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 Blöthgen 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 Blöthgen 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 news story).
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.
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.
Hoffman Y, Pilpel Y. Gene expression. MicroRNAs silence the noisy genome. Science. 2015 Apr 3;348(6230):41-2. PubMed.
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