feedback

Dashed lines - reduced feedback

note temporal ordering


The complete circuit1

The complete circuit

  • Direct positive feedback

  • Indirect positive feedback

  • Double-negative feedback

  • miRNA switches g5 into off state and this then makes g1 also switch to off state

  • This works better in stoichiometric mode, as g1 is not repressed until g5 stops absorbing s

Experimentally, lin-14 inhibits an inhibitor of lin-28 which is independent of lin-4; and vice versa


Final results

Final results

Solid lines: catalytic

Dashed: Stoichiometric

Precise temporal staging is made easier by miRNA


Summary

Summary

  • microRNA’s are yet another level of genetic regulation

  • In nature, miRNA’s seem to be able to regulate both spatial and temporal aspects of development

  • We have argued that the stoichiometric mode of operation seems to be an enabling factor

  • Is this easier to arrange and control (via cell state) than equivalent transcription circuits?? Is it easier to target many genes simultaneously??


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