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# Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease

• Nature Communicationsvolume 8, Article number: 909 (2017)
• doi:10.1038/s41467-017-00867-z
Accepted:
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## Abstract

The abundance of mRNA is mainly determined by the rates of RNA transcription and decay. Here, we present a method for unbiased estimation of differential mRNA decay rate from RNA-sequencing data by modeling the kinetics of mRNA metabolism. We show that in all primary human tissues tested, and particularly in the central nervous system, many pathways are regulated at the mRNA stability level. We present a parsimonious regulatory model consisting of two RNA-binding proteins and four microRNAs that modulate the mRNA stability landscape of the brain, which suggests a new link between RBFOX proteins and Alzheimer’s disease. We show that downregulation of RBFOX1 leads to destabilization of mRNAs encoding for synaptic transmission proteins, which may contribute to the loss of synaptic function in Alzheimer’s disease. RBFOX1 downregulation is more likely to occur in older and female individuals, consistent with the association of Alzheimer’s disease with age and gender.

## Introduction

The mRNA decay rate is a key determinant of steady-state mRNA abundance. Measuring the mRNA decay rate generally involves time series examination of mRNA abundance following blockage of transcription1, 2, or monitoring the abundance of labeled mRNA using high-throughput pulse-chase methods3, 4. These methods are mostly suitable for analysis of cell cultures, however, and cannot be used for measuring mRNA decay rate in tissue samples.

In principle, it should be possible to measure the decay rate of mRNAs by decoupling the transcription rate from the steady-state mRNA abundance. It has been suggested that, while exonic read counts in RNA-seq data correspond to steady-state mRNA abundance, changes in the abundance of intronic reads can be used to estimate the change in transcription rate5,6,7. Therefore, RNA-seq reflects a snapshot of both steady-state mRNA level and transcriptional activity, providing the possibility for deconvolving the mRNA decay rate from RNA-seq data.

This concept has been recently suggested to capture the differential mRNA decay rate in a wide set of contexts5, where the change in mRNA half-life is estimated as the difference of the logarithm of fold-change of exonic reads and the logarithm of fold-change of intronic reads (Δexon–Δintron). Here, we show that this measure is highly biased, and generally overestimates the stability of slow-transcribing mRNAs and underestimates the stability of fast-transcribing mRNAs. We propose a generalizable approach for correcting this bias, and use it to investigate the post-transcriptional regulatory programs of a panel of human tissues.

Our results indicate that mRNA stability plays an integral role in shaping the transcriptomes of all tested tissues, particularly those of the central nervous system. We show that a substantial portion of the brain mRNA stability profile can be explained by the functions of two RNA-binding protein families (the RBFOX and ZFP36 families) and four miRNAs (miR-124, miR-29, miR-9, and miR-128). The RBFOX targets are enriched for mRNAs that encode synaptic transmission proteins, and are destabilized in the brains of individuals with Alzheimer’s disease (AD). We show that knockdown of RBFOX1 can partially recreate the AD stability profile, and its expression rescues the normal transcriptome, suggesting a link between dysregulation of RBFOX1 and AD.

## Results

### Decoupling of changes in transcription and mRNA decay rates

Several recent studies have used the difference of the logarithm of fold-change of exonic reads and the logarithm of fold-change of intronic reads (Δexon–Δintron) as an estimate of the change in mRNA half-life5, 8. However, by considering a simple model of RNA metabolism (Fig. 1a) and solving the kinetic equations of this model (Methods and Supplementary Fig. 1), we found that Δexon–Δintron is affected by a “bias term” that depends on the maximum capacity of the pre-mRNA processing machinery, which is limited in the cell9, as well as the change in transcription rate (Fig. 1b). The presence of this bias leads to a negative correlation between Δexon–Δintron and differential transcription rate. In other words, even in the absence of any change in mRNA stability, Δexon–Δintron would still be positive for genes that were transcriptionally downregulated, and negative for genes that were transcriptionally upregulated (Fig. 1c).

To test this model, we measured Δexon–Δintron for human genes using RNA-seq data from a panel of 20 human tissues10. Indeed, we observed a significant negative trend between Δexon–Δintron and the change in transcription rate (Fig. 1d and Supplementary Fig. 2a), in agreement with our model. Other data sets that we analyzed also showed the same trend, including RNA-seq data from Illumina BodyMap 2.0, data from nine human cell lines from ENCODE11, and three RNA-seq datasets from mouse12,13,14 (Supplementary Fig. 2a, b), suggesting that this bias is not data-set-specific or species-specific. This correlation was not due to inaccuracies in estimating Δexon and Δintron from low read counts, since a negative correlation could be observed for gene sets with varying read coverage (Fig. 1e). In fact, higher expression was associated with larger magnitudes of bias (Fig. 1e), consistent with our kinetic model in which saturation of the pre-mRNA processing machinery results in a steeper bias (Fig. 1c). This bias in Δexon–Δintron can substantially confound downstream analysis of mRNA stability programs. For example, in several tissues, pathways that are transcriptionally downregulated generally appear to be stabilized at the mRNA level (Supplementary Fig. 2c), and in other tissues the bias in Δexon–Δintron partially masks the synergy between transcriptional and post-transcriptional regulation of pathways (Supplementary Fig. 2d).

We devised a computational framework that estimates the transcription rate-dependent bias from RNA-seq data and subtracts it from Δexon–Δintron (Methods, Fig. 2a and Supplementary Fig. 3), providing unbiased estimates of differential mRNA half-life. Although this framework makes simplifying assumptions, such as invariability of some of the kinetic parameters of mRNA metabolism (see Methods), our simulations suggest that even when these assumptions are not met, this framework should still provide more accurate estimates of mRNA stability compared to uncorrected Δexon–Δintron (Supplementary Fig. 4). To empirically evaluate our method, we analyzed the RNA-seq data15 from the breast carcinoma cell line MDA and the in vivo-selected highly metastatic sub-line MDA-LM216. We analyzed this dataset because a large number of mRNAs were previously identified as stabilized or destabilized in MDA-LM2 cells compared to the parental MDA line17 using BRIC-seq18. Our analysis showed that for highly biased genes, uncorrected Δexon–Δintron is not an accurate estimate of differential stability, as expected from our kinetic model. In fact, when the magnitude of the bias term was large, uncorrected Δexon–Δintron was on average higher for LM2-destabilized genes and lower for LM2-stabilized genes (Fig. 2b). In contrast, our framework provided estimates that were overall consistent with the BRIC-seq differential stability measurements, even for highly biased genes (Fig. 2b and Supplementary Fig. 5a, b). To further evaluate our method, we selected six additional genes that did not have statistically significant differences in BRIC-seq measurements between MDA-parental and MDA-LM2 cells, and had the largest bias based on analysis of RNA-seq data (see Methods): three of these genes appeared to be significantly more stable in LM2 cells based on Δexon–Δintron, but after bias correction were inferred to be more stable in MDA cells; the other three genes showed the reverse pattern. We measured the stability of these genes in MDA-parental and MDA-LM2 cells by α-amanitin inhibition of transcription followed by qRT-PCR. Of the six genes that we examined, four genes indeed showed significant differential stability (P < 0.05). In all four cases, the direction of the change in stability was consistent with the bias-corrected predictions but not with the uncorrected Δexon–Δintron (Fig. 2c). Additional analysis for comparison of Δexon–Δintron and our unbiased estimates of stability can be found in Supplementary Fig. 5c, d). We have implemented our computational framework in a software package available at https://github.com/csglab/REMBRANDTS.

### The mRNA stability programs of human brain

We sought to explore the post-transcriptional programs across human tissues using our unbiased estimate of mRNA stability. We analyzed the RNA-seq data from a panel of 20 diverse human tissues10 (data available at http://csg.lab.mcgill.ca/sup/pan_stability/), and observed widespread tissue-specific differences in the mRNA stability profiles (Supplementary Fig. 6). Comparison of these stability profiles with those obtained from a panel of mouse tissues suggests a high degree of conservation across species (Supplementary Fig. 7). Furthermore, genes that are involved in the same pathway are often co-regulated post-transcriptionally (Supplementary Fig. 8). In particular, pathway analysis of mRNA stability profiles revealed a large number of biological processes specifically upregulated or downregulated in the brain, suggesting a prominent role of post-transcriptional programs in shaping the brain transcriptome. This observation prompted us to take a closer look at regulation of mRNA stability in this tissue, since systematic analyses of mRNA stability programs in the brain are scarce, and it is not clear which factors contribute the most to the mRNA stability landscape of the brain.

We began by examining the potential binding sites of all human RBPs with available sequence preferences19 as well as all conserved miRNAs20, and used multiple linear regression to identify factors whose binding to 3′-untranslated region (UTR) of mRNA was predictive of mRNA stability in the brain (see Methods for more details). This analysis identified four miRNAs and two RBPs that were significantly predictive of brain mRNA stability (Fig. 3a). Specifically, presence of 3′ UTR binding sites for miR-124, miR-29, miR-9 and miR-128 was significantly associated with reduced mRNA stability, whereas binding sites of RBFOX and ZFP36 families of RBPs were significantly associated with increased stability.

Of the four miRNAs that were significant, three miRNAs, miR-124, miR-9, and miR-128, show highly specific expression in the brain, based on a dataset of miRNA expression profiles across 40 human tissue samples21 (Fig. 3b), which is consistent with the observed brain-specific destabilization of their targets. Of these miRNAs, miR-124 and miR-9 are involved in development and function of the nervous system22, 23, and de-regulation of miR-128 is associated with tumors of the nervous system24, 25. All four miRNAs have been previously shown to be able to decrease the abundance of their target transcripts26,27,28,29,30, suggesting that they can indeed destabilize their targets. Also, of the two RBP families whose target transcripts were significantly up-regulated in brain, the RBFOX proteins show brain-specific expression (Fig. 3c), consistent with previous studies suggesting that RBFOX proteins stabilize their target mRNAs19, 31. In contrast, ZFP36 destabilizes its targets by binding to their 3′ UTRs32, in agreement with highly specific downregulation of ZFP36 gene and its paralog, ZFP36L1, in the brain (Fig. 3c). This suggests that ZFP36/ZFP36L1 proteins bind to the 3′ UTR of brain-specific mRNAs to destabilize them in non-neural tissues, resulting in highly specific expression of these genes primarily in brain where ZFP36/ZFP36L1 proteins are absent. The genes encoding for RBFOX and ZFP36 families of proteins themselves appear to be mostly regulated at the transcription level, based on analysis of intronic reads (Fig. 3d). Therefore, we propose a regulatory model in which brain-specific transcriptional activation/inhibition of these two RBPs establishes a post-transcriptional program that stabilizes brain-specific mRNAs (Fig. 3e). Together with rapid decay of non-neural transcripts by miRNAs, this parsimonious regulatory model, which consists of only six regulatory factors (Fig. 3e), explains ~10% of the observed variance of the mRNA stability profile of the brain, compared to ~31% of the variance that is reproducible across samples/platforms (Fig. 3f). Furthermore, this model is able to distinguish transcripts that show reproducible brain-specific stabilization from those that show destabilization, with an AUROC of 0.86 (area under the receiver operating characteristic curve, Supplementary Fig. 9).

### Dysregulation of RBFOX programs in Alzheimer’s disease

We combined the stability profiles of mRNAs in human brain with the binding site predictions of the miRNAs and RBPs to reconstruct a high-confidence network of mRNA stability programs in brain (available at http://csg.lab.mcgill.ca/sup/pan_stability/). We considered a transcript to be a regulatory target of one of the six factors discussed in the previous section if that transcript had a putative binding site for that factor in its 3′ UTR, and the presence of the binding site was necessary to explain the stability of the transcript in the brain even after taking into account the effect of other stability factors (see Methods). This network overall encompasses 2499 interactions between six regulatory factors (four miRNAs and two RBPs) and 2138 transcripts (Fig. 4a). Several lines of evidence suggest that the edges in this regulatory network are more likely to represent bona-fide regulatory interactions than sequence-based binding site predictions alone. Specifically, high-confidence predicted RBFOX stability targets are 2.8-fold more likely to be bound by RBFOX proteins than transcripts that only have a match to the RBFOX motif in their 3’ UTR (Fisher’s exact test P < 4 × 10–12, Fig. 4b), based on comparison to HITS-CLIP data of Rbfox1/2/3 in mouse brain33 (see Methods). Similarly, ZFP36 high-confidence targets are more likely to be bound and downregulated by ectopically expressed ZFP36 in HEK293T cells34 (Fisher’s exact test P < 0.05, Fig. 4c). High-confidence predicted targets of miR-124 are also 3.6-fold more likely to be downregulated in HeLa cells that express an ectopic copy of miR-12426, compared to transcripts that only have a match to miR-124 seed sequence (Fisher’s exact test P < 3 × 10–6, Fig. 4d). Furthermore, our high-confidence network is significantly enriched for experimentally validated interactions that are collected from the literature for each of the four miRNAs miR-124, miR-128, miR-29, and miR-935 (Fig. 4e). The high-confidence 3’UTR binding sites of all these four miRNAs are on average significantly more conserved than their adjacent sequences, including the binding sites that were not previously validated in the literature (Supplementary Fig. 10a). Interestingly, the intersection of our network with the predictions of TargetScan20, which uses conservation to identify miRNA targets, shows an even larger enrichment of functional miRNA-target interactions compared to either method alone (Supplementary Fig. 10b, c), suggesting that our method provides orthogonal information compared to conservation-based approaches.

Among the three RBFOX proteins, we found that the transcription of RBFOX1 gene is significantly reduced in the AD brain compared to control samples (two-tailed t-test P < 0.008, Supplementary Fig. 15a). This observation is supported by analysis of a larger dataset of microarray measurements39, which shows ~ 2-fold decrease in the abundance of RBFOX1 mRNA in AD subjects compared to normal individuals (two-tailed t-test P < 3 × 10–53, Fig. 5c and Supplementary Fig. 15b). In contrast, other neuron-specific genes showed only a median downregulation of 1.2-fold in AD (Supplementary Fig. 15b). This suggests that RBFOX1 downregulation may contribute to the deregulation of the RBFOX stability program in AD. Indeed, we observed that RNAi-mediated knockdown of RBFOX1 in differentiated primary human neural progenitor cells40 leads to a transcriptome shift that is significantly correlated with the AD stability signature (r = 0.13, P < 4 × 10–33, Fig. 5d). Furthermore, the predicted RBFOX stability targets are most highly enriched among transcripts that are both destabilized in AD and downregulated by RBFOX1 knockdown (Fig. 5d). This can also be observed in mouse neurons, where knockdown of Rbfox1/3 leads to downregulation of the orthologs of AD-destabilized RBFOX targets, and ectopic expression of a cytoplasmic form of Rbfox1 rescues the expression of these genes (Supplementary Fig. 16).

## Discussion

By inferring mRNA half-life from RNA-seq data, we were able to obtain a global view of the mRNA stability landscape of human tissues, which revealed a prominent role of mRNA stability in shaping the transcriptome of the brain. By combining the brain mRNA stability signature with the consensus sequence binding preferences of miRNAs and RBPs, we identified four miRNAs and two RBPs as the primary determinants of mRNA stability in the brain. It is, however, important to note that the consensus binding preferences of RBPs and miRNAs are often poor predictors of their in vivo binding sites, and spurious matches to these consensus sequences are abundant. Among factors that contribute to such false positive hits are the RNA structure41 and the inaccuracies in known consensus binding sequences42. Our high-confidence stability network (Fig. 4a) reduces these false positives by identifying “functional” targets of each factor, i.e. mRNAs that not only have a sequence match to its consensus binding sequence, but also have a stability that is consistent with the effect of binding of that factor. Interestingly, even though we did not use structural information to construct our high-confidence network, the expected structural preferences of RBPs are reflected in this network, with high-confidence RBFOX1 and ZFP36 binding sites showing higher local accessibility compared to spurious sequence matches (Supplementary Fig. 17a, b). Furthermore, high-confidence binding sites of miR-124 contain conserved sequences that are compatible with extended base-pairing beyond the miR-124 seed region, in contrast to spurious matches to miR-124 recognition sequence (Supplementary Fig. 17c). These observations suggest that by combining mRNA stability with consensus binding preferences, we can overcome some of the challenges in computational identification of the true binding sites of RBPs and miRNAs.

Among the factors that we studied in brain, RBFOX proteins are primarily known for their role in regulating alternative splicing43, and more recently as potential regulators of mRNA stability19, 31. Surprisingly, we found that RBFOX proteins regulate transcripts that are functionally relevant to development of Alzheimer’s disease (AD) and are destabilized in brains of AD patients. Intriguingly, the RBFOX1 gene itself is downregulated in AD, consistent with previous studies that have found an association between rare heterozygous deletions overlapping the RBFOX1 locus and early-onset familial AD44. Furthermore, we found that RNAi-mediated knockdown of RBFOX1 is able to partially recreate the AD stability signature in human neural progenitor cells, suggesting a role of RBFOX1 in AD-associated changes in neural cells.

An almost-invariable feature of Alzheimer’s disease is synaptic impairment, which is often associated with aggregation of β-amyloids45. RBFOX1 is previously reported to regulate the splicing of amyloid precursor protein (APP), with de-regulation of this splicing program leading to an APP isoform that contributes to β-amyloid aggregation46. Here, our analyses suggest an alternative association between RBFOX1 and AD, where downregulation of RBFOX1 may directly affect the stability and abundance of mRNAs that encode synaptic transmission proteins. If this is the case, then we should expect a correlation between the extent of RBFOX1 downregulation and the severity of de-regulation of synaptic transmission pathway in AD patients. Indeed, there is a strong correlation between RBFOX1 expression and abundance of RBFOX-regulated synaptic genes across 310 AD individuals (median r = 0.62, two sided t-test P < 3 × 10–6, Fig. 5e).

These findings provide new insights into pathways that may lead to increased risk of development of AD. For example, while the primary risk factors of AD are age and gender47, the mechanisms through which these factors affect the AD-susceptibility are largely unknown. Surprisingly, we found that RBFOX1 expression is highly correlated with gender in both normal and AD individuals, and with age in normal individuals. Specifically, RBFOX1 is expressed at lower levels in females (Fig. 5c) and older subjects (Figs. 5c, f), consistent with higher risk of AD in these individuals. These associations, however, can only partially explain the large magnitude of differences that are seen between AD and healthy individuals (Figs. 5c, f), consistent with the notion that AD is a complex and multi-factor disease48. Furthermore, interpretation of AD transcriptomics data is particularly challenging given the confounding effect of neuronal loss on gene expression measurements, despite our attempts to control for this confounding factor in our analyses (Supplementary Figs. 13 and 15).

Together with the functional relevance of RBFOX1 stability targets, and the similarity of the expression profile of RBFOX1-deficient cells with the stability profile of AD brain, these observations suggest that defects in RBFOX1 stability program may contribute to the loss of synaptic function in AD. We note that, based on our analysis of tissue-specific RNA-seq data, mRNA stability plays a pivotal role in shaping the transcriptome and the landscape of active biological processes in many tissues (Supplementary Figs. 68), and therefore de-regulation of programs that modulate mRNA stability are likely to lead to various diseases beyond those of the nervous system. Our method for obtaining unbiased estimates of mRNA stability can be applied to a wide range of diseases with available RNA-seq data to reveal disease-associated stability programs.

## Methods

### RNA-seq and microarray data

For analysis of human tissues, RNA-seq data of 20 human tissues were obtained from ref. 10 (SRA accession SRP056969). For analysis of AD-associated stability programs, RNA-seq data from brain tissue of nine AD and eight control individuals were obtained from ref. 37 (GEO accession GSE53697). For analysis of MDA-parental and MDA-LM2 cells, RNA-seq data from ref. 15 were used (GEO accession GSE45162). Reads were mapped to the hg19 assembly of human genome using TopHat249 (or HISAT250 in case of MDA/LM2 data) with default parameters. Intronic and exonic coordinates of genes were extracted from GENCODE v1951, and gene-level read counts were calculated for introns and exons separately using HTSeq52, including only reads with MAPQ score ≥ 30. Three AD and three control individuals from ref. 37 were excluded, as analysis of their RNA-seq data resulted in a disproportionately large number of genes with zero mapped reads and an unusual clustering of these samples (Supplementary Fig. 18), possibly due to low sequencing read quality.

Mouse tissue RNA-seq data were obtained from ref. 53 (GEO accession GSE29278), and were mapped to mm10 assembly using HISAT250. Gene-level intronic and exonic read counts were obtained as above, using Ensembl54 release 87 annotations.

Microarray data from 310 AD and 157 control individuals were obtained from ref. 39. Gene-level abundances in each sample were obtained by averaging the relative intensities of probes that mapped to the same gene (logarithm of ratio of probe intensity between the two channels, with one channel representing pooled RNA reference sample).

Gene-level exonic and intronic read counts for Illumina BodyMap 2.0, ENCODE RNA-seq data for cell lines HMEC, NHEK, HUVEC, HSMM, NHLF, H1 hESC, HepG2, GM12878, and K562, as well as RNA-seq datasets presented in Supplementary Figs. 2b and 5 were obtained from ref. 5. Other RNA-seq and microarray measurements used in this work were taken directly from the supplementary data of their respective publications.

### Modeling the pre-mRNA and mRNA abundance

The model of RNA metabolism we used includes three main steps (Fig. 1a). For each gene, DNA is first transcribed to produce pre-mRNA at rate V t, following zero-order production kinetics with rate constant k t. Then, the pre-mRNA is processed into mature mRNA at rate V p following Michaelis–Menten kinetics, with parameters V p,max as the maximum processing rate obtained at saturating levels of the pre-mRNA, and Michaelis constant K p representing the pre-mRNA concentration at which V p is equal to half of V p,max. Michaelis-Menten is one of the simplest approaches to model kinetics of saturation, and is applied widely to enzymatic reactions, including RNA processing9. The mature mRNA is degraded at rate V d, which follows first-order elimination kinetics5, with a decay rate constant of k d. At steady state, the input and output of the system must be equal, and therefore the rate of transcription is equal to the mRNA decay rate5, as well as the rate of mRNA processing.

Subsequently, we modeled the ratio of abundance of each of pre-mRNA and mature mRNA between any two samples s and s′ as a function of k t and kt, representing the transcription rate in the two samples, respectively, and k d and kd, representing the decay rate constant in the two samples, respectively (Fig. 1b and Supplementary Fig. 1). Values of V p,max and K p were assumed to remain constant between two samples for simplification. We note that these parameters mainly depend on the availability of the RNA processing machinery, which is ubiquitously expressed across most cells55, 56, and the intrinsic properties of the pre-mRNA/processing machinery complex. Therefore, the assumption of invariability of V p,max and K p for each gene across tissues/samples is reasonable in most cases. Together with the assumption of steady sate, this model results in a set of equations that are solved to identify the relationship among the abundance of pre-mRNA, the abundance of mature mRNA, and the differential rates of transcription and decay between two samples, as shown in Fig. 1b and Supplementary Fig. 1. We note that solving the kinetic equations in the absence of the above simplifying assumptions (Supplementary Fig. 4a) suggests that when the variation in V p,max is considerably smaller in magnitude than the variation in degradation rate, its effect is negligible and can be ignored. The same is true for K p when its variation is considerably smaller than the variation in the concentration of pre-mRNA. Supplementary Fig. 4b also shows that even when V p,max and K p are highly variable, the method presented in this paper still captures the change in mRNA stability better than uncorrected Δexon–Δintron.

### Removing bias from Δexon–Δintron

We took the average of all samples within each dataset as the reference sample s ref, and then for each gene in each tissue/sample s, we calculated gene-level Δexon and Δintron values by comparing to s ref using DESeq. Specifically, for each of the exonic and intronic read sets separately, we used variance-stabilized transformation from DESeq57 to estimate the logarithm of read abundances for each gene, and then subtracted the average value across the samples to obtain Δexon and Δintron.

To filter against genes with low read counts that lead to noisy estimates of Δexon and Δintron, we determined a minimum read cutoff θ that nearly maximizes the correlation between Δexon and Δintron. Specifically, we determined the value of read count cutoff that maximizes the correlation (r max) between Δexon and Δintron, and then selected the minimum θ that results in r ≥ 0.99 × r max for genes with both exonic and intronic read counts ≥ θ.

To estimate the logarithm of ratio of k d,ref and k d (corresponding to decay rates in the reference sample s ref and sample s, respectively) for this selected subset of genes, we estimated the bias term shown in Fig. 1b for each gene separately. This bias term depends on the RNA processing Michaelis constant K p, and the concentration of pre-mRNA in query sample s and reference sample s ref, shown below as [pre-mRNA] and [pre-mRNA]ref, respectively:

$f bias Δ i = log 2 K p + pre-mRNA ref K p + pre-mRNA = log 2 K p pre-mRNA ref + 1 K p pre-mRNA ref + pre-mRNA pre-mRNA ref = log 2 c + 1 c + 2 Δ i$

Here, f bias is the gene-specific bias function, and Δi represents Δintron, i.e., the logarithm of change in abundance of intronic fragments relative to the reference sample. The parameter c is an unknown gene-specific constant that depends on the ratio of K p and [pre-mRNA]ref. As shown in Fig. 1c, f bias can be approximated as a linear function of Δi over a wide range of values of the constant c. Therefore, we estimated the function f bias for each gene by least-square linear regression of Δexon–Δintron vs. Δintron across all tissues/samples in each dataset, assuming that the change in mRNA decay constant across samples is independent of the bias term, and therefore the partial regression coefficient in the model Δe–Δi = Δlog(k d) + f biasi) is equal to the regression coefficient in the model Δe–Δi ~ f biasi). The value of the bias function in each sample is then subtracted from Δexon–Δintron to obtain an unbiased estimate of log2(k d,ref/k d), which is equal to differential stability. These steps are implemented in a software package available at https://github.com/csglab/REMBRANDTS. The mRNA stability estimates presented in this paper are available at http://csg.lab.mcgill.ca/sup/pan_stability/.

### Gene selection for qRT-PCR validation of stability estimates

We used RNA-seq data from ref. 15 to measure uncorrected and bias-corrected Δexon–Δintron, and then identified genes that were inferred to be up-regulated or downregulated in MDA-parental or MDA-LM2 cells based on each measure (two-tailed Student’s t-test P < 0.05). We then excluded all genes with previously reported differential stability scores > 0.5 or < –0.5 between MDA-parental and MDA-LM2 cells17, and among the remaining genes, identified those that had conflicting predictions from uncorrected and bias-corrected Δexon–Δintron. We sorted these genes based on their bias slopes, and selected the three most highly biased genes that were predicted by uncorrected Δexon–Δintron to be stabilized in MDA-LM2, as well as the three most highly biased genes that were predicted based on uncorrected Δexon–Δintron to be destabilized in MDA-LM2 relative to MDA-parental—all six genes had the reverse prediction based on bias-corrected Δexon–Δintron. We then measured the stability of these genes by α-amanitin inhibition of transcription followed by qRT-PCR, as described below.

### Sample preparation for qRT-PCR

Poorly metastatic MDA-parental (MDA-MB-231) and highly metastatic MDA-LM2 cells16 were obtained from Tavazoie lab (The Rockefeller University), and were tested to ensure absence of mycoplasma contamination. Cells were seeded at 2 × 105 per well in six-well plates (in biological triplicate). The following day, RNA was extracted from three MDA-parental and three MDA-LM2 wells for the 0-h time point. The remaining wells were treated with 10 μg/mL α-amanitin (Sigma) for 9 h before RNA extraction.

### Validation of mRNA stability estimates by qRT-PCR

RNA was isolated from MDA-parental and MDA-LM2 cells using a total RNA isolation kit with on-column DNase treatment (Norgen). Upon first-strand cDNA synthesis (SSIII, Life Technologies), relative levels of each mRNA of interest were assessed by qRT-PCR (ABI 7300 Real-Time System), using 18S as the endogenous control. Primer sequences are listed in Supplementary Table 1. For each cell-line, relative stability for each gene was defined as the 9-hr/0-hr ratio. Statistical significance was determined using Mann–Whitney U-test on relative stabilities.

### Analysis of RBP and miRNA binding sites

We limited the analysis of RBP and miRNA binding sites to genes for which all isoforms had the same 3′ UTR coordinates, and the 3’ UTR was composed of a single exon, in order to minimize the possibility of confounding effects of alternative splicing. The 3′ UTR coordinates were extracted from GENCODE v1951.

For analysis of RBP binding sites, we first collected a non-redundant compendium of available sequence preferences for human RBPs. We obtained 175 position frequency matrices (PFMs) representing 99 human RBPs from CisBP-RNA19, including PFMs with direct experimental evidence and those inferred by homology. Then, we calculated all pairwise PFM similarities using MoSBAT58, and then used affinity propagation59 to cluster the PFMs based on similarity, keeping only the “exemplar” from each cluster. This reduced the total number of PFMs to 126, which we call the non-redundant RBP motif set. Then, we scanned the 3′ UTR sequences with each PFM using AffiMx from the MoSBAT package58, resulting in a vector of PFM scores that represents the affinity of the corresponding RBP for binding to different 3′ UTRs.

For analysis of miRNAs, we obtained the seed sequences of 153 conserved human miRNA families from TargetScan20, and searched for exact matches to each seed sequence within 3’ UTRs. For each seed sequence, the number of matches within each 3′ UTR was recorded, resulting in a vector of miRNA seed match counts across the 3′ UTRs.

To identify RBPs and miRNAs that are associated with brain-specific stability profiles, we used the unbiased gene-level stability measures as the response variable in multiple linear regression, with RBP affinities and miRNA seed match counts as predictor variables. We also included 3′ UTR length, nucleotide frequencies, and dinucleotide frequencies as additional predictor variables in regression, in order to control for the confounding effect of these variables. RBPs and miRNAs whose binding sites were significantly associated with brain-specific stability were identified based on t-test of regression coefficients at FDR < 0.01.

### High-confidence stability network of brain

In order to identify high-confidence stability targets of each of the two RBPs and four miRNAs in the brain, we searched for genes that had a 3′ UTR binding site for that factor and for which that factor was necessary to explain the differential stability, even after taking into account all other factors. Specifically, for each factor f, we first repeated the multiple linear regression of the previous section after excluding f from the set of predictor variables, and took the residual of regression as the differential stability that remains unexplained after considering all factors except f. Then, we sorted the genes by the ascending order of this residual value, and at each residual value x, we calculated the distance (D) of the cumulative distribution functions for genes that had a binding site for f (the gene set F) and genes that did not have a binding site for f:

$D x = P X ≤ x g ∈ F - P X ≤ x g ∉ F$

The x that maximized D was taken as the residual cutoff that marks stability targets of f. Therefore, genes for which the residual value was ≥ x (for RBPs) or ≤ x (for miRNAs) and had a binding site for f were taken as high-confidence stability targets of f. Note that the maximum value of D is equal to the two-sample Kolmogorov–Smirnov statistic for the distributions of the residual values between genes that have a binding site for f and genes that do not have a binding site for f. We note that this procedure is similar to the “leading-edge” analysis described previously for GSEA60, except that GSEA uses a weighted Kolmogorov–Smirnov statistic instead of the unweighted statistic that we used. The high-confidence stability network of brain is available at http://csg.lab.mcgill.ca/sup/pan_stability/.

For evaluation of the high-confidence stability targets of RBFOX proteins, we obtained potential RBFOX1/2/3 targets by identifying human orthologs of mouse genes that have at least one Rbfox1/2/3 binding site in their 3′ UTRs based on HITS-CLIP33. We used mouse genes that had at least one reported Rbfox1/2/3 peak with height ≥ 30, and identified their human orthologs using Ensembl54, taking only genes with one-to-one orthology. As the background, we used human genes whose mouse orthologs had at least one HITS-CLIP tag, in order to remove non-expressed genes.

For validation of predicted ZFP36 stability targets, we obtained PAR-CLIP data as well as RNA-seq measurements for ectopic expression of ZFP36 in HEK293T cells from ref. 34. Functional ZFP36 targets were defined as genes with at least one PAR-CLIP cluster in their 3’ UTRs that also showed ≥ 2-fold downregulation in ZFP36-expressing cells compared to control.

For validation of miRNA targets, we obtained experimentally validated targets of hsa-miR-124-3p, hsa-miR-128-3p, hsa-miR-29(a/b/c)-3p, and hsa-miR-9-5p from miRTarBase35 release 6.1, which is a database of miRNA-target interactions collected from literature. For each miRNA, we removed target genes with ambiguous miRNA-target interactions (classified as both functional and nonfunctional in miRTarBase). Fisher’s exact test was used to determine the significance of enrichment of functional miRTarBase miRNA-target interactions in our high confidence network, relative to miRNA seed sequence matches.

We also obtained the list of genes that are downregulated after ectopic expression of miR-124 in HeLa cells from ref. 26. We used the set of genes with at least one probe on Rosetta 25k array as the background, in order to control for unobserved gene expression values, and calculated the enrichment of high-confidence stability targets of miR-124 among downregulated genes using Fisher’s exact test.

### Code availability

The scripts used for the analyses are available from https://github.com/csglab/REMBRANDTS.

### Data availability

The RNA-seq and microarray data referenced in this study are available from GEO (https://www.ncbi.nlm.nih.gov/geo/) and SRA (https://www.ncbi.nlm.nih.gov/sra), using accession numbers GSE5369737, GSE4516215, GSE29278, and SRP05696910. Processed data are available from http://csg.lab.mcgill.ca/sup/pan_stability/.

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## References

1. 1.

Wang, Y. et al. Precision and functional specificity in mRNA decay. Proc. Natl Acad. Sci. USA 99, 5860–5865 (2002).

2. 2.

Yang, E. et al. Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Genome Res. 13, 1863–1872 (2003).

3. 3.

Goodarzi, H. et al. Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature 485, 264–268 (2012).

4. 4.

Munchel, S. E., Shultzaberger, R. K., Takizawa, N. & Weis, K. Dynamic profiling of mRNA turnover reveals gene-specific and system-wide regulation of mRNA decay. Mol. Biol. Cell 22, 2787–2795 (2011).

5. 5.

Gaidatzis, D., Burger, L. & Stadler, M. B. Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation. Nat. Biotechnol. 33, 722–729 (2015).

6. 6.

Zeisel, A. et al. Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli. Mol. Syst. Biol. 7, 529 (2011).

7. 7.

Ameur, A. et al. Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat. Struct. Mol. Biol. 18, 1435–1440 (2011).

8. 8.

Gosline, S. J. et al. Elucidating MicroRNA regulatory networks using transcriptional, post-transcriptional, and histone modification measurements. Cell Rep. 14, 310–319 (2016).

9. 9.

Munding, E. M., Shiue, L., Katzman, S., Donohue, J. P. & Ares, M. Jr. Competition between pre-mRNAs for the splicing machinery drives global regulation of splicing. Mol. Cell 51, 338–348 (2013).

10. 10.

Duff, M. O. et al. Genome-wide identification of zero nucleotide recursive splicing in Drosophila. Nature 521, 376–379 (2015).

11. 11.

Encode Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

12. 12.

Koike, N. et al. Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science 338, 349–354 (2012).

13. 13.

Menet, J. S., Rodriguez, J., Abruzzi, K. C. & Rosbash, M. Nascent-Seq reveals novel features of mouse circadian transcriptional regulation. Elife 1, e00011 (2012).

14. 14.

Vollmers, C. et al. Circadian oscillations of protein-coding and regulatory RNAs in a highly dynamic mammalian liver epigenome. Cell Metab. 16, 833–845 (2012).

15. 15.

Furlow, P. W. et al. Mechanosensitive pannexin-1 channels mediate microvascular metastatic cell survival. Nat. Cell Biol. 17, 943–952 (2015).

16. 16.

Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

17. 17.

Goodarzi, H. et al. Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature 513, 256–260 (2014).

18. 18.

Tani, H. et al. Genome-wide determination of RNA stability reveals hundreds of short-lived noncoding transcripts in mammals. Genome Res. 22, 947–956 (2012).

19. 19.

Ray, D. et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature. 499, 172–177 (2013).

20. 20.

Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005 (2015).

21. 21.

Liang, Y., Ridzon, D., Wong, L. & Chen, C. Characterization of microRNA expression profiles in normal human tissues. BMC Genomics 8, 166 (2007).

22. 22.

Cheng, L. C., Pastrana, E., Tavazoie, M. & Doetsch, F. miR-124 regulates adult neurogenesis in the subventricular zone stem cell niche. Nat. Neurosci. 12, 399–408 (2009).

23. 23.

Zhao, C., Sun, G., Li, S. & Shi, Y. A feedback regulatory loop involving microRNA-9 and nuclear receptor TLX in neural stem cell fate determination. Nat. Struct. Mol. Biol. 16, 365–371 (2009).

24. 24.

Papagiannakopoulos, T. et al. Pro-neural miR-128 is a glioma tumor suppressor that targets mitogenic kinases. Oncogene. 31, 1884–1895 (2012).

25. 25.

Evangelisti, C. et al. MiR-128 up-regulation inhibits Reelin and DCX expression and reduces neuroblastoma cell motility and invasiveness. FASEB J. 23, 4276–4287 (2009).

26. 26.

Lim, L. P. et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433, 769–773 (2005).

27. 27.

Delaloy, C. et al. MicroRNA-9 coordinates proliferation and migration of human embryonic stem cell-derived neural progenitors. Cell. Stem Cell. 6, 323–335 (2010).

28. 28.

Fabbri, M. et al. MicroRNA-29 family reverts aberrant methylation in lung cancer by targeting DNA methyltransferases 3A and 3B. Proc. Natl Acad. Sci. USA 104, 15805–15810 (2007).

29. 29.

Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell. 27, 91–105 (2007).

30. 30.

Wuchty, S. et al. Prediction of associations between microRNAs and gene expression in glioma biology. PLoS ONE 6, e14681 (2011).

31. 31.

Lee, J. A. et al. Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes. Neuron 89, 113–128 (2016).

32. 32.

Carballo, E., Lai, W. S. & Blackshear, P. J. Feedback inhibition of macrophage tumor necrosis factor-alpha production by tristetraprolin. Science 281, 1001–1005 (1998).

33. 33.

Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep. 6, 1139–1152 (2014).

34. 34.

Mukherjee, N. et al. Global target mRNA specification and regulation by the RNA-binding protein ZFP36. Genome Biol. 15, R12 (2014).

35. 35.

Chou, C. H. et al. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res. 44, D239–D247 (2016).

36. 36.

Selkoe, D. J. Alzheimer’s disease is a synaptic failure. Science 298, 789–791 (2002).

37. 37.

Scheckel, C. et al. Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain. Elife 5, e10421 (2016).

38. 38.

Magistri, M., Velmeshev, D., Makhmutova, M. & Faghihi, M. A. Transcriptomics profiling of Alzheimer’s disease reveal neurovascular defects, altered amyloid-beta homeostasis, and deregulated expression of long noncoding RNAs. J. Alzheimers. Dis. 48, 647–665 (2015).

39. 39.

Narayanan, M. et al. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol. Syst. Biol. 10, 743 (2014).

40. 40.

Fogel, B. L. et al. RBFOX1 regulates both splicing and transcriptional networks in human neuronal development. Hum. Mol. Genet. 21, 4171–4186 (2012).

41. 41.

Lambert, N. et al. RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol. Cell. 54, 887–900 (2014).

42. 42.

Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015).

43. 43.

Jin, Y. et al. A vertebrate RNA-binding protein Fox-1 regulates tissue-specific splicing via the pentanucleotide GCAUG. EMBO J. 22, 905–912 (2003).

44. 44.

Hooli, B. V. et al. Rare autosomal copy number variations in early-onset familial Alzheimer’s disease. Mol. Psychiatry 19, 676–681 (2014).

45. 45.

Shankar, G. M. & Walsh, D. M. Alzheimer’s disease: synaptic dysfunction and Abeta. Mol. Neurodegener. 4, 48 (2009).

46. 46.

Alam, S., Suzuki, H. & Tsukahara, T. Alternative splicing regulation of APP exon 7 by RBFox proteins. Neurochem. Int. 78, 7–17 (2014).

47. 47.

Vina, J. & Lloret, A. Why women have more Alzheimer’s disease than men: gender and mitochondrial toxicity of amyloid-beta peptide. J. Alzheimers. Dis. 20(Suppl 2): S527–S533 (2010).

48. 48.

Huang, Y. & Mucke, L. Alzheimer mechanisms and therapeutic strategies. Cell 148, 1204–1222 (2012).

49. 49.

Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

50. 50.

Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

51. 51.

Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

52. 52.

Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

53. 53.

Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

54. 54.

Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016).

55. 55.

Zhu, J., He, F., Song, S., Wang, J. & Yu, J. How many human genes can be defined as housekeeping with current expression data? BMC Genomics 9, 172 (2008).

56. 56.

Ramskold, D., Wang, E. T., Burge, C. B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

57. 57.

Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome. Biol. 11, R106 (2010).

58. 58.

Lambert, S. A., Albu, M., Hughes, T. R. & Najafabadi, H. S. Motif comparison based on similarity of binding affinity profiles. Bioinformatics 32, 3504–3506 (2016).

59. 59.

Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

60. 60.

Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

## Acknowledgements

We thank Naghmeh Nikpoor (McGill University) for contribution to the processing of RNA-seq data. This work was supported by funds from McGill University Faculty of Medicine to H.S.N., and National Cancer Institute (NCI) grant CA194077 to H.G.

## Author information

### Author notes

• Rached Alkallas

These authors contributed equally to this work.

1. Rached Alkallas and Hamed S. Najafabadi contributed equally to this work.

### Affiliations

1. #### Department of Human Genetics, McGill University, Montreal, QC, Canada, H3A 0C7

• Rached Alkallas
2. #### McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada, H3A 0G1

• Rached Alkallas
3. #### Department of Biochemistry and Biophysics, University of California, San Francisco, CA, 94158, USA

• Lisa Fish
•  & Hani Goodarzi
4. #### Department of Urology, University of California, San Francisco, CA, 94158, USA

• Lisa Fish
•  & Hani Goodarzi
5. #### Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, 94158, USA

• Lisa Fish
•  & Hani Goodarzi

### Contributions

H.S.N. conceived and designed the study, and developed the computational methods. R.A. and H.S.N. analyzed and interpreted the data. H.G. designed the experiments, and L.F. and H.G. performed the experiments. H.S.N. wrote the manuscript with contribution from R.A.

### Competing interests

The authors declare no competing financial interests.