Amplitude Modulation—Do MicroRNAs Muffle Noise in Gene Expression?

Protein production is a noisy business. No, not the clangs and bangs of the ribosome, but the random fluctuations in the amount of protein produced by specific genes. How does a cell keep this kind of noise in check? With microRNAs, according to a paper in the April 3 Science. Researchers led by senior author Alexander van Oudenaarden of the University Medical Center Utrecht, the Netherlands, examined how these regulatory RNAs influence the ups and downs of protein production with single-cell experiments and computer modeling. “Our findings suggest that microRNAs confer precision to protein expression,” they concluded.

MicroRNAs tune the expression of many genes, typically by repressing translation or promoting mRNA degradation, but they do so only weakly (Baek et al., 2008; Selbach et al., 2008). Moreover, completely eliminating some microRNAs in animals causes no major problems, or even a noticeable change in many cases (Miska et al., 2007). What are microRNAs good for, if they make such tiny differences to gene expression and phenotype?

Some scientists have hypothesized that they stifle noise (Bartel and Chen, 2004). To test this, van Oudenaarden, co-senior author Debora Marks of Harvard Medical School, and colleagues developed a synthetic reporter system. They expressed mCherry in mouse embryonic stem cells, which resulted in a range of mCherry expression due to different copy numbers of the plasmid. The mCherry gene had a synthetic 3’UTR, which first author Jörn Schmiedel of Berlin’s Humboldt Universität varied by including or excluding sites for the microRNA miR-20a produced by the stem cells. Schmiedel used flow cytometry to analyze how much red fluorescence each individual cell produced, and calculated the noise in that value. Then, he analyzed the effect of miR-20.

With no microRNA sites present, mCherry expression was noisiest when cells expressed only a bit of the gene. Adding one or more miR-20 sites changed the relationship between mCherry concentration and noise. The microRNA reduced variability in cells with low levels of mCherry expression, in keeping with the theory that miRNAs reduce noise. However, miR-20a increased noise in cells that made the most mCherry.

The same pattern occurred in a computer model of translation the authors designed in collaboration with co-senior author Nils Blüthgen of Humboldt Universität. The reason for the opposing effects, according to the model, lay in the two types of noise present. One is intrinsic, based on random variation in the transcription and translation process. The major source of intrinsic noise, Schmiedel said, is how many messenger RNAs are in a cell at a given time. If a cell sports two or three copies of one mRNA, making one more will have a much greater effect on protein production than if the cell has a thousand copies of the transcript. MicroRNAs, by slightly reducing translation, would prevent such a dramatic swing.

However, microRNAs contribute to the second type of noise—called extrinsic—since they are an additional variable factor. At low levels of mCherry, the reduction in intrinsic noise dominates over any augmentation of extrinsic noise, according to the computer model. If a gene is highly expressed, the opposite happens. In that case, because each individual mRNA makes a proportionally smaller difference to the total protein produced, microRNAs do not reduce intrinsic noise much and the added extrinsic noise dominates, according to the model.

Schmiedel confirmed this prediction experimentally by isolating intrinsic from extrinsic noise in his cell cultures. He analyzed two fluorescent protein constructs with the exact same promoter and 3’UTR, mCherry and ZsGreen. Any differences between red and green fluorescence should be due to random variation, that is, intrinsic noise, since they were exposed to the same concentrations of miR-20a. He calculated intrinsic noise based on the difference between the red and green fluorescence intensity. The closer the red and green matched, the lower the intrinsic noise. As predicted, including miR-20a binding sites reduced noise, regardless of mCherry and ZsGreen expression level. Therefore, in the first experiment the rise in noise in cells highly expressing mCherry must have been due to extrinsic noise dominating the effects of intrinsic noise, as the computer model predicted.

What does this mean for the average cell? By measuring levels of all mRNAs in the stem cells, the authors reckoned that about 90 percent of mouse genes are expressed weakly enough that miRNAs would reduce intrinsic noise, rather than adding to extrinsic variability.

“This study is insightful,” emailed Phillip Sharp of the Massachusetts Institute of Technology, who was not involved in the study. “The results suggest that the broadest function of microRNA is to suppress variation in gene expression. This helps the cell maintain homeostasis, keeping gene products balanced for normal growth.” In an editorial accompanying the Science paper, Yonit Hoffman and Yitzhak Pilpel of the Weizmann Institute of Science in Rehovot, Israel, suggest that Schmiedel and colleagues may have discovered the first of many RNA-based noise reducers. “‘Antisense’ RNAs may also act in noise filtration,” they speculate. “Perhaps some long noncoding RNAs, too, contribute to fine tuning of gene expression programs.”

For their part, Schmiedel and Blüthgen cautioned that the results do not imply that noise canceling is microRNA’s only, or even its primary, function. It may be minor compared to another, main job, such as simply dampening gene expression, they explained.

Scientists are starting to get a handle on the function of microRNAs in neurodegeneration. Loss of certain microRNAs precipitates neurodegeneration in mice (see Jun 2010 news) and fruit flies (Karres et al., 2007; Feb 2015 news; news). Researchers have also observed altered microRNA profiles in Alzheimer’s, frontotemporal dementia, and ALS (see May 2014 conference news; Nov 2014 conference news). At Washington University in St. Louis, Timothy Miller’s group is already working on a micro-RNA blocking therapeutic for amyotrophic lateral sclerosis (see Nov 2013 conference news). 

What does this noise, or lack thereof, mean for neurodegeneration studies? Schmiedel’s study provides solid evidence for what most researchers studying microRNAs had already intuited, commented Beverly Davidson of Children’s Hospital of Philadelphia, who was not involved in the paper. “I do not think the implications change our understanding of microRNAs in the context of neurodegeneration, or how we might use them in the context of therapy.”

Miller, who did not participate in the work, agreed it was too early to make direct links between gene expression noise in mouse embryonic stem cells and microRNAs related to neurodegeneration. One next step, he suggested, would be to investigate how broad the microRNA effect on noise is—does it work the same with all genes and microRNAs, and in all cell types or animals? If so, he speculated that problems with microRNA regulation of noise might affect risk for neurodegeneration. For example, a person with aberrant microRNAs might have extra-noisy expression of a chaperone. If that chaperone dipped particularly low, it might allow proteins to misfold, and that could spell trouble.—Amber Dance

Comments

  1. MicroRNA expression profiling studies of the AD brain have yielded invaluable information about microRNAs whose expression consistently differs between healthy and diseased CNS. These findings not only shed light on regulatory mechanisms possibly affecting and/or being affected by pathogenesis, they also open new avenues for putative diagnostic and therapeutic approaches. However, one of the major hurdles to overcome in microRNA research is understanding the cellular complexity of microRNA networks: In theory, a single microRNA can regulate some hundreds of mRNAs and, conversely, each mRNA can be regulated by multiple microRNAs. In several cases, microRNA knockdown or knockout phenotypes in CNS have been difficult to assess, highlighting the functional importance of endogenous compensatory regulation. However, van Oudenaarden’s group has shown that microRNA regulation occurs in a threshold-dependent manner, and therefore microRNAs can act both as crucial switches and as fine-tuners of gene expression (Mukherji et al., 2011). Along the same lines, a recent study in fruit flies reported a series of developmental and adult phenotypes resulting from the genetic deletion of single microRNAs (Chen et al., 2014). Now, Schmiedel and his colleagues from van Oudenaarden’s group publish a refined analysis of how microRNAs affect protein expression noise, a widely observed phenomenon that may complicate interpretation of microRNA knockdown or knockout phenotypes.

    Integrating their mathematical modeling and experimental data using a fluorescent 3’UTR reporter system in mouse embryonic stem cells, the authors shed light on certain aspects of microRNA biology: Single microRNAs decrease protein expression noise for lowly expressed transcripts, while above a certain target expression threshold microRNAs exert the opposite effect, increasing the protein expression noise. Combinatorial microRNA regulation enhances the noise reduction. The study also dissects the functional contribution of intrinsic and extrinsic noise to the overall microRNA-mediated effects, providing mechanistic information that could be further applied to the widespread phenomena of non-coding RNA-driven posttranscriptional regulation.

    These data add to the understanding of the overall complexity of the intracellular microRNA functional networks and help elucidate the frequent lack of microRNA knockdown and knockout phenotypes in CNS: It is possible that the functional equilibrium between microRNA and target is substantially disturbed to generate a detectable phenotype only when a stress stimulus is applied to a cellular system that drastically changes the spatiotemporal dynamics of the transcriptome (e.g., synaptic dysfunction, hyperphosphorylated tau deposition, neuroinflammation, neuronal death).

    In AD, passive microRNA leakage from dying neurons into the extracellular space further contributes to aberrant intercellular microRNA cross-talk perturbing the microRNA/endogenous site abundance ratio. Moreover, several stressors present in the diseased brain, including reactive oxygen species, may alter the function of some of the components of the microRNA machinery, such as the AGO2 protein, thereby affecting downstream regulatory events. Thus, in a very well-defined cellular spatiotemporal context in the degenerating CNS, it is possible that only few or even one primary mRNA target may functionally prevail, while other, weaker targets may only “titrate” the effective microRNA concentration available for binding. 

    microRNA-132, which is consistently and significantly downregulated in AD brain, may serve as a prototype for this kind of thinking: microRNA-132 knockdown or knockout can elicit CNS phenotypes that may be of relevance to AD, and in many of these cases the phenotypes have been attributed to a single primary target (Hernandez-Rapp et al., 2015; Salta et al., 2014; Lau et al., 2013; Wong et al., 2013; Shaltiel et al, 2013; Smith et al., 2011).

    To date, analysis of miRNAs in AD has largely considered total CNS tissue as a functional entity. However, studies like this one reported by Schmiedel et al., combined with the remarkable cellular diversity of the brain, clearly indicate the necessity for cellular resolution approaches. Single-cell analysis of microRNA and transcript abundance in different neuronal and glial populations in AD brain may prove to be crucially informative in this regard. 

    References:

    Chen YW, Song S, Weng R, Verma P, Kugler JM, Buescher M, Rouam S, Cohen SM. Systematic study of Drosophila microRNA functions using a collection of targeted knockout mutations. Dev Cell. 2014 Dec 22;31(6):784-800. PubMed.

    Hernandez-Rapp J, Smith PY, Filali M, Goupil C, Planel E, Magill ST, Goodman RH, Hébert SS. Memory formation and retention are affected in adult miR-132/212 knockout mice. Behav Brain Res. 2015 Jul 1;287:15-26. Epub 2015 Mar 23 PubMed.

    Lau P, Bossers K, Janky R, Salta E, Frigerio CS, Barbash S, Rothman R, Sierksma AS, Thathiah A, Greenberg D, Papadopoulou AS, Achsel T, Ayoubi T, Soreq H, Verhaagen J, Swaab DF, Aerts S, De Strooper B. Alteration of the microRNA network during the progression of Alzheimer's disease. EMBO Mol Med. 2013 Oct;5(10):1613-34. Epub 2013 Sep 9 PubMed.

    Mukherji S, Ebert MS, Zheng GX, Tsang JS, Sharp PA, van Oudenaarden A. MicroRNAs can generate thresholds in target gene expression. Nat Genet. 2011 Aug 21;43(9):854-9. PubMed.

    Salta E, Lau P, Sala Frigerio C, Coolen M, Bally-Cuif L, De Strooper B. A self-organizing miR-132/Ctbp2 circuit regulates bimodal notch signals and glial progenitor fate choice during spinal cord maturation. Dev Cell. 2014 Aug 25;30(4):423-36. Epub 2014 Aug 14 PubMed.

    Shaltiel G, Hanan M, Wolf Y, Barbash S, Kovalev E, Shoham S, Soreq H. Hippocampal microRNA-132 mediates stress-inducible cognitive deficits through its acetylcholinesterase target. Brain Struct Funct. 2013 Jan;218(1):59-72. Epub 2012 Jan 14 PubMed.

    Smith PY, Delay C, Girard J, Papon MA, Planel E, Sergeant N, Buée L, Hébert SS. MicroRNA-132 loss is associated with tau exon 10 inclusion in progressive supranuclear palsy. Hum Mol Genet. 2011 Oct 15;20(20):4016-24. PubMed.

    Wong HK, Veremeyko T, Patel N, Lemere CA, Walsh DM, Esau C, Vanderburg C, Krichevsky AM. De-repression of FOXO3a death axis by microRNA-132 and -212 causes neuronal apoptosis in Alzheimer's disease. Hum Mol Genet. 2013 Aug 1;22(15):3077-92. PubMed.

    View all comments by Bart De Strooper

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References

News Citations

  1. Motor Neuron Disease Risk Factors: Fresh miRNA Clue, KIFAP3 Letdown
  2. In Fruit Flies, MicroRNA Protects Synapses Against Excitotoxicity
  3. Neurodegeneration and Aging: Could MicroRNA Be the Link?
  4. Glymphatic Flow, Sleep, microRNA Are Frontiers in Alzheimer’s Research
  5. Do MicroRNAs Cause Mayhem Across Frontotemporal Dementia Spectrum?
  6. Blocking a MicroRNA Slows Motor Neuron Disease in Mice

Paper Citations

  1. Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature. 2008 Sep 4;455(7209):64-71. PubMed.
  2. Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008 Sep 4;455(7209):58-63. PubMed.
  3. Miska EA, Alvarez-Saavedra E, Abbott AL, Lau NC, Hellman AB, McGonagle SM, Bartel DP, Ambros VR, Horvitz HR. Most Caenorhabditis elegans microRNAs are individually not essential for development or viability. PLoS Genet. 2007 Dec;3(12):e215. PubMed.
  4. Bartel DP, Chen CZ. Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs. Nat Rev Genet. 2004 May;5(5):396-400. PubMed.
  5. Karres JS, Hilgers V, Carrera I, Treisman J, Cohen SM. The conserved microRNA miR-8 tunes atrophin levels to prevent neurodegeneration in Drosophila. Cell. 2007 Oct 5;131(1):136-45. PubMed.

Further Reading

Papers

  1. Femminella GD, Ferrara N, Rengo G. The emerging role of microRNAs in Alzheimer's disease. Front Physiol. 2015;6:40. Epub 2015 Feb 12 PubMed.
  2. Volonte C, Apolloni S, Parisi C. MicroRNAs: newcomers into the ALS picture. CNS Neurol Disord Drug Targets. 2015;14(2):194-207. PubMed.
  3. Dumitrescu L, Popescu BO. MicroRNAs in CAG trinucleotide repeat expansion disorders: an integrated review of the literature. CNS Neurol Disord Drug Targets. 2015;14(2):176-93. PubMed.
  4. Tan L, Yu JT, Tan L. Causes and Consequences of MicroRNA Dysregulation in Neurodegenerative Diseases. Mol Neurobiol. 2014 Jun 29; PubMed.
  5. Grasso M, Piscopo P, Confaloni A, Denti MA. Circulating miRNAs as biomarkers for neurodegenerative disorders. Molecules. 2014 May 23;19(5):6891-910. PubMed.
  6. Balázsi G, van Oudenaarden A, Collins JJ. Cellular decision making and biological noise: from microbes to mammals. Cell. 2011 Mar 18;144(6):910-25. PubMed.
  7. Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008 Oct 17;135(2):216-26. PubMed.

Primary Papers

  1. Schmiedel JM, Klemm SL, Zheng Y, Sahay A, Blüthgen N, Marks DS, van Oudenaarden A. Gene expression. MicroRNA control of protein expression noise. Science. 2015 Apr 3;348(6230):128-32. PubMed.
  2. Hoffman Y, Pilpel Y. Gene expression. MicroRNAs silence the noisy genome. Science. 2015 Apr 3;348(6230):41-2. PubMed.

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