Scientists can easily track mRNAs from cradle to grave in cultured cells, but monitoring them in tissues presents a challenge. Now, a group led by Hamed Najafabadi at McGill University in Montreal describe a new method claimed to yield more accurate results. As reported October 13 in Nature Communications, the authors fix a bias in a previous algorithm to estimate RNA decay rates in situ. Applying it to brain tissue, the researchers calculated different stabilities of RNAs. They also identified two RNA-binding protein families, RBFOX and ZFP36, and four microRNAs, miR-124, miR-29, miR-9, and miR-128, that dominate regulation of RNA stability. RBFOX1, which binds mRNAs encoding synaptic proteins, drops in Alzheimer’s brains as synaptic RNA decay accelerates.

  • New method estimates mRNA decay more accurately.
  • Two RNA-binding proteins, four microRNAs control brain mRNA stability.
  • In AD, RBFOX1 expression drops and synaptic mRNA decays faster.

Some researchers cautioned that AD sample tissue was limited and that long postmortem intervals compromise some of the RNA data sets, but others welcomed the potential of this approach. “The study adds to the realization that post-transcriptional regulation is of high importance in the brain,” wrote Evgenia Salta at VIB/KU Leuven, Belgium (see full comment below). “This novel approach increases the depth of information that can be extracted from [omics] data sets.”

For years, researchers have used microarrays or RNA sequencing (RNA-Seq) to quantify gene expression, but these offer only a snapshot of steady-state mRNA levels. Reasoning that RNAs still containing intron sequences reflect levels of pre-mRNAs, which are quickly spliced into intron-less mRNAs, scientists realized they could calculate relative mRNA decay rates by subtracting changes in the synthesis rate (introns) from changes in steady-state levels (exons) (Gaidatzis et al., 2015). “It’s a clever method,” said Najafabadi. However, when his group used it to compare mRNA decay rates across different tissues, they ran into problems. “We were seeing results we couldn’t explain,” he told Alzforum. “We saw mRNAs for brain-specific pathways that were less stable in the brain than in non-brain tissues. It didn’t make sense.” The researchers figured out that introns can linger in cells when transcription rates are high and the splicing machinery cannot keep up. Using intron levels as the sole indicator of synthesis leads to an overestimation of mRNA decay for RNAs that are quickly transcribed, while underestimating slowly transcribed RNAs. 

RNA Fireworks. More (red) and less (blue) stable mRNAs in the brain have binding sites for two proteins and four microRNAs that stabilize (square) or destabilize (diamond) the nucleic acids. [Courtesy of Alkallas et al., Nature Communications, 2017.]

To correct for this, Najafabadi and first author Rached Alkallas created an algorithm that takes into account the kinetics of RNA processing. To test the new technique, they used RNA-Seq data sets from two breast carcinoma cell lines (Furlow et al., 2015; Minn et al., 2005). The researchers chose these data sets because previous studies using a direct measure of RNA stability that only worked in cultured cells had revealed dozens of mRNAs whose stability differed between the two lines. Najafabadi’s algorithm yielded results that were consistent with the direct measure. Importantly, it roundly outperformed the simple estimates based on intron versus exon levels, which, in some cases, suggested decay rates were faster in one cell line than in the other, when it was the other way around. 

The researchers then applied their new tool to obtain one of the first broad surveys of mRNA stability across human tissue. They analyzed RNA-Seq profiles from samples of 20 different tissues (Duff et al., 2015). They found stark, tissue-specific differences in mRNA stability, and that these differences were conserved between humans and mice.

Najafabadi and colleagues then turned their attention to the brain because analysis of its RNA decay profile suggested many processes are regulated differently in this tissue than in others. To get a handle on which factors might be shaping brain RNA stability profiles, the authors scanned the 3' UTRs of brain mRNAs for RNA-binding protein (RBP) and miRNA binding sites. They then correlated those sequences with the stability estimates for brain mRNAs. Strikingly, sites for just the two RBP families—RBFOX and ZFP36—and the four miRNAs—miR-124, miR-29, miR-9, and miR-128—surfaced as the main regulators. One of the RNA-binding proteins, RBFOX, is highly expressed in brain and stabilizes mRNAs. Counterintuitively, the other mRNA stabilizing protein, ZFP36, is essentially absent in brain. It turns out ZFP36 destabilizes neural-specific mRNAs in nonneural tissues.

All of the miRNAs destabilize their target transcripts and, interestingly, all have been implicated in neurodegenerative disorders. For example, researchers have reported miR-128 dysregulation in AD (Lukiw, 2007), miR-29a levels correlated with AD progression (Apr 2017 conference news), and reductions in miR-9 and miR-124 have been found in the brains of people with frontotemporal dementia (Nov 2014 conference news). 

Integrating data from more than 2,000 transcripts, the authors then built a network that linked these six key factors to their mRNA targets (see image above). Is this network meaningful? The authors turned to previous experimental data, reviewing crosslinking studies that mapped protein-binding sites in the mRNAs of living cells, and studies on the effects of miRNAs on specific mRNAs. They found that their network better predicted protein-RNA and miRNA binding than did simple examination of sequence motifs. For example, their networks had a 3.6-fold better chance of identifying mRNA targets that were degraded by miR-124 than simply searching for mRNAs with miR-124 binding sites.

Noticing that the RBFOX component of the network was most highly enriched for genes involved in synaptic transmission, Alkallas and Najafabadi wondered if it might be disrupted in AD. The researchers analyzed RNA-Seq data from postmortem prefrontal cortices of six patients with advanced AD, as judged by a Clinical Dementia Rating score of between four and five, with that from five age- and gender-matched controls (Scheckel et al., 2016). Surveying nearly 8,500 mRNAs, they found a generalized destabilization of brain-specific transcripts in the AD brains. The predicted targets of RBFOX took the biggest hit—a drop of about 25 percent on average. With 37 mRNAs, synaptic transmission emerged as the pathway most highly represented among the top 500 destabilized transcripts.

The researchers also found that expression of the RBFOX family member RBFOX1 was lower in brain tissue of AD data sets than in controls. This supports previous transcriptome microarray data from 310 AD patients with moderate to severe AD, which showed a halving of RBFOX1 mRNA (Narayanan et al., 2014). Alkallas found the lower RBFOX1 expression levels in AD samples correlated with a dearth of synaptic mRNAs predicted to be RBFOX1 targets. When the researchers knocked down RBFOX1 in primary human neural progenitor cells, they observed an mRNA stability signature similar to that of AD brains.

Benjamin Wolozin at Boston University, who found that RNA decays faster in a mouse model of tauopathy, welcomed the findings, but worried about the limitations of computational studies. Although he complimented the authors’ correction of a bias in the original exon-intron algorithm, he feared there might be other important biases for which they have still not accounted. “There are lots of potential biases that are simply unknown, particularly when looking at disease states,” he said. Salta noted that further experimental validation is needed. “Widespread alterations in the machinery involved in RNA metabolism that perturb, for instance, RNA splicing or intron stability can occur in neurodegenerative disorders, and this would affect the reliability of the algorithm,” she wrote. Najafabadi acknowledged the limitations, but emphasized that this is a start and the algorithm can be refined as new information emerges.

Walter Lukiw, Louisiana State University, New Orleans, pointed out that the survey of human tissues included total brain RNA from just one person and cerebellar RNA from 10 pooled samples. The cerebellum is relatively spared in AD. He also noted that the RNA-Seq data sets of AD brains included samples with up to 18-hour postmortem intervals.After about three hours, accurate sampling of the total mRNA of brain tissue is not easily obtainable,” he wrote. Najafabadi acknowledged the problem. “That’s absolutely a concern,” he said. “But given that we are comparing postmortem AD samples with postmortem control samples, we predict that biases introduced by postmortem decay cancel each other out.”

Najafabadi also noted that it remains challenging to separate the contributions of different cell types, and to estimate how neuronal loss affects RNA decay. He would like to collaborate with labs doing single-cell RNA-Seq and test computational methods to tease apart neuron-specific signatures. Studies like these might clarify open questions such as what causes the generalized RNA decay seen in AD brains, and what roles the four miRNAs play.

The when, where, and how of RBFOX dysregulation in Alzheimer’s intrigued researchers. Salta wants to know when and where in the brain RBFOX levels drop. Najafabadi wants to test the effect of overexpressing RBFOX in mouse models of AD. He also wants to examine other neurologic disorders, noting a recent link between RBFOX and autism (Lee et al., 2016). RBFOX1 alternatively splices genes involved in neural development (Zhou et al., 2007) and may play a role in the alternative splicing of Aβ precursor protein (APP), which could affect Aβ aggregation (Alam et al., 2014).—Marina Chicurel 


  1. 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.


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  2. 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, 2012Pogue 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.


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  3. 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.


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    . Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. Elife. 2016 Feb 19;5 PubMed.

    . 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.

    . Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol. 2014 Jul 30;10:743. PubMed.

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    . 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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News Citations

  1. Look in the MiR: MicroRNA Fans Neurogenesis in Old Alzheimer’s Mice
  2. Do MicroRNAs Cause Mayhem Across Frontotemporal Dementia Spectrum?

Paper Citations

  1. . 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.
  2. . Mechanosensitive pannexin-1 channels mediate microvascular metastatic cell survival. Nat Cell Biol. 2015 Jul;17(7):943-52. Epub 2015 Jun 22 PubMed.
  3. . Genes that mediate breast cancer metastasis to lung. Nature. 2005 Jul 28;436(7050):518-24. PubMed.
  4. . Genome-wide identification of zero nucleotide recursive splicing in Drosophila. Nature. 2015 May 21;521(7552):376-9. Epub 2015 May 13 PubMed.
  5. . Micro-RNA speciation in fetal, adult and Alzheimer's disease hippocampus. Neuroreport. 2007 Feb 12;18(3):297-300. PubMed.
  6. . Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. Elife. 2016 Feb 19;5 PubMed.
  7. . Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol. 2014 Jul 30;10:743. PubMed.
  8. . Cytoplasmic Rbfox1 Regulates the Expression of Synaptic and Autism-Related Genes. Neuron. 2016 Jan 6;89(1):113-28. Epub 2015 Dec 10 PubMed.
  9. . Alternative splicing regulation of APP exon 7 by RBFox proteins. Neurochem Int. 2014 Dec;78:7-17. Epub 2014 Aug 11 PubMed.

Further Reading

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Primary Papers

  1. . 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.