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.
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.
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.
Comments
Netherlands Institute for Neuroscience
UK Dementia Research Institute@UCL and VIB@KuLeuven
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.
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