Alkallas R, Fish L, Goodarzi H, Najafabadi HS.
Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer's disease.
Nat Commun. 2017 Oct 13;8(1):909.
PubMed.
Post-transcriptional regulation of gene expression is one of the determinants of cellular function and dysfunction. RNA deep sequencing data are often used as a proxy for gene expression regulation, however, in the majority of the cases the deduced analyses only provide a snapshot of steady-state mRNA levels without addressing other pivotal layers of post-transcriptional regulation, such as mRNA stability. Regulated mRNA stability is achieved by half-life shifts in response to developmental and environmental stimuli and stressors. The rate of mRNA turnover will eventually define cellular and tissue mRNA levels in a given pathophysiological context, as mRNAs with short half-lives tend to respond to changes in transcription more rapidly than transcripts that are relatively stable.
The previously established Δexon-Δintron model uses the quantitative relationship between exonic and intronic reads in RNA-sequencing data sets as a readout of mRNA decay rate and post-transcriptional regulation, where exonic read counts correspond to steady-state abundance, and intronic read changes reflect alterations in transcriptional activity (Gaidatzis et al., 2015). Even though this computational approach already has been shown to reduce the number of false positives when combined with standard prediction algorithms to predict potential microRNA targets, it is not bias-free.
Alcallas et al. now report an improved method that estimates the transcription rate-dependent bias from RNA-sequencing data and corrects for it in the Δexon-Δintron model, providing unbiased estimates of differential mRNA half-life. The study demonstrates widespread tissue-specific differences in mRNA stability profiles with high degree of species conservation. Interestingly, the data indicate a prominent role of mRNA stability regulation in shaping the brain transcriptome and suggest an overall destabilization of brain-specific transcripts in advanced AD. The authors propose a minimalistic network consisting of two RNA-binding proteins (RBFOX and ZFP36) and four microRNAs (miR-124, miR-29, miR-9, miR-128) that emerges as a potent regulator of mRNA stability in the central nervous system. Moreover, RBFOX1 knockdown in differentiated primary human neural progenitor cultures induces a transcriptomic profile that resembles the mRNA stability signature observed in AD.
The study adds to the realization that post-transcriptional regulation is of high importance in the brain and may account for both its high-order functional flexibility and its vulnerability to certain stressors, such as aging. It inevitably also raises a series of questions on what are the precise mechanisms that mediate the effects of the mRNA stability machinery on brain function and on neurodegeneration. The four identified microRNAs have been previously reported to be either downregulated or unchanged in postmortem human AD brain (Lau et al., 2013; Hébert et al., 2008), which cannot explain the observed trend for destabilization of their target transcripts. Recently, it was very elegantly shown that the degradation rate of miRNA target proteins is the rate-limiting factor for miRNA-mediated gene silencing under physiological conditions (Ando et al., 2017). Could a scenario hold true in which there either is a reprioritization of functional microRNA targets in the degenerating brain and/or the massive alterations in relative transcript abundance decouple miRNA-mediated gene regulation from mRNA decay rates?
Another important point that has not yet been addressed by the study is the region- and stage-specificity of the described changes. For instance, in which brain areas and at which Braak stages do the alterations in the levels of RBFOX and its synaptic targets occur, and how do these correlate with region vulnerability and histopathology progression? Interestingly, functional gene network analyses in human AD cortex have previously suggested increased synaptic activity during very early stages of the pathology (Bossers et al., 2010). Additionally, would the mRNA stability machinery be differentially impacted in sporadic and familial AD? And finally, which are the upstream protein-coding or noncoding regulators of the proteins and microRNAs involved in mRNA stability mechanisms? Prediction algorithms suggest that ZFP36 and RBFOX1 theoretically could be targeted by miR-29 and miR-132, respectively, pointing toward the possible existence of feedback regulatory interactions in the network described by the authors.
The computational model reported here is obviously of particular significance in the omics era, where single-cell RNA-sequencing big data are continuously generated and used to assess gene expression in health and disease. This novel approach increases the depth of information that can be extracted from the resulting datasets, while it also can be used as an extra parameter in the assessment of potential druggable targets in neurodegeneration. Of note, perturbation of mRNA splicing or intron stability (as it is the case in several neurodegenerative disorders) will introduce a bias in the classification and interpretation of the intron-exon relative abundance. Therefore, experimental validation of the inferred functional interactions between the regulatory network identified and the transcriptome is required.
References:
Ando H, Hirose M, Kurosawa G, Impey S, Mikoshiba K.
Time-lapse imaging of microRNA activity reveals the kinetics of microRNA activation in single living cells.
Sci Rep. 2017 Oct 3;7(1):12642.
PubMed.
Bossers K, Wirz KT, Meerhoff GF, Essing AH, van Dongen JW, Houba P, Kruse CG, Verhaagen J, Swaab DF.
Concerted changes in transcripts in the prefrontal cortex precede neuropathology in Alzheimer's disease.
Brain. 2010 Dec;133(Pt 12):3699-723. Epub 2010 Oct 1
PubMed.
Gaidatzis D, Burger L, Florescu M, Stadler MB.
Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation.
Nat Biotechnol. 2015 Jul;33(7):722-9. Epub 2015 Jun 22
PubMed.
Hébert SS, Horré K, Nicolaï L, Papadopoulou AS, Mandemakers W, Silahtaroglu AN, Kauppinen S, Delacourte A, De Strooper B.
Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression.
Proc Natl Acad Sci U S A. 2008 Apr 29;105(17):6415-20. Epub 2008 Apr 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.
Alkallas et al. have reported on enhanced mRNA destabilization in aged and female Alzheimer’s disease (AD) brains as the result of downregulation of the “protective” RNA-binding proteins (RBP) RBFOX1 (RBP fox-1 homolog) and ZFP36 (also known as the zinc finger protein 36 homolog tristetraprolin, or TTP), two proteins of a very much larger RBP family. They report that these two RNA-binding proteins of the ~860 proteins that qualify as RBPs (Castello et al., 2012) appear to be downregulated, inducing mRNA instability, as the result of increases in miRNAs, and especially miRNA-9, miRNA-29, miRNA-124, and miRNA-128; upregulated miRNAs are known to downregulate their messenger RNA (mRNA) targets (Guo et al., 2010). In these studies they refer to the use of “RNA-seq data from a panel of 20 diverse human tissues,” which refers to the paper by Duff et al. (reference 10), a paper mostly about Drosophila RNA, and “total RNA from 20 human tissues obtained from Clontech (cat no. 636643)”—that actually contains RNA extracted from just one 18-year-old male Caucasian brain and cerebellum pooled from 10 male/female Caucasians (ages 22–68); it is well known that the cerebellum is not the anatomical focus of AD-type change (Duff et al., 2015). Alkallas et al. also refer to a paper by Scheckel et al (reference 37), who have used up to 18 hour postmortem interval (PMI) brains in the description of neuronal ELAV-like (nELAVL) RBPs, which have long been linked to numerous neurological disorders (Castello et al., 2012; Scheckel et al, 2016). It is well known that human brain miRNAs and mRNAs have a very limited stability to start with, and after about three hours PMI an accurate sampling of the total mRNA that comprise a representative transcriptome of especially brain tissue is not easily obtainable.
It seems a genuine conundrum why there should be robust quantities of highly specific miRNAs (and presumably much larger miRNA precursors) in AD in the face of an environment of enriched oxidization, very active pools of reactive oxygen species (ROS), and neurodegeneration. Unlike DNA, miRNA and mRNA are long known to have very limited half-life in carrying genetic information from the DNA to the ribosome, even under the best of conditions. On the other hand, there is plenty of evidence of transcriptional failure or deficits in the RNA-polymerase II- and III-directed basic transcription mechanisms in AD brain that contributes to the widely observed downregulation of gene expression in this devastating disease.
Our laboratory has been studying small noncoding RNA (sncRNA), single-stranded RNA (ssRNA), and more recently miRNA, and mRNA abundance, complexity, speciation, and stability in aging and AD brains for the last 36 years (Lewis et al., 1981; Colangelo et al., 2002; Jaber et al., 2017). We were the first laboratory to report, more than 10 years ago, that micro-RNAs (miRNAs) including miRNA-9, miRNA-124a, miRNA-125b, and others, are abundantly represented in fetal human hippocampus, are differentially regulated in aged brain, and that an alteration in specific miRNA abundance and speciation occurs in AD brain (Lukiw, 2007). Our work on the examination of miRNA and mRNA in several hundred high-quality, short PMI, and autopsied AD brains and controls shows clearly that there is a major problem with transcription and transcriptional control in AD. We have found a general rapid—and equal—decay both of miRNA and mRNA in AD brains and very little, if any, difference between the stability of miRNA or mRNA in short PMI brain samples from AD or age-matched controls (Rüegger and Großhans, 2012; Pogue et al., 2014). These data are consistent with the idea that altered miRNA-mediated processing of mRNA populations may contribute to atypical mRNA abundance patterns, an altered transcriptome, and neural dysfunction as is observed in AD brain. The Alkallas et al. paper begins “The abundance of mRNA is mainly determined by the rates of RNA transcription and decay”; using carefully assayed total mRNA yields per gram wet weight of AD and control tissue, quantitative RT-PCR, LED-Northern analysis, microRNA-ribosomal RNA (miRNA-rRNA) ratios, and RNA sequencing, we have no evidence that miRNAs or mRNAs are degraded any faster in the AD-affected brain, just that there a less initial abundance of them (Colangelo et al., 2002; Jaber et al., 2017; Lukiw, 2007; Pogue et al., 2014).
In addition, neurons have devised important ancillary controls on miRNA and mRNA abundance and stability in human brain cells and cells of the central nervous system (CNS) besides special miRNAs and RBPs not mentioned by Alkallas et al. These include (i) percent adenine+uridine (A+U) content of miRNA and mRNA primary sequence structure and content of A+U-enriched sequences which confer instability to ssRNA; (ii) the ability of sncRNAs and especially miRNAs to be concentrated and packaged into small brain cell-membrane-derived extracellular vesicles that can protect ssRNAs from ribonuclease-directed degradation; (iii) by ssRNAs adopting secondary and tertiary structures inaccessible to ribonuclease (a good example is the extremely lethal and immune-evading ssRNA Ebola virus); (iv) by miRNA, sncRNA, and mRNA interaction with multiple RBPs and/or other ssRNAs which bind to and extend miRNA and mRNA longevity under physiological conditions; (v) by miRNA, anti-miRNA, and/or mRNA circularization; or by any combination of these sometimes rare and exotic genetic regulatory mechanisms (Colangelo et al., 2002; Jaber et al., 2017; Lukiw, 2007; Rüegger and Großhans, 2012; Pogue et al., 2014).
Overall, there is a lot wrong with gene expression signaling, including significant transcriptional deficits, in AD, especially in the neocortex and hippocampus. As an extremely heterogeneous and insidious disease, there is probably an equal heterogeneity in the molecular-genetic, epigenetic, and pathogenetic mechanisms that drive the disruption of gene-expression processes leading to the alternately populated transcriptome characteristic of AD-affected brain.
References:
Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM, Krijgsveld J, Hentze MW.
Insights into RNA biology from an atlas of mammalian mRNA-binding proteins.
Cell. 2012 Jun 8;149(6):1393-406. Epub 2012 May 31
PubMed.
Duff MO, Olson S, Wei X, Garrett SC, Osman A, Bolisetty M, Plocik A, Celniker SE, Graveley BR.
Genome-wide identification of zero nucleotide recursive splicing in Drosophila.
Nature. 2015 May 21;521(7552):376-9. Epub 2015 May 13
PubMed.
Scheckel C, Drapeau E, Frias MA, Park CY, Fak J, Zucker-Scharff I, Kou Y, Haroutunian V, Ma'ayan A, Buxbaum JD, Darnell RB.
Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain.
Elife. 2016 Feb 19;5
PubMed.
Lewis PN, Lukiw WJ, De Boni U, McLachlan DR.
Changes in chromatin structure associated with Alzheimer's disease.
J Neurochem. 1981 Nov;37(5):1193-202.
PubMed.
Colangelo V, Schurr J, Ball MJ, Pelaez RP, Bazan NG, Lukiw WJ.
Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling.
J Neurosci Res. 2002 Nov 1;70(3):462-73.
PubMed.
Jaber V, Zhao Y, Lukiw WJ.
Alterations in micro RNA-messenger RNA (miRNA-mRNA) Coupled Signaling Networks in Sporadic Alzheimer's Disease (AD) Hippocampal CA1.
J Alzheimers Dis Parkinsonism. 2017 Apr;7(2) Epub 2017 Mar 10
PubMed.
Lukiw WJ.
Micro-RNA speciation in fetal, adult and Alzheimer's disease hippocampus.
Neuroreport. 2007 Feb 12;18(3):297-300.
PubMed.
Rüegger S, Großhans H.
MicroRNA turnover: when, how, and why.
Trends Biochem Sci. 2012 Oct;37(10):436-46. Epub 2012 Aug 23
PubMed.
Pogue AI, Hill JM, Lukiw WJ.
MicroRNA (miRNA): sequence and stability, viroid-like properties, and disease association in the CNS.
Brain Res. 2014 Oct 10;1584:73-9. Epub 2014 Apr 4
PubMed.
Professor Lukiw raises several concerns that we also share with regard to our analyses, and we have tried to address them to the point that was possible in the paper. Nonetheless, additional experiments and analyses are warranted to fully address these issues.
First, Professor Lukiw points out that the RNA-seq data set used in our paper (from Duff et al., 2015) contains an RNA sample from just one 18-year-old male Caucasian brain. We used this RNA-seq set to derive a regulatory network of mRNA stability in the normal brain. In the paper, we compared the stability estimates from this sample to those from Illumina BodyMap (Alkallas et al., Fig 3f), and observed overall consistent measurements. Importantly, the stability model that we obtained for two RBPs and four miRNAs was able to explain the brain-specific stability measurements that were consistent between these two brain samples (Alkallas et al., Supplementary Fig 9). It would be interesting to see to what extent these trends can also be reproduced in other RNA-seq data sets.
Secondly, Professor Lukiw correctly points out the potential degradation of RNAs in postmortem samples of AD patients (from Scheckel et al., 2016). There are a few lines of evidence suggesting that our conclusions about destabilization of RBFOX1 targets in AD were not confounded by postmortem RNA degradation. (i) In our analyses we compare AD brains to brains of non-AD individuals, which in principle should be affected similarly by postmortem RNA degradation as long as sample collection procedures were consistent. Therefore, destabilization of a specific set of genes in AD brains compared to normal would likely reflect an underlying biological process. (ii) We see specific destabilization of RBFOX1 targets in AD compared to other genes (Alkallas et al., Fig 5f), which is difficult to explain simply based on postmortem RNA degradation, which presumably affects all mRNAs, or at least should not be correlated with the presence of RBFOX1 binding site. This observation also holds true when we compare RBFOX1 targets to other neuron-specific genes (Alkallas et al., Supplementary Fig 13). (iii) We also tried to reproduce these analyses in two other AD cohorts: an RNA-seq dataset from Magistri et al. (2017), and a microarray dataset from Narayanan et al. (2014). While each of these datasets have their limitations, we observed that RBFOX1 targets are specifically downregulated in these cohorts too (Alkallas et al., Fig 5e and Supplementary Fig 14). I also want to point out that we did not observe destabilization of the targets of the four brain-specific miRNAs in AD. Instead, there may be an AD-associated stabilization of the targets of miR-124, which points to downregulation of miR-124. This is consistent with previous publications (e.g., Lukiw 2007, which reported a trend toward downregulation of miR-124a in AD). Also, it would be interesting to see how the stability of targets of miRNAs that are not brain-specific changes in AD. For example, Jaber et al. (2017) show upregulation of miR-34a and miRNA-146a in sporadic AD, and subsequent downregulation of their targets, which most likely reflects rapid degradation of these mRNAs as a result of miRNA upregulation.
Overall, we share Professor Lukiw’s opinion that AD is a heterogeneous disorder with abnormalities in gene expression at different layers of transcriptional and post-transcriptional regulation, and studying a regulatory model that encompasses both transcriptional and post-transcriptional layers in the context of AD is certainly warranted.
References:
Duff MO, Olson S, Wei X, Garrett SC, Osman A, Bolisetty M, Plocik A, Celniker SE, Graveley BR.
Genome-wide identification of zero nucleotide recursive splicing in Drosophila.
Nature. 2015 May 21;521(7552):376-9. Epub 2015 May 13
PubMed.
Scheckel C, Drapeau E, Frias MA, Park CY, Fak J, Zucker-Scharff I, Kou Y, Haroutunian V, Ma'ayan A, Buxbaum JD, Darnell RB.
Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain.
Elife. 2016 Feb 19;5
PubMed.
Magistri M, Velmeshev D, Makhmutova M, Faghihi MA.
Transcriptomics Profiling of Alzheimer's Disease Reveal Neurovascular Defects, Altered Amyloid-β Homeostasis, and Deregulated Expression of Long Noncoding RNAs.
J Alzheimers Dis. 2015;48(3):647-65.
PubMed.
Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, Zhang B, Zhang C, Lamb JR, Xie T, Suver C, Molony C, Melquist S, Johnson AD, Fan G, Stone DJ, Schadt EE, Casaccia P, Emilsson V, Zhu J.
Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.
Mol Syst Biol. 2014 Jul 30;10:743.
PubMed.
Lukiw WJ.
Micro-RNA speciation in fetal, adult and Alzheimer's disease hippocampus.
Neuroreport. 2007 Feb 12;18(3):297-300.
PubMed.
Jaber V, Zhao Y, Lukiw WJ.
Alterations in micro RNA-messenger RNA (miRNA-mRNA) Coupled Signaling Networks in Sporadic Alzheimer's Disease (AD) Hippocampal CA1.
J Alzheimers Dis Parkinsonism. 2017 Apr;7(2) Epub 2017 Mar 10
PubMed.
Comments
Netherlands Institute for Neuroscience
Post-transcriptional regulation of gene expression is one of the determinants of cellular function and dysfunction. RNA deep sequencing data are often used as a proxy for gene expression regulation, however, in the majority of the cases the deduced analyses only provide a snapshot of steady-state mRNA levels without addressing other pivotal layers of post-transcriptional regulation, such as mRNA stability. Regulated mRNA stability is achieved by half-life shifts in response to developmental and environmental stimuli and stressors. The rate of mRNA turnover will eventually define cellular and tissue mRNA levels in a given pathophysiological context, as mRNAs with short half-lives tend to respond to changes in transcription more rapidly than transcripts that are relatively stable.
The previously established Δexon-Δintron model uses the quantitative relationship between exonic and intronic reads in RNA-sequencing data sets as a readout of mRNA decay rate and post-transcriptional regulation, where exonic read counts correspond to steady-state abundance, and intronic read changes reflect alterations in transcriptional activity (Gaidatzis et al., 2015). Even though this computational approach already has been shown to reduce the number of false positives when combined with standard prediction algorithms to predict potential microRNA targets, it is not bias-free.
Alcallas et al. now report an improved method that estimates the transcription rate-dependent bias from RNA-sequencing data and corrects for it in the Δexon-Δintron model, providing unbiased estimates of differential mRNA half-life. The study demonstrates widespread tissue-specific differences in mRNA stability profiles with high degree of species conservation. Interestingly, the data indicate a prominent role of mRNA stability regulation in shaping the brain transcriptome and suggest an overall destabilization of brain-specific transcripts in advanced AD. The authors propose a minimalistic network consisting of two RNA-binding proteins (RBFOX and ZFP36) and four microRNAs (miR-124, miR-29, miR-9, miR-128) that emerges as a potent regulator of mRNA stability in the central nervous system. Moreover, RBFOX1 knockdown in differentiated primary human neural progenitor cultures induces a transcriptomic profile that resembles the mRNA stability signature observed in AD.
The study adds to the realization that post-transcriptional regulation is of high importance in the brain and may account for both its high-order functional flexibility and its vulnerability to certain stressors, such as aging. It inevitably also raises a series of questions on what are the precise mechanisms that mediate the effects of the mRNA stability machinery on brain function and on neurodegeneration. The four identified microRNAs have been previously reported to be either downregulated or unchanged in postmortem human AD brain (Lau et al., 2013; Hébert et al., 2008), which cannot explain the observed trend for destabilization of their target transcripts. Recently, it was very elegantly shown that the degradation rate of miRNA target proteins is the rate-limiting factor for miRNA-mediated gene silencing under physiological conditions (Ando et al., 2017). Could a scenario hold true in which there either is a reprioritization of functional microRNA targets in the degenerating brain and/or the massive alterations in relative transcript abundance decouple miRNA-mediated gene regulation from mRNA decay rates?
Another important point that has not yet been addressed by the study is the region- and stage-specificity of the described changes. For instance, in which brain areas and at which Braak stages do the alterations in the levels of RBFOX and its synaptic targets occur, and how do these correlate with region vulnerability and histopathology progression? Interestingly, functional gene network analyses in human AD cortex have previously suggested increased synaptic activity during very early stages of the pathology (Bossers et al., 2010). Additionally, would the mRNA stability machinery be differentially impacted in sporadic and familial AD? And finally, which are the upstream protein-coding or noncoding regulators of the proteins and microRNAs involved in mRNA stability mechanisms? Prediction algorithms suggest that ZFP36 and RBFOX1 theoretically could be targeted by miR-29 and miR-132, respectively, pointing toward the possible existence of feedback regulatory interactions in the network described by the authors.
The computational model reported here is obviously of particular significance in the omics era, where single-cell RNA-sequencing big data are continuously generated and used to assess gene expression in health and disease. This novel approach increases the depth of information that can be extracted from the resulting datasets, while it also can be used as an extra parameter in the assessment of potential druggable targets in neurodegeneration. Of note, perturbation of mRNA splicing or intron stability (as it is the case in several neurodegenerative disorders) will introduce a bias in the classification and interpretation of the intron-exon relative abundance. Therefore, experimental validation of the inferred functional interactions between the regulatory network identified and the transcriptome is required.
References:
Ando H, Hirose M, Kurosawa G, Impey S, Mikoshiba K. Time-lapse imaging of microRNA activity reveals the kinetics of microRNA activation in single living cells. Sci Rep. 2017 Oct 3;7(1):12642. PubMed.
Bossers K, Wirz KT, Meerhoff GF, Essing AH, van Dongen JW, Houba P, Kruse CG, Verhaagen J, Swaab DF. Concerted changes in transcripts in the prefrontal cortex precede neuropathology in Alzheimer's disease. Brain. 2010 Dec;133(Pt 12):3699-723. Epub 2010 Oct 1 PubMed.
Gaidatzis D, Burger L, Florescu M, Stadler MB. Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation. Nat Biotechnol. 2015 Jul;33(7):722-9. Epub 2015 Jun 22 PubMed.
Hébert SS, Horré K, Nicolaï L, Papadopoulou AS, Mandemakers W, Silahtaroglu AN, Kauppinen S, Delacourte A, De Strooper B. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/beta-secretase expression. Proc Natl Acad Sci U S A. 2008 Apr 29;105(17):6415-20. Epub 2008 Apr 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.
View all comments by Evgenia SaltaLouisiana State University
Alkallas et al. have reported on enhanced mRNA destabilization in aged and female Alzheimer’s disease (AD) brains as the result of downregulation of the “protective” RNA-binding proteins (RBP) RBFOX1 (RBP fox-1 homolog) and ZFP36 (also known as the zinc finger protein 36 homolog tristetraprolin, or TTP), two proteins of a very much larger RBP family. They report that these two RNA-binding proteins of the ~860 proteins that qualify as RBPs (Castello et al., 2012) appear to be downregulated, inducing mRNA instability, as the result of increases in miRNAs, and especially miRNA-9, miRNA-29, miRNA-124, and miRNA-128; upregulated miRNAs are known to downregulate their messenger RNA (mRNA) targets (Guo et al., 2010). In these studies they refer to the use of “RNA-seq data from a panel of 20 diverse human tissues,” which refers to the paper by Duff et al. (reference 10), a paper mostly about Drosophila RNA, and “total RNA from 20 human tissues obtained from Clontech (cat no. 636643)”—that actually contains RNA extracted from just one 18-year-old male Caucasian brain and cerebellum pooled from 10 male/female Caucasians (ages 22–68); it is well known that the cerebellum is not the anatomical focus of AD-type change (Duff et al., 2015). Alkallas et al. also refer to a paper by Scheckel et al (reference 37), who have used up to 18 hour postmortem interval (PMI) brains in the description of neuronal ELAV-like (nELAVL) RBPs, which have long been linked to numerous neurological disorders (Castello et al., 2012; Scheckel et al, 2016). It is well known that human brain miRNAs and mRNAs have a very limited stability to start with, and after about three hours PMI an accurate sampling of the total mRNA that comprise a representative transcriptome of especially brain tissue is not easily obtainable.
It seems a genuine conundrum why there should be robust quantities of highly specific miRNAs (and presumably much larger miRNA precursors) in AD in the face of an environment of enriched oxidization, very active pools of reactive oxygen species (ROS), and neurodegeneration. Unlike DNA, miRNA and mRNA are long known to have very limited half-life in carrying genetic information from the DNA to the ribosome, even under the best of conditions. On the other hand, there is plenty of evidence of transcriptional failure or deficits in the RNA-polymerase II- and III-directed basic transcription mechanisms in AD brain that contributes to the widely observed downregulation of gene expression in this devastating disease.
Our laboratory has been studying small noncoding RNA (sncRNA), single-stranded RNA (ssRNA), and more recently miRNA, and mRNA abundance, complexity, speciation, and stability in aging and AD brains for the last 36 years (Lewis et al., 1981; Colangelo et al., 2002; Jaber et al., 2017). We were the first laboratory to report, more than 10 years ago, that micro-RNAs (miRNAs) including miRNA-9, miRNA-124a, miRNA-125b, and others, are abundantly represented in fetal human hippocampus, are differentially regulated in aged brain, and that an alteration in specific miRNA abundance and speciation occurs in AD brain (Lukiw, 2007). Our work on the examination of miRNA and mRNA in several hundred high-quality, short PMI, and autopsied AD brains and controls shows clearly that there is a major problem with transcription and transcriptional control in AD. We have found a general rapid—and equal—decay both of miRNA and mRNA in AD brains and very little, if any, difference between the stability of miRNA or mRNA in short PMI brain samples from AD or age-matched controls (Rüegger and Großhans, 2012; Pogue et al., 2014). These data are consistent with the idea that altered miRNA-mediated processing of mRNA populations may contribute to atypical mRNA abundance patterns, an altered transcriptome, and neural dysfunction as is observed in AD brain. The Alkallas et al. paper begins “The abundance of mRNA is mainly determined by the rates of RNA transcription and decay”; using carefully assayed total mRNA yields per gram wet weight of AD and control tissue, quantitative RT-PCR, LED-Northern analysis, microRNA-ribosomal RNA (miRNA-rRNA) ratios, and RNA sequencing, we have no evidence that miRNAs or mRNAs are degraded any faster in the AD-affected brain, just that there a less initial abundance of them (Colangelo et al., 2002; Jaber et al., 2017; Lukiw, 2007; Pogue et al., 2014).
In addition, neurons have devised important ancillary controls on miRNA and mRNA abundance and stability in human brain cells and cells of the central nervous system (CNS) besides special miRNAs and RBPs not mentioned by Alkallas et al. These include (i) percent adenine+uridine (A+U) content of miRNA and mRNA primary sequence structure and content of A+U-enriched sequences which confer instability to ssRNA; (ii) the ability of sncRNAs and especially miRNAs to be concentrated and packaged into small brain cell-membrane-derived extracellular vesicles that can protect ssRNAs from ribonuclease-directed degradation; (iii) by ssRNAs adopting secondary and tertiary structures inaccessible to ribonuclease (a good example is the extremely lethal and immune-evading ssRNA Ebola virus); (iv) by miRNA, sncRNA, and mRNA interaction with multiple RBPs and/or other ssRNAs which bind to and extend miRNA and mRNA longevity under physiological conditions; (v) by miRNA, anti-miRNA, and/or mRNA circularization; or by any combination of these sometimes rare and exotic genetic regulatory mechanisms (Colangelo et al., 2002; Jaber et al., 2017; Lukiw, 2007; Rüegger and Großhans, 2012; Pogue et al., 2014).
Overall, there is a lot wrong with gene expression signaling, including significant transcriptional deficits, in AD, especially in the neocortex and hippocampus. As an extremely heterogeneous and insidious disease, there is probably an equal heterogeneity in the molecular-genetic, epigenetic, and pathogenetic mechanisms that drive the disruption of gene-expression processes leading to the alternately populated transcriptome characteristic of AD-affected brain.
References:
Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM, Krijgsveld J, Hentze MW. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell. 2012 Jun 8;149(6):1393-406. Epub 2012 May 31 PubMed.
Guo H, Ingolia NT, Weissman JS, Bartel DP. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature. 2010 Aug 12;466(7308):835-40. PubMed.
Duff MO, Olson S, Wei X, Garrett SC, Osman A, Bolisetty M, Plocik A, Celniker SE, Graveley BR. Genome-wide identification of zero nucleotide recursive splicing in Drosophila. Nature. 2015 May 21;521(7552):376-9. Epub 2015 May 13 PubMed.
Scheckel C, Drapeau E, Frias MA, Park CY, Fak J, Zucker-Scharff I, Kou Y, Haroutunian V, Ma'ayan A, Buxbaum JD, Darnell RB. Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. Elife. 2016 Feb 19;5 PubMed.
Lewis PN, Lukiw WJ, De Boni U, McLachlan DR. Changes in chromatin structure associated with Alzheimer's disease. J Neurochem. 1981 Nov;37(5):1193-202. PubMed.
Colangelo V, Schurr J, Ball MJ, Pelaez RP, Bazan NG, Lukiw WJ. Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling. J Neurosci Res. 2002 Nov 1;70(3):462-73. PubMed.
Jaber V, Zhao Y, Lukiw WJ. Alterations in micro RNA-messenger RNA (miRNA-mRNA) Coupled Signaling Networks in Sporadic Alzheimer's Disease (AD) Hippocampal CA1. J Alzheimers Dis Parkinsonism. 2017 Apr;7(2) Epub 2017 Mar 10 PubMed.
Lukiw WJ. Micro-RNA speciation in fetal, adult and Alzheimer's disease hippocampus. Neuroreport. 2007 Feb 12;18(3):297-300. PubMed.
Rüegger S, Großhans H. MicroRNA turnover: when, how, and why. Trends Biochem Sci. 2012 Oct;37(10):436-46. Epub 2012 Aug 23 PubMed.
Pogue AI, Hill JM, Lukiw WJ. MicroRNA (miRNA): sequence and stability, viroid-like properties, and disease association in the CNS. Brain Res. 2014 Oct 10;1584:73-9. Epub 2014 Apr 4 PubMed.
View all comments by Walter J. LukiwMcGill University
Professor Lukiw raises several concerns that we also share with regard to our analyses, and we have tried to address them to the point that was possible in the paper. Nonetheless, additional experiments and analyses are warranted to fully address these issues.
First, Professor Lukiw points out that the RNA-seq data set used in our paper (from Duff et al., 2015) contains an RNA sample from just one 18-year-old male Caucasian brain. We used this RNA-seq set to derive a regulatory network of mRNA stability in the normal brain. In the paper, we compared the stability estimates from this sample to those from Illumina BodyMap (Alkallas et al., Fig 3f), and observed overall consistent measurements. Importantly, the stability model that we obtained for two RBPs and four miRNAs was able to explain the brain-specific stability measurements that were consistent between these two brain samples (Alkallas et al., Supplementary Fig 9). It would be interesting to see to what extent these trends can also be reproduced in other RNA-seq data sets.
Secondly, Professor Lukiw correctly points out the potential degradation of RNAs in postmortem samples of AD patients (from Scheckel et al., 2016). There are a few lines of evidence suggesting that our conclusions about destabilization of RBFOX1 targets in AD were not confounded by postmortem RNA degradation. (i) In our analyses we compare AD brains to brains of non-AD individuals, which in principle should be affected similarly by postmortem RNA degradation as long as sample collection procedures were consistent. Therefore, destabilization of a specific set of genes in AD brains compared to normal would likely reflect an underlying biological process. (ii) We see specific destabilization of RBFOX1 targets in AD compared to other genes (Alkallas et al., Fig 5f), which is difficult to explain simply based on postmortem RNA degradation, which presumably affects all mRNAs, or at least should not be correlated with the presence of RBFOX1 binding site. This observation also holds true when we compare RBFOX1 targets to other neuron-specific genes (Alkallas et al., Supplementary Fig 13). (iii) We also tried to reproduce these analyses in two other AD cohorts: an RNA-seq dataset from Magistri et al. (2017), and a microarray dataset from Narayanan et al. (2014). While each of these datasets have their limitations, we observed that RBFOX1 targets are specifically downregulated in these cohorts too (Alkallas et al., Fig 5e and Supplementary Fig 14). I also want to point out that we did not observe destabilization of the targets of the four brain-specific miRNAs in AD. Instead, there may be an AD-associated stabilization of the targets of miR-124, which points to downregulation of miR-124. This is consistent with previous publications (e.g., Lukiw 2007, which reported a trend toward downregulation of miR-124a in AD). Also, it would be interesting to see how the stability of targets of miRNAs that are not brain-specific changes in AD. For example, Jaber et al. (2017) show upregulation of miR-34a and miRNA-146a in sporadic AD, and subsequent downregulation of their targets, which most likely reflects rapid degradation of these mRNAs as a result of miRNA upregulation.
Overall, we share Professor Lukiw’s opinion that AD is a heterogeneous disorder with abnormalities in gene expression at different layers of transcriptional and post-transcriptional regulation, and studying a regulatory model that encompasses both transcriptional and post-transcriptional layers in the context of AD is certainly warranted.
References:
Duff MO, Olson S, Wei X, Garrett SC, Osman A, Bolisetty M, Plocik A, Celniker SE, Graveley BR. Genome-wide identification of zero nucleotide recursive splicing in Drosophila. Nature. 2015 May 21;521(7552):376-9. Epub 2015 May 13 PubMed.
Scheckel C, Drapeau E, Frias MA, Park CY, Fak J, Zucker-Scharff I, Kou Y, Haroutunian V, Ma'ayan A, Buxbaum JD, Darnell RB. Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. Elife. 2016 Feb 19;5 PubMed.
Magistri M, Velmeshev D, Makhmutova M, Faghihi MA. Transcriptomics Profiling of Alzheimer's Disease Reveal Neurovascular Defects, Altered Amyloid-β Homeostasis, and Deregulated Expression of Long Noncoding RNAs. J Alzheimers Dis. 2015;48(3):647-65. PubMed.
Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, Zhang B, Zhang C, Lamb JR, Xie T, Suver C, Molony C, Melquist S, Johnson AD, Fan G, Stone DJ, Schadt EE, Casaccia P, Emilsson V, Zhu J. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol. 2014 Jul 30;10:743. PubMed.
Lukiw WJ. Micro-RNA speciation in fetal, adult and Alzheimer's disease hippocampus. Neuroreport. 2007 Feb 12;18(3):297-300. PubMed.
Jaber V, Zhao Y, Lukiw WJ. Alterations in micro RNA-messenger RNA (miRNA-mRNA) Coupled Signaling Networks in Sporadic Alzheimer's Disease (AD) Hippocampal CA1. J Alzheimers Dis Parkinsonism. 2017 Apr;7(2) Epub 2017 Mar 10 PubMed.
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