Global target mRNA specification and regulation by the RNA-binding protein ZFP36
© Mukherjee et al.; licensee BioMed Central Ltd. 2014
Received: 8 January 2013
Accepted: 6 August 2013
Published: 8 January 2014
ZFP36, also known as tristetraprolin or TTP, and ELAVL1, also known as HuR, are two disease-relevant RNA-binding proteins (RBPs) that both interact with AU-rich sequences but have antagonistic roles. While ELAVL1 binding has been profiled in several studies, the precise in vivo binding specificity of ZFP36 has not been investigated on a global scale. We determined ZFP36 binding preferences using cross-linking and immunoprecipitation in human embryonic kidney cells, and examined the combinatorial regulation of AU-rich elements by ZFP36 and ELAVL1.
Targets bound and negatively regulated by ZFP36 include transcripts encoding proteins necessary for immune function and cancer, and transcripts encoding other RBPs. Using partial correlation analysis, we were able to quantify the association between ZFP36 binding sites and differential target RNA abundance upon ZFP36 overexpression independent of effects from confounding features. Genes with increased mRNA half-lives in ZFP36 knockout versus wild-type mouse cells were significantly enriched for our human ZFP36 targets. We identified thousands of overlapping ZFP36 and ELAVL1 binding sites, in 1,313 genes, and found that ZFP36 degrades transcripts through specific AU-rich sequences, representing a subset of the U-rich sequences ELAVL1 interacts with to stabilize transcripts.
ZFP36-RNA target specificities in vivo are quantitatively similar to previously reported in vitro binding affinities. ZFP36 and ELAVL1 bind an overlapping spectrum of RNA sequences, yet with differential relative preferences that dictate combinatorial regulatory potential. Our findings and methodology delineate an approach to unravel in vivo combinatorial regulation by RNA-binding proteins.
Regulation of gene expression is a complex process coordinated at many steps. Post-transcriptional regulation is controlled through RNA-binding proteins (RBPs) and non-coding RNAs (ncRNAs) interacting with RNA regulatory elements (RREs). Dynamic and combinatorial interactions of RBPs and ncRNAs with these RREs determine the functional outcome of specific steps of RNA processing, such as splicing, polyadenylation, export, stability and translation . Additionally, interactions between RBPs and RREs govern messenger RNA (mRNA) stability and translational efficiency.
AU-rich elements (AREs) are conserved cis-regulatory elements, originally discovered in the 3′ untranslated region (UTR) of labile mRNAs, encoding cytokines and early expressed immune response genes [2, 3]. The most commonly defined AREs are the pentamer AUUUA, which is often found tandemly arranged, and the nonamer UUAUUUAUU, which is a pentamer flanked by uridylates [4–6]. AREs are generally considered instability elements, since they typically interact with RBPs that subsequently recruit mRNA degradation machinery [7, 8]. Examples of ARE-binding proteins include the ELAV family, ZPF36 family, HNRNPD/AUF family, TIA1, TIAR and KSRP. Mice lacking the AU-rich region containing multiple AREs residing in the 3′ UTR of the tumor necrosis factor α (TNFα) transcript develop inflammatory disease, demonstrating their primacy in gene regulatory mechanisms .
Tristetraprolin (ZFP36) is the founding member of the TIS11 family of RBPs. It interacts with AREs through tandem CCCH zinc fingers (TZFs) . Mice deficient in ZFP36 also exhibit inflammatory disease phenotypes, mostly explained by deregulation of TNFα mRNA stability and biosynthesis . The interaction of ZFP36 with AREs in the 3′ UTR of targeted mRNAs, such as TNFα, promotes mRNA degradation . ZFP36 is a nucleo-cytoplasmic shuttling protein that is predominantly cytoplasmic at steady state . The RNA-binding specificity of ZFP36 resides in the TZF domain, which interact with low nanomolar affinity with synthetic RNA substrates containing the nonamer UUAUUUAUU . The NMR structure of the nonamer sequence bound to the TZF domain from human ZFP36L2 revealed that the zinc fingers symmetrically bind to UAUU half-sites, with the 5′ uridylate unbound . There have been many detailed in vitro investigations of ZFP36–mRNA interaction (reviewed in ); however, in vivo studies have only determined the mRNA pools associated with ZFP36 and have not defined individual binding sites at high resolution [17, 18].
Although many ARE-binding RBPs recruit mRNA degradation complexes, the ELAV/Hu family of proteins binds to AREs and promotes mRNA stability and translation [2, 3, 19–21]. Both in vitro and in vivo studies have determined that Hu proteins interact with AREs, as well as U and CU-rich sequences [4–6, 22, 23]. These sequence preferences were substantiated in vivo using photoactivatable ribonucleoside cross-linking and immunoprecipitation (PAR-CLIP) to define high-resolution interaction sites for the ubiquitously expressed family member ELAVL1 [7, 8, 24–26]. Due to T-to-C transitions indicative of RBP–RNA interaction sites [10, 27], PAR-CLIP gives a higher resolution of binding sites than earlier methods [11, 28, 29]. A commonly postulated mechanism through which ELAVL1 promotes mRNA stability is competition with ARE-binding RBPs that promote degradation. Like ZFP36, ELAVL1 is a nucleo-cytoplasmic shuttling protein, but under normal conditions is predominantly nuclear localized [12, 30], consistent with numerous intronic binding sites found in PAR-CLIP experiments [13, 24–26]. Simultaneous temporal and spatial competition for binding sites by ELAVL1 and ZFP36 is important when responding to stimuli, particularly immune activation, upon which ELAVL1 redistributes from the nucleus to the cytoplasm [14, 31] and ZFP36 is induced and cytoplasmically localized. There is evidence for competitive binding and displacement of ZFP36 with increasing amounts of ELAVL1 and/or MAPK signaling influencing the combinatorial regulation of the TNFα mRNA by ELAVL1 and ZFP36 [15, 32]. However, the extent to which similar competitive mechanisms exist transcriptome-wide is unknown.
Conflated direct and indirect relations are a concern, particularly when integrating multiple genomic measurements to understand complex gene regulatory mechanisms. An example of a relation relevant for post-transcriptional regulation is that longer transcripts contain more binding sites for trans-acting factors. Consequently, significant correlations between 3′ UTR length and miRNA-dependent downregulation have been found in numerous datasets [16, 33]. Overexpression of ZFP36 in the absence of stress has been demonstrated to promote stress granule assembly , potentially causing non-ZFP36 specific effects in transcript abundance. Partial correlation analysis quantifies the direct dependence between two variables (for example, number of binding sites or expression changes) when accounting for other variables (for example, UTR length and mRNA abundance). It has recently been applied to investigate genome evolution [34, 35], as well as chromatin state and transcriptional regulation . Here we apply it to account for generic transcript features when inferring functional relations between the number of binding sites and gene expression changes due to perturbing the expression of the RBP.
There are numerous examples of combinatorial binding of RBPs resulting in specific splicing decisions [37–40], but this has not been as extensively studied for mRNA stability. Given the numerous ARE-binding RBPs, the principles underlying the integration of RBP binding and concomitant regulatory outcomes have remained elusive. We therefore determined the in vivo mRNA-binding sites of ZFP36. The interaction sites and sequence preferences were compared to similarly acquired in vivo HuR interaction data, specifically contrasting AU-rich sequences with predominantly U- and CU-rich sequences. Our results indicate active competition between ZFP36 and ELAVL1 for commonly used AU-rich sequences and define the characteristics of these combinatorial regulatory events.
Results and discussion
ZFP36 preferentially binds to 3′ UTRs of mRNAs encoding regulators of gene expression
The majority of ZFP36 mRNA binding sites (70%, 2,314) mapped to the 3′ UTR, and very few binding sites were located at the 5′ UTR or the protein coding region (Figure 1C). Approximately one-quarter of the mRNA binding sites were intronic (871), which is conceivable since ZFP36 is a known nucleo-cytoplasmic shuttling RBP . However, the 3′ UTR sites had a substantially higher T-to-C conversion fraction and specificity relative to ZFP36 sites from other annotation categories including introns, 5′ UTRs and protein coding regions (Additional file 3: Figure S1B). We observed closely spaced ZFP36 sites within 3′ UTRs (Figure 1D), indicative of multimerization of ZFP36 and supported by results from 2-hybrid analysis and immunoreactive bands migrating at twice the expected number of kilodaltons of ZFP36 in cell extracts (PJB, unpublished data). Of the 1,148 transcripts with more than one ZFP36 site in the 3′ UTR, 228 had two sites within 70 nucleotides, the range for which proximity of ZFP36 sites is above background. The statistically significant enrichment for ZFP36 sites in the last 100 nucleotides of the 3′ UTR (Figure 1E) was intriguing given its ability to promote deadenylation . This spatial bias was not observed for the sites of all other investigated 3′ UTR binding RBPs (Additional file 3: Figure S1D) and thus not due to biases in library generation, though it may be related to AU-richness proximal to polyadenylation sites . Of the 2,143 genes with ZFP36 sites, 64% exclusively had 3′ UTR sites, 23% exclusively had intronic sites and only 8% contained both 3′ UTR and intronic sites (Figure 1F). Genes with 3′ UTR sites were less likely to also have intronic sites in contrast to ELAVL1 . The strong preference for 3′ UTR binding was anticipated for an RBP predominantly involved in cytoplasmic regulation of mRNA stability and translation.
In vivoZFP36 binding specificity
Previously reported in vitro ZFP36 binding studies quantified the binding of numerous RNA probes to the 73 amino acid portion of ZFP36 containing the TZF using fluorescence anisotropy measurements . For each probe, we compared the experimentally determined association, K, and disassociation constant, K d , with the SNR values calculated from our ZFP36 PAR-CLIP data matching experimental RNA probe and in vivo binding site sequences. We observed a very strong correlation with K d values (Figure 3C). This strong concordance allowed us to calculate predicted K d values from the PAR-CLIP derived SNR ratios and thus perform in silico experiments with specific sequences. The relation between SNR and K d was non-linear. For example, larger differences between the SNR values for the nonamer (UUAUUUAUU) and the octamer (UUAUUUAU) translated to smaller differences in the predicted K d values for high affinity targets (Figure 3D). When inserting a spacer in the octamer between the UUAU half-sites bound by the TZF domain, a single nucleotide disruption by any nucleotide resulted in similar increases in predicted K d values. However, multiple continuous disruptions with non-uridylates exhibit quite large increases in predicted K d values, whereas disruptions with uridylates had little to no change (Figure 3D). These results highlight the quality and complementary value of the PAR-CLIP method, as well as the detailed in vitro biochemical analysis of ZFP36 substrate specificity.
Specificity of ZFP36 function
To evaluate the regulatory function of ZFP36, we used microarray analysis to compare transcript abundance before and after doxycycline-induction of EGFP-ZFP36 fusion protein expression in HEK293 cells otherwise expressing little endogenous ZFP36 (Additional file 4: Figure S2A,B and Additional file 5: Table S2). We detected 2,784 genes with statistically significant expression differences by comparing overnight doxycycline-induced cells to mock-induced cells. Doxycycline treatment of the parental cell line did not yield any statistically significant differences defined by Bonferroni corrected P < 0.01 (Additional file 4: Figure S2C). Only 45% of the differentially expressed genes decreased as a result of ZFP36 overexpression (downregulated genes). The large number of upregulated genes was likely due to secondary effects from prolonged ZFP36 overexpression (Additional file 4: Figure S2D). (Detailed characterization of genes with ZFP36 and ELAVL1 sites with numerous other motif contributions for significantly upregulated and downregulated genes are shown in Additional file 4: Figure S2H.) The 1,254 significantly downregulated genes decreased by approximately 3.5 fold on average. The breadth and degree of expression differences resulting from EGFP-ZFP36 overexpression were distinctly greater than the typical fine-tuning associated with most RBPs and miRNAs. This is particularly striking in comparison to ELAVL1 where only 15 genes exhibited greater than a twofold change due to ELAVL1 knockdown (Additional file 4: Figure S2G from ).
Downregulated genes were significantly enriched for AU-rich sequences in an unbiased analysis of the contribution of 3′ UTR motif elements to expression changes (Additional file 4: Figure S2E) . Thus, we examined the association between the number of counts per 3′ UTR of specific AREs and EGFP-ZFP36-induced changes in mRNA abundance. The RREs described by the ZFP36L2:ARE NMR structure, the UAUUUAUU octamer and the UAUU half-site, had the strongest association with ZFP36-induced changes in mRNA abundance. The UAUUUAUU octamer had a stronger association with ZFP36-induced changes in mRNA abundance than the UUAUUUAUU nonamer (Additional file 4: Figure S2F). The UAUU half-site had a stronger association with ZFP36-induced changes in mRNA abundance than the AUUUA pentamer (Additional file 4: Figure S2F).
To illustrate functional effects, genes are frequently stratified by a variable of interest (such as the presence or absence or number of binding sites), followed by a statistical test for significant phenotypic differences (such as expression changes) between the subsets. For instance, we observed strong associations between the number of ZFP36 sites in the 3′ UTR of a gene and its downregulation due to EGFP-ZFP36 overexpression (Figure 4B), between 3′ UTR length and EGFP-ZFP36-induced changes in mRNA abundance (Figure 4C), and between ZFP36 sites and 3′ UTR length. These effects were corroborated by the strength of Spearman correlations between all three variables (Figure 4D, left). However, pairwise correlations are not able to quantify direct and indirect relations for more than two variables and thus we employed partial correlation analysis. Comparing the correlation and partial correlation of ZFP36 sites and 3′ UTR length with EGFP-ZFP36-induced changes in mRNA abundance, most of the relation between ZFP36 sites and EGFP-ZFP36-induced changes in mRNA abundance was independent of 3′ UTR length and vice versa (Figure 4D). Secondary effects from prolonged ZFP36 overexpression, such as spontaneous stress granule assembly  and changes in the expression levels of other RBPs, were potential explanations for the independent contribution of 3′ UTR length to EGFP-ZFP36-induced changes in mRNA abundance.
We determined which transcript regions contained the most independent information for explaining EGFP-ZFP36-induced changes in mRNA abundance, without having to lose statistical power by separating transcripts into mutually exclusive categories, such as binding within introns only or 3′ UTRs only. EGFP-ZFP36-induced changes in mRNA abundance correlated negatively with the number of ZFP36 3′ UTR sites and positively with the number of intronic ZFP36 sites (Figure 4E, left). Partial correlation analysis revealed that ZFP36 sites outside of 3′UTRs, including introns, did not contribute any additional independent explanation of EGFP-ZFP36-induced changes in mRNA abundance (Figure 4E, right). Here, the strong negative association or avoidance between the number of intronic and 3′ UTR sites per gene (like the results in Figure 1F) acts as a mediator in explaining the spurious effects between intronic ZFP36 sites and EGFP-ZFP36-induced changes in mRNA abundance. This result does not rule out that ZFP36 sites from other transcript regions have other functional effects, but we only utilized 3′ UTR binding sites for further analysis.
We compared our ZFP36 PAR-CLIP and overexpression data from human HEK293 cells with global mRNA stability changes after serum stimulation for ZFP36 wild-type (WT) or knockout (KO) mouse fibroblast cell lines . Genes with increased mRNA half-lives in the ZFP36 KO vs WT mouse cells were significantly enriched for genes with ZFP36 PAR-CLIP sites in 3′ UTR (P = 0.003) and genes significantly downregulated upon EGFP-ZFP36 overexpression (P = 0.001), but were not significantly enriched for genes significantly upregulated upon EGFP-ZFP36 overexpression (P = 0.1) (Additional file 6: Figure S3A). Interestingly, ELAVL1 RIP-chip targets from human Jurkat T-cell activation were significantly enriched in genes with increased mRNA half-lives in the ZFP36 KO vs WT mouse cells, even though ZFP36 RIP-chip targets from activated mouse macrophages were slightly but not significantly enriched . Genes with ELAVL1 sites in the 3′ UTRs were similarly enriched, but genes without ELAVL1 or ZFP36 sites in their 3′ UTR did not have any enrichment. These results provide independent validation of the ZFP36 bound and regulated genes in our study using a system not reliant on ectopic overexpression of ZFP36. The enrichment of ELAVL1 gene is further support for the potential of ELAVL1 to effect expression of ZFP36 targets.
Combinatorial regulation of mRNAs with AREs by ZFP36 and ELAVL1
To assess specific sequences within ELAVL1-bound sites and the strength of their contribution to the EGFP-ZFP36-induced changes in mRNA abundance, we performed the same analysis using ELAVL1 sites containing CUUUC, UUUUU or AUUUA. The AUUUA ELAVL1 sites (−0.21) accounted for more EGFP-ZFP36-induced changes in mRNA abundance than the CUUUC ELAVL1 sites (−0.07) (Figure 5C,D). This result was consistent with the observation that ELAVL1 specific sites were characterized by predominantly U- and CU-rich sequences, while overlapping ELAVL1 and ZFP36 sites were characterized more by AU-rich sequences (Figure 5E). The exact same analysis using an independently derived ELAVL1 HEK293 PAR-CLIP library yielded similar results (Additional file 7: Figure S4D-F), supporting the robustness of our findings. EGFP-ZFP36-induced changes in mRNA abundance were better explained by the ARE nonamer and octamer compared to U8 or U9. ELAVL1-knockdown-responsive mRNAs had the reciprocal pattern (Additional file 6: Figure S3B). Finally, transcripts containing 3′ UTR sites for both ELAVL1 and ZFP36 had a greater concomitant decrease upon ELAVL1 knockdown and decrease upon ZFP36 overexpression, relative to transcripts containing 3′ UTR sites for only ELAVL1 or ZFP36 or no sites (Figure 5F, shifted down and to the right). Taken together, there is substantial potential for ZFP36 and ELAVL1 to compete for the same pool of binding sites, while exerting a more targeted influence on a subset of sites with factor-specific sequence preferences.
Our global in vivo ZFP36–mRNA interaction experiments support previously reported biochemical in vitro ZFP36 affinity measurements, demonstrating the remarkable precision and quantitative characteristics of ZFP36 PAR-CLIP results. ZFP36 targets identified in this study were enriched for ZFP36-dependent changes in mRNA stability in mouse fibroblast cells. ZFP36 interacted with mRNAs encoding proteins important for the immune response and cancer. Many of these are other RBPs central to these two biological processes, specifically other ARE-binding RBPs such as the positive-stability regulator ELAVL1. There was extensive overlap between the interaction sites of ZFP36 and ELAVL1. Over 80% of ZFP36 sites in 3′ UTRs overlapped with ELAVL1 target sites. The preference for ZFP36 and ELAVL1 to bind and regulate mRNAs encoding RBPs suggests a complex interconnected network of post-transcriptional regulation that needs to be reconciled with respect to known ZFP36 and ELAVL1 protein expression, localization and modification dynamics, particularly in response to immune activation .
Partial correlation analysis accounting for confounding features demonstrated that a higher number of ZFP36 sites corresponded to stronger downregulation upon EGFP-ZFP36-induced changes in mRNA abundance. While the two proteins bind AREs, ZFP36 clearly shows a preferences towards AU- vs simply U-rich or CU-rich sequences relative to ELAVL1. This was corroborated at the level of mRNA stability, since ELAVL1 sites containing AUUUA were best able to explain EGFP-ZFP36-induced changes in mRNA abundance.
In summary, this study provides a resource for the precise transcriptome-wide RNA interactions of two ARE-binding RBPs with antagonistic functions. The precise sequence definition and function of AREs are an important question that will greatly benefit from global interaction data for these and other ARE-binding RBPs. Furthermore, we have integrated multiple genome-wide assays with computational analyses to advance our understanding of the sequence basis and extent of combinatorial gene regulation at the RNA level, in particular the ARE-mediated regulation by two of the most well-characterized RBPs.
Materials and methods
Doxycycline-inducible FLAG/HA-tagged ZFP36 HEK293 cell lines were produced utilizing the Flp-In™ T-REx™ 293 Cell Line system (Life Technologies, USA).
Flp-In™ T-REx™ HEK293 cells were generated with a FLAG/HA-tagged human ZFP36 or an EGFP-tagged human ZFP36. Both cell lines were cultured in DMEM supplemented with 10% tetracycline-reduced fetal bovine serum and appropriate selection antibiotics. For induction of FLAG/HA-tagged or EGFP-tagged ZFP36 protein, cells were treated for 16 hr with 1 μg/ml doxycycline (Sigma, USA).
The protocol was performed as detailed in  for a FLAG-HA-tagged ZFP36 and sequenced using an Illumina platform. The primary dataset comprised previously derived ELAVL1 PAR-CLIP and ELAVL1 knockdown data . This was integrated with ZFP36 PAR-CLIP data. An independent ELAVL1 PAR-CLIP dataset for HEK293 cells was also used to substantiate our results. Specifically, we used the union of all ELAVL1 T1 PAR-CLIP libraries. PUM2, QKI and FMR1 HEK293 PAR-CLIP libraries were investigated to compare spatial binding biases in 3′ UTRs [55, 56]. Precise analysis parameters for the PAR-CLIP libraries above are provided in Additional file 1. Binding site coordinates for ELAVL1 HEK293 CLIP and HeLa PAR-CLIP were downloaded from doRiNA .
Briefly, reads from the ZFP36 and ELAVL1 deep-sequencing library were stripped of the adaptor sequence using Cutadapt . Reads that were less than 20 nucleotides in length or contained an ambiguous nucleotide were discarded. The remaining reads were aligned to the human genome (hg19), with up to one mismatch allowed and ten alignment locations, with Bowtie version 0.12.7 . Reads with T-to-C mismatches were reconciled  to retain only those that mapped to a single genomic location for the minimum number of mismatches. Annotation was performed as described in . Full site- and gene-level data for the primary data compared are in Additional file 2: Table S1 and Additional file 5: Table S2, respectively.
The SNR values were calculated for each probe sequence in Table one in  appearing at least once in 3′ UTR ZFP36 binding sites. The SNR was calculated as in , and is defined as the number of instances per nucleotide in a given set of PARalyzer interaction sites, divided by the number of instances per nucleotide in the background set. In this case the background was the sequence of the longest annotated 3′ UTR for a gene. The SNR values were plotted against the K and K d values from . The formula for the power curve fit of the SNR to K d was used to predict K d values for other sequences.
Microarray samples and analysis
EGFP-ZFP36 cells were passaged at 1:10 dilution in 15 ml of media in ten T75 flasks. They were allowed to grow for three days to 60% confluence. They were then treated with doxycycline (Sigma D989) to a final concentration of 1 μg/ml. The same volume of sterile water was used as a control. The cells were incubated for 16 hr at 37°C, in 5% CO2. The flasks without doxycycline were about 80% confluent. Cells with doxycycline were about 40% confluent and there were more floating cells and rounded cells. The cells were harvested by trypsinizing. Cells from five flasks per treatment were pooled. They were centrifuged at 500 g for 5 min, and resuspended in growth medium to final concentrations of 1 × 107 cells/ml. Fluorescence-activated cell sorting (FACS) was done using a FACSAria II from Becton Dickinson (San Jose, CA). Cells were excited at 488 nm and sorted based on their GFP fluorescence at 525/550 nm.
The FacsDiva software version 6.1.3 was used for analysis. Doublet discrimination gates were set on forward scatter and side scatter to ensure the sorting of single cells. The sort gate was set on a histogram of GFP + cells for a fluorescence signal of greater than 103 log units. Control cells were sorted in the same way, except that fluorescent cells were excluded (Additional file 3: Figure S1B). The sorted cells were collected into DMEM with 10% FBS in 6 ml polypropylene tubes at approximately 2 × 106 cells per tube. They were pelleted at 500 g for 20 min at 4°C and washed in PBS, then transferred into 1.5 microfuge tubes at approximately 4 × 106 cells per tube. These were centrifuged at 500g for 20 to 30 min at 4°C. The supernatant was removed, and the pellets were snap frozen in liquid nitrogen and stored at −80°C. This experiment was repeated on five independent days, using cells from passages 17, 20, 23, 26 and 27. RNA was isolated from approximately 3 × 106 to 6 × 106 cells using the GE Illustra RNAspin mini kit. To control for doxycycline effects, parental HEK293 cells were passaged at 1:4 or 1:5 dilution in 15 ml medium in four T75 flasks. They grew for 2 days to approximately 60% confluence. They were then treated with doxycycline in the same way, that is, 1 μg/ml for 16 hr at 37°C in 5% CO2. Control cells were treated with the same volume of sterile water. The cells were harvested by trypsinizing, and processed and used for the microarray in the same way as for the GFP + cells. The same experiment was performed on five different days, and comparisons were made using five doxycycline treated vs five vehicle (water) treated samples, with each experiment done as pairs of treated and untreated cells. The samples were stored at −80°C. The integrity of the RNA was confirmed by running 3 μg of each RNA sample on 1.2% agarose/formaldehyde gels.
Then 1.5 μg of RNA from each sample was provided to the NIEHS Microarray Core Facility for analysis, using an Affymetrix Human Genome U133 Plus 2.0 gene chip (Affymetrix, Santa Clara, CA). As directed in the Affymetrix 3′ IVT Express kit protocol, 100 ng of total RNA was amplified. Then 12.5 μg of amplified biotin-RNA was fragmented and 10 μg were hybridized to each array for 16 hr at 45°C in a rotating hybridization oven using the Affymetrix Eukaryotic Target Hybridization Controls and protocol. Array slides were stained with streptavidin/phycoerythrin utilizing a double-antibody staining procedure and then washed for antibody amplification according to the GeneChip Hybridization, Wash and Stain Kit and user manual. Arrays were scanned in an Affymetrix Scanner 3000 and data were obtained using the GeneChip® Command Console Software (AGCC; Version 1.1). The data were normalized using gcrma  and then filtered to retain only unambiguously mapping probes and minimal expression level (mean log hybridization value for replicates within a group > 4.5). Bonferroni corrected P values for fold changes were calculated using Gene Pattern . Gene set enrichment analysis  was used to compare HEK293 data to ZFP36 mouse fibroblast mRNA stability data. Human–mouse mappings were done using an appropriate microarray chip. The probes were ranked by differential mRNA half-life score, with higher scores indicating a larger slope in ZFP36 KO than ZFP36 WT cells (for details refer to ).
Analysis of spatial patterns
To visualize binding along transcripts, cluster data was taken from PARalyzer output and annotated. Only 3′ UTR, 5′ UTR, coding and intron annotation categories were utilized. The transcript segment associated with the annotation category of each cluster was selected using a GENCODE version 18 gene transfer format file . For each gene, we used the transcript that had the longest segment matching the binding site of the cluster. The location and relevant distance along the transcript segment were then calculated. For each of the relevant annotation categories, a density plot was created utilizing each of these distances. Permutations of the observed data were used to create the background data distributions. For each cluster we created a random site along the length of its associated transcript segment repeated 100 times. The cluster plot function in the R LSD package , was used to plot the observed and permuted data. The LSD cluster plot function creates a graph in which the black center line is the median, and the thick gray lines separating the color gradients around the black line represent the upper and lower quartiles. The color gradient continues until the 10th and 90th percentiles.
Both ZFP36 PAR-CLIP sequencing and overexpression microarray datasets have been deposited into GEO [GEO:GSE53185].
Dulbecco’s modified Eagle’s medium
green fluorescent protein
nuclear magnetic resonance
photoactivatable ribonucleoside cross-linking and immunoprecipitation
RNA regulatory element
small interfering RNA
tumor necrosis factor
tandem CCCH zinc finger
TT and UO acknowledge support from an award by the US National Institutes of Health (R01-GM104962). NM is supported by EU Marie Curie IIF (EU). PJB acknowledges support from the Intramural Research Program of the NIH, NIEHS. We would like to acknowledge Jack Keene for very insightful early discussions and suggestions.
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