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Competition between target sites of regulators shapes post-transcriptional gene regulation

Nature Reviews Genetics volume 16, pages 113–126 (2015)Cite this article

Subjects

Key Points

  • Post-transcriptional regulators of gene regulation, such as RNA-binding proteins (RBPs) and microRNAs (miRNAs), often have many thousands of binding sites in the transcriptome. Transcription renders the concentrations of these binding sites highly dynamic.

  • To understand their function, we consider how binding site occupancy is determined by the composition of the transcriptome and by the regulator concentration in a simple steady-state model.

  • Competition between many binding sites prevents saturation. This buffering helps to explain how changes in the expression of an RBP or a miRNA can regulate targets with different affinities inside a cell.

  • Expression of many strong binding sites can reduce occupancies by sequestration (that is, the 'sponge' effect). However, the larger the number of competing binding sites, the weaker the crosstalk between individual transcripts.

  • A sponge requires approximately double the number of binding sites in order to be effective. For miRNAs this requires tens of thousands of additional binding sites, which is unlikely for typical mRNAs.

  • Crosstalk between mRNAs could be enhanced if local concentrations deviate strongly from the average or if multiple sites cooperate.

Abstract

Post-transcriptional gene regulation (PTGR) of mRNA turnover, localization and translation is mediated by microRNAs (miRNAs) and RNA-binding proteins (RBPs). These regulators exert their effects by binding to specific sequences within their target mRNAs. Increasing evidence suggests that competition for binding is a fundamental principle of PTGR. Not only can miRNAs be sequestered and neutralized by the targets with which they interact through a process termed 'sponging', but competition between binding sites on different RNAs may also lead to regulatory crosstalk between transcripts. Here, we quantitatively model competition effects under physiological conditions and review the role of endogenous sponges for PTGR in light of the key features that emerge.

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Figure 1: RBPs and miRNAs regulate protein output and mRNA fate.
Figure 2: RNA competition.
Figure 3: Steady-state model of the transcriptome to study RNA competition effects.
Figure 4: Quantitative modelling of competition effects for miR-20a binding.
Figure 5: An overview of competition effects.

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Acknowledgements

The authors acknowledge discussions with I. Legnini (Bozzoni laboratory) and J. Schmiedel (van Oudenaarden, Bluethgen and Marks laboratories). They thank P. Zamore for providing feedback on the manuscript, and J. Berg for help with deriving the self-consistency solution for the steady-state model from first principles. They also thank M. Landthaler for input. M.J. thanks the Deutsche Forschungsgemeinschaft for a fellowship in the International Research Training Group Genomics and Systems Biology of Molecular Networks/CSB (GRK 1360) and the BMBF-funded DEEP programme (01KU1216D).

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  1. Laboratory of Systems Biology of Gene Regulatory Elements, Max Delbrück Center for Molecular Medicine, Robert Rössle Strasse 10, Berlin, 13092, Germany

    Marvin Jens & Nikolaus Rajewsky

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  1. Marvin Jens
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  2. Nikolaus Rajewsky
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Correspondence to Nikolaus Rajewsky.

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Glossary

Competition

Competition occurs if different ligands can form a complex with the same molecule (for example, an RNA-binding protein). In biochemistry this is often used to characterize an interaction. However, in biochemistry the term 'competition' describes a situation with multiple available species of ligands, regardless of whether these are bound or unbound, which essentially means that 'alternatives are present'. In this Analysis, we use the term for situations in which the binding factor is limited and distributed among many possible ligands (binding sites); that is, we refer to the unbound ligands as 'competing'. This is closer to the intuitive meaning used to describe, for example, markets or sports, where competition is introduced by either limited money or limited trophies, which cannot be awarded to everybody.

Binding equation

At equilibrium, the binding equation gives the site occupancy (Θ) as a function of free ligand concentration (F) and the dissociation constant (Kd). More generally, it describes a system with components that can only be in one of two states, which differ in energy (in this context, bound or unbound). The equation therefore arises in many contexts and is known, for example, as Langmuir isotherm or Fermi function. As we consider non-cooperating, independent binding sites, the binding equation is equivalent to the more general Hill equation, with the Hill coefficient equal to 1.

Occupancy

The probability with which a particular binding site is bound by a regulator, for example, an RNA-binding protein.

Competing endogenous RNA (ceRNA) hypothesis

In its current form, a hypothesis stating that competition for microRNA binding can introduce crosstalk between RNA transcripts, including mRNAs and pseudogenes.

Binding energies

The energies of molecules in a complex, which are contributed by the physical interactions (for example, hydrogen bonding) between them. It is often expressed in kcal mol−1; it determines the dissociation constant (Kd), which describes the concentration at which binding and unbinding are in equilibrium.

CLIP–seq

(Crosslinking and immunoprecipitation followed by sequencing). A biochemical technique to extract RNA-binding protein (RBP)-bound fragments of RNA with high specificity and sensitivity, which are then subjected to high-throughput sequencing to map RBP interactions transcriptome-wide at nucleotide resolution.

Argonaute

(AGO). A functional protein component of the microRNA (miRNA) effector complex, RNA-induced silencing complex (RISC). When AGO is loaded with a miRNA, it is guided to a miRNA target site on a target mRNA. It reduces stability and protein production of a target mRNA.

CLASH

(Crosslinking, ligation and sequencing of hybrids). A method that uses high-throughput sequencing to profile and computationally analyse RNA–RNA interactions (for example microRNA–target binding).

Excess

Species A is in excess over species B if its concentration is greater. In the context of binding or simple complex formation with a 1:1 stoichiometry, this may be an indication that most molecules of B are bound. However, the fraction of B that is bound by A is described by the binding equation and strongly depends on the dissociation constant (Kd). For example, weak binding may require many times more A than B to be present before substantial amounts of complex are formed.

Small interfering RNA (siRNA)-directed cleavage by AGO

A process by which target mRNAs are cleaved by the endonucleolytic ('slicing') activity of Argonaute (AGO) proteins, which is triggered when complementarity extends beyond position 11 of the guide RNA. Base-pairing with positions 10 and 11 distinguishes siRNA function (slicing) from microRNA function (no slicing).

Dissociation constant

(Kd). In the absence of competition effects, the concentration of the unbound, free regulator (for example, an RNA-binding protein) at which a binding site is bound or unbound with equal probability. It is derived from the binding energy (E) of the regulator bound to the site: Kd = exp(E/kBT) × [mol/L], where kB is the Boltzmann constant and T is the temperature in Kelvin. As binding energies are negative, strong binding corresponds to a small Kd. Kd can also be defined as the ratio of the off-rates and on-rates: Kd = koff/kon.

Threshold concentration

(Also known as equivalence point of titration). The total ligand concentration at which occupancy is 50%. Without competition effects, this is equal to the dissociation constant (Kd). With many competing binding sites, the free ligand concentration can be much lower than the total concentration, which leads to increased threshold concentrations.

Surface plasmon resonance

A precise technique to measure dissociation constants, for example, for binding of RNA-binding proteins to RNA sequences.

On-rates and off-rates

The rates at which a complex of two molecules is formed (on-rate, measured in M−1 s−1) and decays (off-rate, measured in s−1). They are determined by the series of structural and energetic changes that both molecules undergo upon binding or unbinding. These intrinsic rates depend on temperature and the solvent, but not on the concentrations of the molecules that form the complex. Kinetic (time-dependent) models of molecular interactions require knowledge of these rates.

Seed match

The core region of a microRNA (miRNA) binding site. It is complementary to nucleotides 2–7 (the seed) of the miRNA (guide RNA) and forms a duplex with the miRNA upon binding, which contributes most of the binding energy.

Equimolar

Pertaining to the situation where the same number of different molecules is present in the same volume. The concentrations of these molecules are the same.

Target mimic

A plant microRNA binding site that is resistant to cleavage.

miRNA sponge

A highly expressed RNA transcript that carries an unusually large number (~5 or more) of binding sites for the same microRNA (miRNA). The term was originally introduced for artificial constructs designed to inhibit miRNA function.

miRNA decoys

MicroRNA binding sites that are not selected for conferring repression on its target transcript; they are non-functional or act by sequestration.

Pseudogene

A mutated copy of a protein-coding gene that has lost a functional open reading frame. Some pseudogenes seem to be conserved, but their function is unclear.

Long non-coding RNA

An RNA transcript of at least 200 nucleotides in length that cannot be translated. Many long non-coding RNAs are spliced and capped RNA polymerase II transcripts with poly(A) tails; the function of most long non-coding RNAs is unknown.

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Jens, M., Rajewsky, N. Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat Rev Genet 16, 113–126 (2015). https://doi.org/10.1038/nrg3853

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