Emiliano P Ricci

Alper Kucukural

Can Cenik

Blandine C Mercier

Guramrit Singh

Erin E Heyer

Ami Ashar-Patel

Lingtao Peng

Melissa J Moore
email: melissa.moore@umassmed.edu

Staufen1 senses overall transcript secondary structure to regulate translation

Human Staufen1 (Stau1) is a double-stranded RNA (dsRNA)-binding protein implicated in multiple post-transcriptional gene-regulatory processes. Here we combined RNA immunoprecipitation in tandem (RIPiT) with RNase footprinting, formaldehyde cross-linking, sonication-mediated RNA fragmentation and deep sequencing to map Staufen1-binding sites transcriptome wide. We find that Stau1 binds complex secondary structures containing multiple short helices, many of which are formed by inverted Alu elements in annotated 3′ untranslated regions (UTRs) or in 'strongly distal' 3′ UTRs. Stau1 also interacts with actively translating ribosomes and with mRNA coding sequences (CDSs) and 3′ UTRs in proportion to their GC content and propensity to form internal secondary structure. On mRNAs with high CDS GC content, higher Stau1 levels lead to greater ribosome densities, thus suggesting a general role for Stau1 in modulating translation elongation through structured CDS regions. Our results also indicate that Stau1 regulates translation of transcription-regulatory proteins.

Staufen proteins are highly conserved dsRNA-binding proteins (dsRBPs) found in most bilateral animals 1 . Mammals contain two Staufen paralogs encoded by different loci. Stau1,

expressed in most tissues, has a microtubule-binding domain, a dimerization domain and four conserved dsRNA-binding domains (dsRBDs), only two of which (dsRBDs 3 and 4) are necessary for dsRNA binding [START_REF] Luo | Molecular mapping of the determinants involved in human Staufen-ribosome association[END_REF] . Within cells, Stau1 can make direct interactions both with itself and with Stau2, the more tissue-specific paralog [START_REF] Martel | Multimerization of Staufen1 in live cells[END_REF] . Functionally, Staufen proteins are involved in multiple post-transcriptional regulatory processes. In flies, 3′ UTR-bound Staufen is required for proper localization and translational control of bicoid and prospero mRNAs during oogenesis [START_REF] St Johnston | Staufen, a gene required to localize maternal RNAs in the Drosophila egg[END_REF][START_REF] Ferrandon | RNA-RNA interaction is required for the formation of specific bicoid mRNA 3′ UTR-STAUFEN ribonucleoprotein particles[END_REF] . In mammals, Stau1 has been implicated in mRNA transport to neuronal dendrites [START_REF] Köhrmann | Microtubule-dependent recruitment of Staufen-green fluorescent protein into large RNA-containing granules and subsequent dendritic transport in living hippocampal neurons[END_REF] , regulation of translation via physical interaction with the ribosome [START_REF] Dugré-Brisson | Interaction of Staufen1 with the 5′ end of mRNA facilitates translation of these RNAs[END_REF] , a form of translation-dependent mRNA degradation known as Staufen-mediated decay (SMD) [START_REF] Kim | Mammalian Staufen1 recruits Upf1 to specific mRNA 3′UTRs so as to elicit mRNA decay[END_REF][START_REF] Gong | lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements[END_REF][START_REF] Gleghorn | Staufen1 dimerizes through a conserved motif and a degenerate dsRNA-binding domain to promote mRNA decay[END_REF][START_REF] Park | Staufen2 functions in Staufen1-mediated mRNA decay by binding to itself and its paralog and promoting UPF1 helicase but not ATPase activity[END_REF] , regulation of stress-granule homeostasis [START_REF] Thomas | Mammalian Staufen 1 is recruited to stress granules and impairs their assembly[END_REF] , alternative splicing, nuclear export and translation of a gene containing 3′-UTR CUG-repeat expansions [START_REF] Ravel-Chapuis | The RNA-binding protein Staufen1 is increased in DM1 skeletal muscle and promotes alternative pre-mRNA splicing[END_REF] . Although Stau1 is not essential for mammalian development, neurons lacking Stau1 have dendritic spinemorphogenesis defects in vitro, and knockout mice have locomotor-activity deficits [START_REF] Vessey | A loss of function allele for murine Staufen1 leads to impairment of dendritic Staufen1-RNP delivery and dendritic spine morphogenesis[END_REF] .

Crucial for the understanding of how Stau1 regulates gene expression is comprehensive knowledge of its intracellular RNA-binding sites. Although mammalian Stau1-and Drosophila Staufen-associated mRNAs were identified by microarray analysis after native RNA immunoprecipitation (RIP) [START_REF] Furic | A genome-wide approach identifies distinct but overlapping subsets of cellular mRNAs associated with Staufen1-and Staufen2-containing ribonucleoprotein complexes[END_REF][START_REF] Laver | Genome-wide analysis of Staufen-associated mRNAs identifies secondary structures that confer target specificity[END_REF][START_REF] Maher-Laporte | Genome wide identification of Staufen2-bound mRNAs in embryonic rat brains[END_REF][START_REF] Kusek | Asymmetric segregation of the double-stranded RNA binding protein Staufen2 during mammalian neural stem cell divisions promotes lineage progression[END_REF] , those studies were unable to directly map any individual Stau1-binding site, and subsequent bioinformatics analysis yielded no clear consensus for identified mammalian targets [START_REF] Laver | Genome-wide analysis of Staufen-associated mRNAs identifies secondary structures that confer target specificity[END_REF] . Thus, with the exception of a few wellcharacterized binding sites validated by mutagenesis [START_REF] Ferrandon | Staufen protein associates with the 3′UTR of bicoid mRNA to form particles that move in a microtubule-dependent manner[END_REF][START_REF] Kim | Staufen1 regulates diverse classes of mammalian transcripts[END_REF] , the exact target sites and RNA structures recognized by mammalian Stau1 remain to be determined. To address this, we here undertook a tandem affinity purification strategy (RIPiT [START_REF] Singh | RIPiT-Seq: a high-throughput approach for footprinting RNA:protein complexes[END_REF] ) to map Stau1-binding sites transcriptome wide in human tissue-cultured cells. We also knocked down and overexpressed Stau1 to measure functional consequences on target-mRNA levels and translation efficiency. Our results revealed a new role for Stau1 in regulating translation of GC-rich mRNAs by 'sensing' overall transcript secondary structure.

Results

Transcriptome-wide mapping of Stau1-binding sites

Using the Flp-In system and a tetracycline promoter, we generated HEK293 cells that inducibly expressed a single Flag-tagged copy of either the Stau1 65-kDa spliced isoform (Stau1-WT) or a mutant version (Stau1-mut) containing point mutations in dsRBDs 3 and 4 known to disrupt binding to dsRNA [START_REF] Luo | Molecular mapping of the determinants involved in human Staufen-ribosome association[END_REF] (Fig. 1a). Consistently with its propensity to bind dsRNA through the sugar-phosphate backbone [START_REF] Ramos | RNA recognition by a Staufen double-stranded RNA-binding domain[END_REF] and with a previous report suggesting poor UV-cross-linking ability [START_REF] Liu | Detection of double-stranded RNA-protein interactions by methylene blue-mediated photo-crosslinking[END_REF] , we found that Stau1 cross-linked with very poor efficiency to poly(A) + RNA upon shortwave UV irradiation of living cells (Supplementary Fig. 1a). Therefore we used a RIPiT approach wherein initial immunoprecipitation (IP) with anti-Flag antibody was followed by affinity elution with Flag peptide and then a second IP with a polyclonal anti-Stau1 antibody. RIPiT was performed under two different regimens: (i) To finely-map stable Stau1 footprints, we extensively digested samples with RNase I in between native anti-Flag and native anti-Stau1 IPs, generating 30-to 50-nt Stau1-bound RNA fragments (FOOT libraries; Fig. 1b and Supplementary Fig. 1c). However, many of these short reads derived from Alu repeat elements (described below) and so were not uniquely mappable. Further, under native conditions, Stau1 can make new dsRNA associations after cell lysis (Supplementary Fig. 1b). (ii) Therefore, we also subjected cells to formaldehyde cross-linking before lysis, extensively sonicated the lysates to shear long RNAs into 200-to 300-nt fragments (thereby increasing their ability to be mapped) and performed a denaturing anti-Flag IP and then a native anti-Stau1 IP (CROSS libraries; Fig. 1b and Supplementary Fig. 1d). Cross-linking and subsequent denaturation should both preserve weak in situ interactions that might otherwise dissociate during sample workup and prevent formation of any new interactions after cell lysis.

We sequenced all libraries constructed by 3′-adaptor ligation to RNA fragments, reverse transcription and circularization on GAII or HiSeq 2000 Illumina platforms and then mapped them to HG18 by using RefSeq gene annotations. Biological replicates of WT and mut CROSS and WT FOOT libraries exhibited extremely high correlations (r >0.98), thus indicating the reproducibility of the approach (Supplementary Fig. 1e).

Stau1 associates with translating ribosomes

In contrast to our previous exon junction complex (EJC) RIPiT libraries [START_REF] Singh | The cellular EJC interactome reveals higher-order mRNP structure and an EJC-SR protein nexus[END_REF] , all Stau1 FOOT libraries (WT and mut) were dominated by rRNA-mapping reads (14-30% versus 74-83%, respectively; Supplementary Fig. 2). Further, despite attempts to specifically deplete rRNA fragments during CROSS-library preparation, WT and mut CROSS libraries also contained abundant rRNA-mapping reads (Supplementary Fig. 2). These findings are consistent with a previous report that Stau1 cosediments with 60S ribosomal subunits via interactions independent of the functionality of dsRBDs 3 and 4 (ref.

2).

To further investigate this ribosome association, we performed sucrose sedimentation in the presence of inhibitors that either block elongation (cycloheximide) or initiation (harringtonine) (Supplementary Fig. 3a). In the presence of cycloheximide, both endogenous Stau1 and Flag-Stau1-WT cosedimented with 60S sub-units, 80S monosomes and polysomes, with very little Stau1 observable in ribosome-free fractions at the top of the gradient. However, when lysates were treated with RNase before sedimentation, ∼60% of Stau1 sedimented at the top of the gradient, with the remainder cosedimenting with 60S and 80S ribosomes (Supplementary Fig. 3a). This suggests that dsRBP-independent interactions with the ribosome are not the sole factor driving Stau1 polysome association. Finally, when translation initiation was blocked with harringtonine and elongating ribosomes allowed to complete translation (i.e., run off the mRNAs) before cell lysis, Stau1 sedimentation mirrored that of RPL26, an integral 60S protein. Both Stau1 and RPL26 rapidly shifted from heavy polysomal to 80S ribosome fractions upon inhibition of translation initiation (Supplementary Fig. 3b), suggesting that Stau1 associates with actively translating ribosomes.

Consistently with our ribosome-association data, approximately half of mRNA-mapping WT and mut CROSS reads (49% and 54%, respectively) mapped to coding exons (CDS regions; Fig. 1c-e). To test whether these CDS-mapping reads were due to Stau1 association with translating ribosomes, we compared their density to the density of ribosome footprints (ribo-seq; Fig. 1f). For both Stau1-WT (Fig. 1f; Spearman correlation = 0.89) and Stau1-mut (data not shown), CROSS read density strongly correlated with ribosome density in CDS regions. This correlation held for the entire gene population, thus suggesting that Stau1 generally associates with elongating ribosomes.

In sum, our data indicate that Stau1 is generally associated with the 60S ribosomal subunit, both on and off mRNA. Further, this ribosome association does not require dsRBD functionality but is partially dependent on RNA integrity. Last, Stau1 appears to associate with actively translating, not stalled, ribosomes.

Stau1 binds paired Alu elements in 3′ UTRs

Whereas WT and mut libraries were quite similar in their rRNA content, they were quite different with regard to Alu repeat-mapping reads. Alu repeats are ∼300-nt primate-specific mobile elements in the short interspersed nuclear element family; the human genome contains ∼1 million Alu elements, primarily in intergenic regions, introns and 3′ UTRs. Reads mapping to Alu repeats constituted 42% and 28% of non-rRNA-mapping reads in WT FOOT and CROSS libraries, respectively, but only 19% and 14% in the corresponding mut libraries (Supplementary Fig. 2). Greater Alu enrichment in WT libraries suggested that their interaction depended on Stau1's ability to bind dsRNA. Consistently with this, WT CROSS reads were often highly enriched over and adjacent to closely spaced Alu pairs likely to form dsRNA secondary structures. We detected such Alu-pair Stau1-binding sites on only two large intergenic noncoding RNAs (NR_026757 and NR_026999) and minimally in introns (Supplementary Fig. 4). Conversely, they were highly enriched in 3′ UTRs (Fig. 2a and Supplementary Fig. 4) and in select 'intergenic' regions immediately 3′ to annotated 3′ UTRs (Fig. 2b and Supplementary Fig. 4). Polyadenylation-site sequencing (PAS-seq) revealed the intergenic regions to represent strongly distal 3′ UTRs [START_REF] Miura | Widespread and extensive lengthening of 3′ UTRs in the mammalian brain[END_REF] (Fig. 2b). Overall, we detected 515 strongly distal 3′ UTRs enriched for Stau1-WT CROSS reads (Supplementary Table 1), most of which contained multiple Alu pairs. To identify those 3′ UTRs most enriched for dsRBD-dependent Stau1 binding, we called peaks in the WT CROSS libraries by using ASPeak (an expression-sensitive peak-calling algorithm [START_REF] Kucukural | ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq[END_REF] ). We then compared, for each gene, the cumulative read counts under peak positions in the WT and mut CROSS libraries (Fig. 2c,d). Overall, the data sets were highly correlated (r = 0.83). Nonetheless, an outlier population (n = 574; Supplementary Table 2) exhibited much higher cross-linking (by a factor of 2.7) in WT than in mut (Fig. 2c,d); these outliers constitute a set of high-confidence 3′ UTRs displaying dsRNA-dependent Stau1 binding.

We next investigated the structural features of these targets. To identify those containing Alu pairs, we wrote an algorithm to identify, transcriptome wide, pairs of full-length Alu elements in the same (tandem) or opposite (inverted) orientation. Overlaying the inter-Alu distance for tandem Alu pairs on the WT versus mut CROSS scatter plot (Fig. 2c) revealed no specific relationship between tandem pairs and Stau1 cross-linking. However, the inverted Alu-pair overlay revealed a striking coincidence with the above outlier population (Fig. 2d). Further, inverted Alu elements separated by the least distance were the most outlying (Fig. 2d).

We confirmed the inverse relationship between dsRBD-dependent Stau1 cross-linking efficiency and inverted-pair inter-Alu distance in composite plots (Fig. 3a and Supplementary Fig. 5). We found similar, but less striking, results for inverted pairs containing partial Alu elements and for inverted pairs in introns and strongly distal 3′ UTRs (Supplementary Fig. 5a). As expected, we observed no specific mapping of WT reads on tandem Alu pairs or mapping of mut reads on Alu pairs in either orientation (Fig. 3a). The inverse correlation between Stau1-WT cross-linking efficiency and inverted-pair inter-Alu distance is consistent with the expectation that secondary-structure formation should inversely correlate with pairing-partner distance.

Because intact Alu elements are ∼300 nt, inverted pairs containing full-length Alu elements could potentially form very long helices. However, individual elements in pairs exhibiting the highest WT cross-linking signal were often from different Alu families unlikely to be fully complementary. Consistently with this, in silico folding of an inverted Alu pair exhibiting one of the strongest Stau1-WT occupancies suggests the presence of many short helices interrupted by small loops (Fig. 3b). To assess the generalizability of this, we folded in silico all-full-length, 3′-UTR inverted Alu pairs highly enriched for Stau1-WT crosslinking and compared them to 3′-UTR sequences of similar length randomly chosen from nontarget genes. Histograms of predicted helix and loop lengths (Fig. 3c,d) revealed that Stau1-interacting Alu pairs tend to form structures with multiple helices containing <30 interrupted base pairs, spaced by 2-to 10-nt loops. Conversely, nontarget 3′ UTRs were predicted to have significantly shorter paired stretches (Wilcoxon rank-sum test, P = 0.04) interrupted by longer loops (Wilcoxon rank-sum test, P = 6 × 10 -5 ).

Non-Alu 3′-UTR targets

Among the outlier population in Figure 2d, 201 of 574 contained clearly identifiable inverted Alu pairs (Alu targets), and 373 of 574 did not (non-Alu targets; Supplementary Table 2). Many of these non-Alu targets had clearly defined WT footprints in regions with high base-pairing probability (Fig. 4). A few contained a single strong footprint corresponding to a short stem-loop structure (Fig. 4a); others resembled inverted Alu pairs with many consecutive helices separated by short loops (Fig. 4b). The largest set, however, consisted of complex structures covering a few hundred nucleotides within which Stau1 footprints could be observed on multiple 7-to 40-bp helices (Fig. 4c,d). WT footprints were also present on the Arf1 3′ UTR, for which the precise Stau1-binding site was previously mapped by mutagenesis (Supplementary Fig. 6a) [START_REF] Kim | Staufen1 regulates diverse classes of mammalian transcripts[END_REF] .

Comparison of FOOT and CROSS reads mapping to individual 3′ UTRs revealed that CROSS reads generally extended over much more of the 3′ UTR than did FOOT reads (for example, Fig. 4c). We could even observe extensive CROSS read coverage for many 3′ UTRs having no detectable footprints (Supplementary Fig. 6b). Greater abundance of such CROSS reads in WT libraries than in mut libraries indicated that they depended on Stau1's ability to bind dsRNA. This suggested that the kinetically stable Stau1-binding sites revealed by native footprinting represent only a small subset of RNA-interaction sites occurring within cells. Supporting the notion of many low-affinity Stau1-interaction sites in vivo, we observed a strong correlation (r = 0.63, P < 2.2 × 10 -16 ) over all expressed genes between average per-nucleotide predicted secondary-structure strength (ΔG of the minimum freeenergy structure/3′-UTR length) and the ratio of total WT/mut CROSS reads per 3′ UTR (Fig. 5a). We observed a similarly strong correlation (r = 0.55, P < 2.2 × 10 -16 ) between this ratio and 3′-UTR GC content in all expressed genes (Fig. 5b).

We conclude that some Stau1-binding sites in 3′ UTRs consist of highly defined structures containing multiple short helices to which Stau1 binding is kinetically stable. Other binding sites, however, are more kinetically labile, and the extent of Stau1 occupancy on these sites is a function of overall 3′-UTR secondary structure-forming propensity, often driven by high GC content.

dsRBD-dependent binding of Stau1 to CDS regions

As previously discussed, both WT and mut CROSS reads mirrored ribosome density across CDS regions transcriptome wide (Fig. 1f). WT and mut reads were also similarly distributed relative to start and stop codons (Fig. 1e). In contrast to the general population, however, our 373 non-Alu 3′-UTR target genes had significantly greater CROSS reads in CDS regions for WT than for mut (Fig. 6a). This strong relationship between 3′ UTR and CDS WT/mut cross-linking initially suggested to us that dsRBD-dependent Stau1 binding within the 3′ UTR increases its association with CDS-bound ribosomes. Consistently with this, the correlation between preferential WT cross-linking in 3′-UTR and CDS regions held true for the entire mRNA population (Fig. 6b; r = 0.61, P < 2.2 × 10 -16 ), with our identified 3′-UTR target genes simply being strongly skewed toward the higher end of both ratios. However, we also found that predicted per-nucleotide secondary structure-forming propensity and GC content were strongly correlated (r = 0.55 and 0.73, respectively, P < 2.2 × 10 -16 ) between the 3′-UTR and CDS regions of individual genes (Fig. 6c,d); that is, the genes with high 3′-UTR secondary structure-forming propensity and GC content also tend to have high CDS secondary structure-forming propensity and GC content. Consistently with this, preferential WT cross-linking in CDS regions strongly correlated with both predicted CDS secondary structure and GC content (Fig. 6d ande, r = 0.62 and 0.65, respectively, P < 2.2 × 10 -16 ) and the 5′ UTR (r = 0.2; data not shown). These analyses suggest that enhanced Stau1-WT binding within CDS regions is primarily driven by GC content and secondary structureforming propensity of the CDS itself, rather than by interactions of 3′ UTR-bound Stau1 with CDS-bound ribosomes.

From the above data, we conclude that Stau1 interacts to varying extents with the CDS and 3′-UTR regions of all cellular mRNAs in a manner dependent on their secondary structureforming propensities. Further, the observed correlation between dsRBD-dependent Stau1 occupancy in CDS and 3′-UTR regions mainly reflects similar GC content between the CDS and 3′ UTR in individual genes rather than any direct effect of 3′-UTR binding on CDS binding. Instead, Stau1-WT occupancy on CDS regions appears to be driven by a combination of direct interactions with CDS secondary structures and its dsRBDindependent association with actively translating ribosomes.

Gene ontology analysis

To assess whether any particular gene classes were specifically enriched for dsRBDdependent Stau1 binding, we performed gene ontology analysis using GeneCodis [START_REF] Carmona-Saez | GENECODIS: a webbased tool for finding significant concurrent annotations in gene lists[END_REF][START_REF] Nogales-Cadenas | GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information[END_REF][START_REF] Tabas-Madrid | GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics[END_REF] (Supplementary Table 3). We obtained the most significant associations for the 469 genes having the highest WT/mut CDS cross-linking ratios (>1.9) and the 515 genes exhibiting high WT cross-linking to strongly distal 3′ UTRs. Both sets were highly enriched in transcription-regulatory proteins (P = 7.1 × 10 -13 and P = 1.1 × 10 -13 , respectively). Among transcription-factor types, C2H2 zinc-finger proteins were the most enriched (P = 4.5 × 10 -6 ), with homeobox and high-mobility group (HMG) proteins following close behind (P = 1.3 × 10 -5 and 6.9 × 10 -5 , respectively). Consistently with the strong correlation between Stau1 CDS and 3′-UTR occupancy, transcription-regulatory proteins were also highly enriched among our 373 non-Alu 3′-UTR targets (P = 6.9 × 10 -7 ). Thus Stau1 may have a role in post-transcriptional regulation of transcription factors. Also enriched in the non-Alu and extended 3′-UTR targets (P = 0.001 and P = 5.0 × 10 -5 , respectively), but not in the 469 high CDS targets, were proteins involved in cell-cycle control.

Functional consequences of varying Stau1 protein levels

To directly test the functional consequences of Stau1 binding, we next varied intracellular Stau1 concentration (Fig. 7a,b). Transduction of HEK293 FLP-in cells with a lentivirus expressing an anti-Stau1 short hairpin RNA (shRNA) stably reduced endogenous Stau1 to ∼20% normal levels (UNDER, Fig. 7a). Incubation of our stably integrated Flag-Stau1-WT cells overnight (16 h) with a high level of doxycycline induced transgene overexpression by 300-400% relative to endogenous Stau1 (OVER). We then assessed effects of Stau1 depletion or overexpression by preparing cytoplasmic poly(A) + RNA-seq and ribo-seq libraries.

RNA-seq and ribo-seq read counts on individual genes were highly correlated both between biological replicates (r ≥ 0.98; E.P.R., unpublished data) and between UNDER and OVER samples (r ≥ 0.98; Supplementary Fig. 7). Other than STAU1 itself, there were no clear outlier genes between UNDER and OVER conditions for either RNA-seq or ribo-seq. Further, no significant changes in alternative-splicing patterns could be detected (data not shown); thus, at least in HEK cells, binding of Stau1 in introns is of little apparent consequence for pre-mRNA splicing. However, small negative correlations between RNA levels and Stau1 levels could be detected when we ordered transcripts by CDS GC content or preferential Stau1-WT CDS cross-linking (Fig. 7c,d). That is, transcripts with high CDS GC content (which drives greater Stau1-WT CDS binding) exhibited slightly lower cytoplasmic mRNA abundances when Stau1 was overexpressed than underexpressed.

The strongest observable effect of varying Stau1 concentration was on ribosome occupancy. Cumulative histograms revealed positive relationships between ribosome occupancy and both Stau1-WT CDS cross-linking and CDS GC content across the entire transcriptome (Fig. 7c,d; Spearman correlation r = 0.21 and r = 0.34, respectively, P < 2.2 × 10 -16 ). That is, genes with higher Stau1 CDS occupancy and higher CDS GC content exhibited increased ribosome occupancy upon Stau1 overexpression compared to Stau1 knockdown; conversely, genes with lower Stau1 CDS occupancy and CDS GC content exhibited decreased ribosome occupancy upon Stau1 overexpression compared to Stau1 knockdown. This suggests that higher Stau1 protein levels increase ribosome occupancy on high-GC-content transcripts at the expense of low-GC-content transcripts. Ontology analysis of the 400 genes exhibiting the greatest increase in ribosome occupancy between UNDER and OVER conditions revealed significant enrichments for transcription-regulatory proteins (P = 0.004) and zincbinding proteins (P = 1.1 × 10 -6 ; Supplementary Table 3), the same terms obtained above for genes exhibiting the highest CDS and extended 3′-UTR Stau1 occupancies.

Although we observed the strongest effects of varying Stau1 protein levels for genes with high Stau1 CDS occupancy, we also examined the effects of Stau1 over-and underexpression on our 3′-UTR non-Alu and Alu target sets. Ribosome occupancy increased slightly on non-Alu 3′-UTR targets (10% change from UNDER to OVER; P = 0.00005) when compared to the total population, whereas their mRNA levels decreased slightly (-2% change from UNDER to OVER; P = 0.01). Thus, non-Alu targets behaved like high-GCcontent mRNAs. Conversely, Alu targets exhibited no significant change in ribosome occupancy, but their cytoplasmic mRNA levels increased upon Stau1 upregulation (+8% change from UNDER to OVER; P = 0.03; Fig. 7e). Therefore, mRNAs containing 3′-UTR inverted Alu pairs behave differently from other cellular mRNAs in response to Stau1 abundance. For the strongly distal 3′-UTR Stau1-binding sites, we detected no significant effect of Stau1 expression on either mRNA levels or ribosome occupancy (E.P.R., unpublished data), possibly because such isoforms represent only a minor fraction of transcripts from individual loci.

To confirm that changes in Stau1 levels are of little consequence for levels of mRNAs with 3′ UTR-binding sites, we performed quantitative reverse-transcription PCR (qRT-PCR) on several Alu and non-Alu 3′-UTR target mRNAs including Arf1, a previously identified SMD target (Supplementary Fig. 8). Consistently with our RNA-seq results, neither downregulation nor overexpression of Stau1 had a significant impact on the abundance of tested targets (Supplementary Fig. 8a). Experiments performed in two other cell lines (Huh7 and SK-Hep1) in which either Stau1 or Stau2 or both were downregulated yielded similar results (Supplementary Fig. 8b,c).

Taken together, our results indicate that Stau1 binding to the CDS results in increased ribosome occupancy and in decreased mRNA levels proportionate to both the amount of bound Stau1 and the GC content of the target mRNA. Further, at least in the cell lines we tested, Stau1 binding within the 3′ UTR appears to be of little or no consequence for translation efficiency or steady-state mRNA levels.

Discussion

Like many RNA-binding factors, the Drosophila and mammalian Staufen proteins have been implicated in multiple post-transcriptional processes including alternative splicing [START_REF] Ravel-Chapuis | The RNA-binding protein Staufen1 is increased in DM1 skeletal muscle and promotes alternative pre-mRNA splicing[END_REF] , RNA localization [START_REF] St Johnston | Staufen, a gene required to localize maternal RNAs in the Drosophila egg[END_REF][START_REF] Köhrmann | Microtubule-dependent recruitment of Staufen-green fluorescent protein into large RNA-containing granules and subsequent dendritic transport in living hippocampal neurons[END_REF][START_REF] Micklem | Distinct roles of two conserved Staufen domains in oskar mRNA localization and translation[END_REF][START_REF] Macchi | The brain-specific double-stranded RNA-binding protein Staufen2: nucleolar accumulation and isoform-specific exportin-5-dependent export[END_REF][START_REF] Kiebler | The mammalian staufen protein localizes to the somatodendritic domain of cultured hippocampal neurons: implications for its involvement in mRNA transport[END_REF] , translational activation [START_REF] Dugré-Brisson | Interaction of Staufen1 with the 5′ end of mRNA facilitates translation of these RNAs[END_REF] and translation-dependent mRNA decay [START_REF] Kim | Mammalian Staufen1 recruits Upf1 to specific mRNA 3′UTRs so as to elicit mRNA decay[END_REF][START_REF] Gong | lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements[END_REF][START_REF] Gleghorn | Staufen1 dimerizes through a conserved motif and a degenerate dsRNA-binding domain to promote mRNA decay[END_REF][START_REF] Park | Staufen2 functions in Staufen1-mediated mRNA decay by binding to itself and its paralog and promoting UPF1 helicase but not ATPase activity[END_REF][START_REF] Vessey | A loss of function allele for murine Staufen1 leads to impairment of dendritic Staufen1-RNP delivery and dendritic spine morphogenesis[END_REF][START_REF] Kim | Staufen1 regulates diverse classes of mammalian transcripts[END_REF][START_REF] Cho | Staufen1-mediated mRNA decay functions in adipogenesis[END_REF][START_REF] Kretz | Control of somatic tissue differentiation by the long non-coding RNA TINCR[END_REF] . Which activity is observed depends on the cellular context, the identity of the bound RNA and the location of the binding site on the target RNA. Many of Staufen's previously documented activities parallel those of the EJC [START_REF] Dugré-Brisson | Interaction of Staufen1 with the 5′ end of mRNA facilitates translation of these RNAs[END_REF][START_REF] Kim | Mammalian Staufen1 recruits Upf1 to specific mRNA 3′UTRs so as to elicit mRNA decay[END_REF][START_REF] Gong | lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements[END_REF][START_REF] Gleghorn | Staufen1 dimerizes through a conserved motif and a degenerate dsRNA-binding domain to promote mRNA decay[END_REF][START_REF] Park | Staufen2 functions in Staufen1-mediated mRNA decay by binding to itself and its paralog and promoting UPF1 helicase but not ATPase activity[END_REF][START_REF] Ferrandon | Staufen protein associates with the 3′UTR of bicoid mRNA to form particles that move in a microtubule-dependent manner[END_REF][START_REF] Kim | Staufen1 regulates diverse classes of mammalian transcripts[END_REF][START_REF] Cho | Staufen1-mediated mRNA decay functions in adipogenesis[END_REF][START_REF] Ghosh | Control of RNP motility and localization by a splicing-dependent structure in oskar mRNA[END_REF][START_REF] Nott | Splicing enhances translation in mammalian cells: an additional function of the exon junction complex[END_REF][START_REF] Wiegand | Exon junction complexes mediate the enhancing effect of splicing on mRNA expression[END_REF][START_REF] Ivanov | Interactions between UPF1, eRFs, PABP and the exon junction complex suggest an integrated model for mammalian NMD pathways[END_REF] . To better understand EJC function, we recently determined the complete EJC RNA-binding landscape in HEK293 cells [START_REF] Singh | The cellular EJC interactome reveals higher-order mRNP structure and an EJC-SR protein nexus[END_REF] . Here we undertook the same analysis for Stau1.

Up to now, confirmed Staufen-binding sites were limited to a few well-characterized structures [START_REF] Ferrandon | Staufen protein associates with the 3′UTR of bicoid mRNA to form particles that move in a microtubule-dependent manner[END_REF][START_REF] Kim | Staufen1 regulates diverse classes of mammalian transcripts[END_REF] . Broader identification of Staufen-associated mRNAs has been attempted in various organisms by combination of native RIP protocols with microarray analyses [START_REF] Furic | A genome-wide approach identifies distinct but overlapping subsets of cellular mRNAs associated with Staufen1-and Staufen2-containing ribonucleoprotein complexes[END_REF][START_REF] Laver | Genome-wide analysis of Staufen-associated mRNAs identifies secondary structures that confer target specificity[END_REF][START_REF] Maher-Laporte | Genome wide identification of Staufen2-bound mRNAs in embryonic rat brains[END_REF][START_REF] Kusek | Asymmetric segregation of the double-stranded RNA binding protein Staufen2 during mammalian neural stem cell divisions promotes lineage progression[END_REF] . Unfortunately, however, such methodologies have yielded no consensus as to general features of Staufen targets. One recent study of Staufen-associated mRNAs from Drosophila oocytes reported enrichment of three different secondary-structural motifs that might explain Staufen binding specificity in flies [START_REF] Laver | Genome-wide analysis of Staufen-associated mRNAs identifies secondary structures that confer target specificity[END_REF] . However, the authors were unable to identify similar structural motifs among human Staufen-associated mRNAs from available native mammalian Stau1 and Stau2 RIP microarray data [START_REF] Furic | A genome-wide approach identifies distinct but overlapping subsets of cellular mRNAs associated with Staufen1-and Staufen2-containing ribonucleoprotein complexes[END_REF] . We show here that human Stau1 generally associates with actively translating ribosomes; therefore, it is impossible to discriminate between sites of direct Stau1-mRNA interaction via dsRNA binding and sites of indirect Stau1-mRNA association via elongating ribosomes without some sort of footprinting approach. Further, because of (i) Stau1's strong ribosome association, (ii) the prevalence of kinetically labile Stau1-binding sites in vivo and (iii) Stau1's ability to form new interactions with dsRNA after cell lysis, native RIP experiments are likely to be biased toward both highly translated mRNAs and RNAs containing the most stable sites of direct Stau1-dsRNA interaction. Our experimental design, which combined formaldehyde crosslinking and fragmentation of Stau1-associated RNAs, using both WT and mut proteins, allowed us to both avoid binding-site reassortment after cell lysis and discriminate between binding modes that do or do not require Stau1 dsRBD functionality.

The majority of non-rRNA reads in our cross-linked libraries mapped sense to 3′ UTRs and CDS regions. Within 3′ UTRs, we identified numerous high-occupancy Stau1-binding sites composed of either inverted Alu pairs (Alu targets) or sequences with extremely high secondary structure-forming propensity (non-Alu targets). Observable native Stau1 footprints showed that these structures often consist of several closely spaced helices separated by short loops. Bioinformatics analysis of the footprints, however, failed to identify any particular enriched motif (A.K. and E.P.R., unpublished data), results consistent with the idea that Staufen recognizes dsRNA in a sequence-independent manner [START_REF] Marión | A human sequence homologue of Staufen is an RNA-binding protein that is associated with polysomes and localizes to the rough endoplasmic reticulum[END_REF][START_REF] Wickham | Mammalian staufen is a doublestranded-RNA-and tubulin-binding protein which localizes to the rough endoplasmic reticulum[END_REF][START_REF] Monshausen | Two rat brain staufen isoforms differentially bind RNA[END_REF] .

Unexpectedly, in addition to detecting strong binding to large RNA secondary structures, we also detected extensive dsRBD-dependent Stau1 cross-linking extending throughout the entire length of 3′ UTRs and CDS regions. This cross-linking strongly correlated with both GC content and per-nucleotide predicted secondary-structure strength. Because GC content in CDS and 3′-UTR regions also correlate, mRNAs exhibiting preferential Stau1-WT 3′-UTR cross-linking also tend to exhibit preferential Stau1-WT CDS cross-linking. Inverted Alu pairs, the 3′ UTRs containing them and their associated CDS regions, however, exhibit average GC content. Despite high Stau1 occupancy on such 3′ UTRs, their CDS occupancies are close to levels that would be expected from their GC content alone. We therefore conclude that the strongest feature driving dsRBD-dependent Stau1 binding within CDS regions is the secondary structure-forming propensity of the CDS itself. Thus dsRBDdependent Stau1 binding to 3′ UTRs appears to be functionally uncoupled from dsRBD-dependent Stau1 binding to CDS regions, with the correlation between 3′ UTR and CDS cross-linking driven primarily by GC-content similarity.

Our results suggest that endogenous Stau1 RNA targets can be divided into two broad classes dependent on their structural topology. One class corresponds to stable RNA secondary structures such as inverted Alu pairs and other sequences with extremely high secondary structure-forming propensity. Such elements are capable of simultaneously binding multiple Staufen molecules whose association may be further stabilized by multimerization. Close association of multiple Staufen-binding sites would assure continuous Staufen occupancy even though individual protein molecules might come and go. It is of note that we generally detected such binding sites in annotated 3′ UTRs and extended 3′ UTRs, the latter being particularly rich in inverted Alu pairs. Recently, extended 3′ UTRs were shown to be especially prevalent in the brain. Because Stau1 is known to have a role in dendritic mRNA targeting, these stable RNA secondary structures with their longlived Stau1 associations could well be the functional binding sites through which Stau1 promotes proper subcellular mRNA localization in neurons.

The second class consists of smaller and more labile secondary structures as might occur in GC-rich CDS regions. Here our data indicate that transient Stau1 binding, perhaps by Stau1 molecules simultaneously interacting with elongating ribosomes, has a role in regulating translation. We arrived at this conclusion by analyzing cytoplasmic poly(A) + RNA-seq and ribo-seq data from cells under-and overexpressing Stau1. This allowed us to assess the effects of varying intracellular Stau1 concentration on both cytoplasmic mRNA levels and ribosome occupancy. Observable changes in mRNA levels were extremely subtle. Consistently with recent data indicating that Stau1 binding to mRNAs containing inverted Alu elements enhances their nucleocytoplasmic export [START_REF] Elbarbary | STAU1 binding 3′ UTR IRAlus complements nuclear retention to protect cells from PKR-mediated translational shutdown[END_REF] , we did observe a small positive effect of increasing Stau1 on cytoplasmic mRNA levels for our 3′-UTR Alu targets. Conversely, for all other sets of mRNAs exhibiting preferential Stau1-WT cross-linking, Stau1 levels negatively influenced cytoplasmic mRNA levels proportionately to CDS Stau1 occupancy but not to 3′-UTR occupancy. Thus we could find little evidence for SMD driven by 3′ UTR-bound Stau1, either over the entire mRNA population or for previously identified SMD targets. Instead, higher Stau1 levels led to a preferential increase in ribosome density on high-GC-content mRNAs.

We propose a model (Fig. 8) based on these findings, wherein ribosome-bound Stau1 molecules transiently interact with short dsRNA helices throughout the CDS and 3′ UTR. In the CDS, such interactions somehow serve to increase ribosome density. Because Stau1 interacts with actively translating ribosomes, the increase in ribosome density may reflect increased translation efficiency. One possibility is that Stau1 helps ribosomes elongate through otherwise inhibitory secondary structures by recruiting factors such as RNA helicase A (RHA or DHX9) to disrupt them. RHA is a positive regulator of translation on mRNAs containing 5′-UTR secondary structures [START_REF] Hartman | RNA helicase A is necessary for translation of selected messenger RNAs[END_REF] and is known to copurify with Stau1 (ref.

44 and E.P.R., unpublished data). Another abundant translational-regulatory protein that binds ribosomes and cross-links throughout CDS regions is the fragile X protein, FMRP [START_REF] Darnell | FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism[END_REF] . FMRP, however, is a negative regulator of translation. Whereas deletion of either FMRP or Stau1 causes neurological defects, the phenotypes are opposite: absence of FMRP leads to dendritic spine overgrowth [START_REF] Comery | Abnormal dendritic spines in fragile X knockout mice: maturation and pruning deficits[END_REF] , whereas absence of Stau1 results in fewer spines [START_REF] Vessey | A loss of function allele for murine Staufen1 leads to impairment of dendritic Staufen1-RNP delivery and dendritic spine morphogenesis[END_REF] . Thus it is possible that FMRP and Stau1 have opposing roles in synaptic protein production, with FMRP inhibiting translation and Stau1 promoting it.

Finally, mRNAs encoding transcription-regulatory proteins were recently reported as being enriched in Drosophila Staufen RIP samples [START_REF] Laver | Genome-wide analysis of Staufen-associated mRNAs identifies secondary structures that confer target specificity[END_REF] . Consistently with this, we found that mRNAs encoding transcription factors of the C2H2 zinc-finger, HMG and homeobox families were highly enriched among mRNAs exhibiting the highest preferential 3′-UTR and CDS Stau1-WT occupancy. Transcription factors and zinc-binding proteins were also highly enriched among the mRNAs whose ribosome density was most positively affected by Stau1 protein levels. Thus Stau1 may have a previously unrecognized role in the translational regulation of transcription-regulatory proteins.

Methods

Methods and any associated references are available in the online version of the paper.

Online Methods

Plasmids and cell lines pcDNA5-TetO-Flag was previously described [START_REF] Ivanov | Interactions between UPF1, eRFs, PABP and the exon junction complex suggest an integrated model for mammalian NMD pathways[END_REF] . A cDNA encoding Stau1 (HindIII-NotI) was inserted into the polylinker of pcDNA5-TetO-Flag. A cDNA encoding the Stau1 mutant lacking RNA-binding activity was created by PCR using primers carrying the mutations described in ref. 2, in which phenylalanines at position 216 in dsRBD3 and at position 319 in dsRBD4 were mutated into alanines.

Stable cell lines were generated as described in ref. 38. In these cells, the expression level of the stably integrated Flag-tagged protein was optimized by titration of doxycycline (Dox; 0-2,000 ng ml -1 ) to determine a concentration at which exogenous protein expression levels were comparable to those of endogenous counterparts.

Generation of Stau1-knockdown cell line

HEK293T LentiX cells (Clontech) were transfected with pGIPZ encoding shRNAs directed against Stau1 (Open Biosystems, CloneID: V2LHS_42695), pPAX2 and pMD2.G at a 12:9:3 ratio with Lipofectamine 2000 (Invitrogen). 2 d after transfection, the supernatant of transfected cells was collected and passed through a 0.45-μm filter. To generate the Stau1knockdown cell line, HEK293 TRex cells (Invitrogen) were transduced with 7 mL of the supernatant of lentiviral-producing cells in the presence of 10 μg ml -1 polybrene for 6 h. Transduced cells were then selected in the presence of puromycin (3 μg ml -1 ) for 2 weeks.

Stau1 RIPiT

The procedure was performed essentially as described in (ref. 38). For each Staufen purification, TRex-HEK293 cells containing a stable copy of Flag-tagged Stau proteins (Stau1-WT and Stau1-mut) or control cells (expressing Flag tag only) were grown in four 15-cm plates. Expression of the Flag-tagged protein was induced with doxycycline for 16 h. 1 h before cell harvesting, cycloheximide (CHX) was added to 100 μg ml -1 . The monolayer was rinsed and harvested in phosphate-buffered saline (PBS) containing 100 μg ml -1 CHX. The cells were lysed in 3 ml hypotonic lysis buffer (20 mM Tris-HCl, pH 7.5, 15 mM NaCl, 10 mM EDTA, 0.5% NP-40, 0.1% Triton X-100, 1× EDTA-free protease inhibitor cocktail (ROCHE), and 100 μg ml -1 CHX) for 10 min on ice. The suspension was sonicated (Branson Digital Sonifier-250) at 40% amplitude with a Microtip for a total of 20 s (in 2-s bursts with 10-s intervals). NaCl was adjusted to 150 mM, and the lysate was cleared by centrifugation at 15,000g for 10 min at 4 °C. The lysate was incubated for 2 h at 4 °C with 420 μl of anti-Flag agarose beads (50% slurry, Sigma) prewashed twice with 10 ml isotonic wash buffer (IsoWB) (20 mM Tris-HCl, pH 7.5, 150 mM NaCl, and 0.1% NP-40). The RNA-protein (RNP) complexes captured on beads were washed four times (4 × 10 ml) with 10 ml IsoWB. After the fourth wash, bound RNP complexes were incubated with one bed volume of IsoWB containing 1 U μl -1 of RNase I for 10 min at 37 °C with intermittent shaking. RNP complexes were again washed four times with 10 ml IsoWB. Flag epitopecontaining complexes were affinity eluted from the beads in one bed volume of IsoWB containing 250 μg.ml -1 Flag peptide with gentle shaking at 4 °C for 2 h. To prepare the recovered elution for input into a second IP, its volume was adjusted to 400 μl and its composition adjusted to that of the lysis buffer above with NaCl at 150 mM. The suspension was incubated with 7 μg of anti-Stau1 antibody (ab105398, Abcam, validation of IP in Supplementary Fig. 1c,d) precoupled to 35 μl of Protein G Dynabeads (Invitrogen) according to the manufacturer's instructions. Immunoprecipitation was carried out at 4 °C for 2 h. Captured RNP complexes were washed six times with 1 ml of ice-cold IsoWB and eluted with 200 μl of clear sample buffer (100 mM Tris-HCl, pH 6.8, 4% SDS, 10 mM EDTA and 100 mM DTT) at 25 °C for 5 min and subsequently at 95 °C for 2 min.

For IPs under protein-cross-linking conditions, cells were collected and rinsed once in PBS + CHX and then resuspended in PBS + CHX. Formaldehyde was added to 0.1%, and the suspension was gently mixed at RT for 10 min. A one-tenth volume of quenching buffer (2.5 M glycine, and 25 mM Tris base) was added. Cells were pelleted and lysed in hypotonic lysis buffer supplemented with 0.1% SDS and 0.1% sodium deoxycholate. Sonication after cell lysis was performed at 40% amplitude with a Microtip for a total of 90 s (in 5-s bursts with 30-s intervals). After Flag IP as described above, IP samples were washed twice with IsoWB + 0.1% SDS and 0.1% sodium deoxycholate and then with IsoWB. All subsequent steps were as above with omission of RNase I treatment.

RIPiT RNA extraction

The volume of RIPiT elution was extracted as described in ref. 38. For Stau1 cross-linked RIPiT experiments, extracted RNAs were depleted of rRNA before cDNA library construction with the Ribozero rRNA-removal kit from Epicentre.

Preparation of samples for RNA-seq

75 μg of total RNA were poly(A)-selected with the Dynabeads mRNA-purification kit (Invitrogen). After poly(A) selection, mRNAs were fragmented with RNA-fragmentation buffer (Ambion) for 4 min and 30 s at 70 °C to obtain fragments 100-125 nt long. After fragmentation, RNAs were precipitated in three volumes of 100% ethanol at -20 °C overnight. After a wash with 70% ethanol, RNA was resuspended in 5 μl of water and the 3′ ends dephosphorylated with PNK (New England BioLabs) for 1 h at 37 °C. After this, RNAs were subjected to cDNA library preparation.

Poly(A)-site sequencing (PAS-seq)

75 μg of total RNA were poly(A)-selected with the Dynabeads mRNA purification kit (Invitrogen). After poly(A) selection, mRNAs were fragmented with RNA fragmentation buffer (Ambion) for 5 min at 70 °C to obtain fragments 60-80 nt long. After fragmentation, RNAs were reverse transcribed with an anchored and barcoded oligo(dT) 21 VN (where V corresponds to A, C or G residues) containing the sequences complementary to Illumina's paired-end primers (PE1.0 and PE2.0) separated by a polyethylene glycol (PEG) spacer. After reverse transcription (RT) with Superscript III (Invitrogen), cDNAs were size-selected and circularized with Circligase I (Epicentre) for 4 h at 60 °C. This was followed by inactivation at 80 °C for 10 min. After circularization, cDNAs were PCR-amplified with Illumina's PE1.0 and 2.0 primers for a total of 14 cycles. PCR products were size-selected on a nondenaturing polyacrylamide gel and sent for high-throughput sequencing on Illumina's HiSeq2000 platform.

Quantitative RT-PCR

Total RNA from HEK293, Huh7 and HepG2 cells transfected with control shRNA or a Stau1-targeting shRNA construct was reverse transcribed with the Vilo RT kit from Life Technologies. Obtained cDNAs were then used as input for quantitative PCR analysis as described in ref. 48 with the following primers: GAPDH forward, 5′ tccaccaccctgttgctgtag 3′ and reverse, 5′ acccactcctccacctttgac3′; Arf1 forward, 5′ atcttcgcctcccgactc 3′ and reverse, 5′ atgcttgtggacaggtgga3′; C11orf58 forward, 5′ cagacgacgatctgggatct 3′ and reverse, 5′ tgatctcctataacaagacgaccag 3′; PAICS forward, 5′ aaggaaaagctgcaatctcaa 3′ and reverse, 5′ ccccacattttctggtgaag 3′; MDM2 forward, 5′ catgcctgcccactttaga 3′ and reverse, 5′ ggaggctcccaactgctt 3′; MDM4 forward, 5′ agggatgaaatgcttcttgg 3′ and reverse, 5′ aaggttgctatgaggtctaccttg 3′.

Sucrose-gradient sedimentation of Flag-Stau1-WT cells

HEK293 cells were plated at 5 million in a 150-mm 2 plate and Flag-Stau1-WT expression induced overnight with doxycycline at 0.5 ng ml -1 . 16 h after induction, cells were either incubated with cycloheximide (100 μg ml -1 ) for 10 min or with harringtonine (2 μg ml -1 ) for either 3, 10 or 40 min. This was followed by incubation with cycloheximide (100 μg ml -1 ) for 10 min. Cells were then washed in PBS + cycloheximide (100 μg ml -1 ) or PBS + harringtonine (2 μg ml -1 ) + cycloheximide (100 μg ml -1 ) and scraped. Cells were then lysed in 1 ml of lysis buffer (10 mM Tris-HCl, pH 7.5, 5 mM MgCl 2 , 100 mM KCl, 1% Triton X-100, 2 mM DTT, 100 μg/ml cycloheximide and 1× Protease-Inhibitor Cocktail EDTAfree (Roche). Lysate was homogenized by gentle pipetting up and down with a P1000 pipettor for a total of eight strokes and incubated at 4 °C for 10 min. The lysate was centrifuged at 1,300g for 10 min at 4 °C; the supernatant was recovered. After this, samples were loaded on top of a 10-50% (w/v) sucrose gradient (20 mM HEPES-KOH, pH 7.4, 5 mM MgCl 2 , 100 mM KCl, 2 mM DTT, and 100 μg ml -1 cycloheximide) and centrifuged in a SW-40ti rotor at 35,000 r.p.m. for 2 h 40 min at 4 °C. Samples were fractionated into 14 individual samples that were subjected to SDS-PAGE to monitor Stau1 (ab105398, Abcam, 1:1,000 dilution) and Rpl26 (Bethyl, A300-685A, 1:1,000 dilution) levels by westernblotting. Experimental validation of antibodies used for western blots can be found at the manufacturers' websites.

Oligo(dT) pulldown of poly(A) RNAs after UV exposure of living cells

HEK293 Flp-In cells were exposed to 0, 0.2, 0.4 and 0.8 J cm -2 of 254-nm UV light. Cells were then scraped and lysed with binding buffer (0.5 M NaCl, 10 mM Tris-HCl, pH 7.5, 0.5% SDS, 0.1 mM EDTA and protease inhibitor cocktail). Cell lysates were passed through a 22-gauge syringe needle five times and spun at 15,000g for 10 min. Cleared cell lysates were added to oligo(dT) cellulose beads (Ambion, AM10020) previously washed in binding buffer. After 1 h of incubation at room temperature, beads were washed three times with binding buffer and once with nondenaturing wash buffer (0.5 M NaCl, 10 mM Tris-HCl, pH 7.5, 0.1% NP-40, 0.1% Triton-X 100 and 0.2 mM EDTA). Finally, beads were resuspended in elution buffer (10 mM Tris-HCl, pH 7.5, and 1 mM EDTA) complemented with RNase A/T1 cocktail (Ambion AM2286) and incubated at 37 °C for 1 h. The supernatant was finally recovered and loaded on a 12% SDS-PAGE for western-blotting of hnRNPA1 and Stau1.

Stau1 RNA reassociation test

HEK293 Flp-In, Flag-Stau1-WT or Flag-Stau1-mut cells were harvested and lysed as described in the Stau1 RIPiT section (above). After cell lysis, 0.1 pmol of a radiolabeled in vitro-transcribed Arf1 3′-UTR sequence (labeled with [α-32 P]UTP) was added for each 150mm 2 plate. The remaining procedure is identical to that described in the Stau1 RIPiT section until the Flag elution step. Flag eluates on each sample were monitored for radioactivity with liquid scintillation.

Ribosome profiling

HEK293 Flag-Stau1-WT cells incubated in the presence of ng ml -1 of doxycycline (Stau1 overexpression) or not (control) as well as HEK293 cells expressing the shRNAs against Stau1 (Stau1 knockdown) were seeded at 5 million cells in a 150-mm dish. After 16 h of culture, cycloheximide was added to 100 μg ml -1 for 10 min. Cells were then washed two times in ice-cold PBS + cycloheximide (100 μg ml -1 ) and scraped in 1 ml of PBS + cycloheximide (100 μg ml -1 ). Cells were pelleted at 500g for 5 min at 4 °C and lysed in 1 ml of lysis buffer (10 mM Tris-HCl, pH 7.5, 5 mM MgCl 2 , 100 mM KCl, 1% Triton X-100, mM DTT, 100 μg/ml cycloheximide and 1× Protease-Inhibitor Cocktail EDTA-free (Roche)). Lysate was homogenized with a P1000 pipettor by gentle pipetting up and down for a total of eight strokes and incubated at 4 °C for 10 min. The lysate was centrifuged at 1,300g for 10 min at 4 °C, the supernatant recovered and the absorbance at 260 nm measured. For the footprinting, 5 A 260 units of the cleared cell lysates were incubated with 300 units of RNase T1 (Fermentas) and 500 ng of RNase A (Ambion) for 30 min at RT After this, samples were loaded on top of a 10-50% (w/v) linear sucrose gradient (20 mM HEPES-KOH, pH 7.4, 5 mM MgCl 2 , 100 mM KCl, 2 mM DTT and 100 μg ml -1 of cycloheximide) and centrifuged in a SW-40ti rotor at 35,000 r.p.m. for 2 h 40 min at 4 °C.

Samples were then collected from the top of the gradient while absorbance was measured at 254 nm and the fraction corresponding to 80S monosomes recovered. The collected fraction was complemented with SDS to 1% final and proteinase K (200 μg ml -1 ) and then incubated at 42 °C for 45 min. After proteinase K treatment, RNA was extracted with one volume of phenol (pH 4.5)/chloroform/isoamyl alcohol (25:24:1). The recovered aqueous phase was supplemented with 20 μg of glycogen, 300 mM sodium acetate, pH 5.2, and 10 mM MgCl 2 . RNA was precipitated with three volumes of 100% ethanol at -20 °C overnight. After a wash with 70% ethanol, RNA was resuspended in 5 μl of water and the 3′ ends dephosphorylated with PNK (New England BioLabs) in MES buffer (100 mM MES-NaOH, pH 5.5, 10 mM MgCl 2 , 10 mM β-mercaptoethanol and 300 mM NaCl) at 37 °C for 3 h. Dephosphorylated RNA footprints were then resolved on a 15% acrylamide (19:1), 8 M urea denaturing gel for 1 h 30 min at 35 W and fragments ranging from 26 nt to 32 nt sizeselected from the gel. Size-selected RNAs were extracted from the gel slice by overnight nutation at RT in RNA elution buffer (300 mM NaCl, and 10 mM EDTA). The recovered aqueous phase was supplemented with 20 μg of glycogen, 300 mM sodium acetate, pH 5.2, and 10 mM MgCl 2 . RNA was precipitated with three volumes of 100% ethanol at -20 °C overnight. After a wash with 70% ethanol, RNA was resuspended in 5 μl of water and subjected to cDNA library construction.

The remaining undigested cell lysates were extracted with an equal volume of phenol (pH 4.5)/chloroform/isoamyl alcohol (25:24:1). The recovered aqueous phase was supplemented with 20 μg of glycogen, 300 mM sodium acetate, pH 5.2, and 10 mM MgCl 2 . RNA was precipitated with three volumes of 100% ethanol at -20 °C overnight. After a wash with 70% ethanol, RNA was resuspended in 9 μl of water and fragmented with RNA fragmentation buffer (Ambion) at 70 °C for 4 min 30 s in order to obtain RNA fragments of 100-150 nt. Fragmented RNAs were supplemented with 20 μg of glycogen, 300 mM sodium acetate, pH 5.2, and 10 mM MgCl 2 and precipitated with three volumes of 100% ethanol at -20 °C overnight. After a wash with 70% ethanol, RNA was resuspended in 5 μl of water and dephosphorylated as described above with PNK. After 3′-end dephosphorylation, RNA fragments were subjected to cDNA library construction.

Illumina cDNA library construction

cDNA libraries were prepared with a homemade kit (E.E.H., unpublished data). Briefly, RNA fragments with a 3′-OH were ligated to a preadenylated DNA adaptor. Following this, ligated RNAs were reverse transcribed with Superscript III (Invitrogen) with a barcoded reverse-transcription primer that anneals to the preadenylated adaptor. After reverse transcription, cDNAs were resolved in a denaturing gel (10% acrylamide and 8 M urea) for 1 h and 45 min at 35 W. Gel-purified cDNAs were then circularized with CircLig ase I (Epicentre) and PCR-amplified with Illumina's paired-end primers 1.0 and 2.0 for 5 cycles (ribosome footprints), 12 cycles (Stau1 RIPiT) or 16 cycles (RNA-seq libraries). PCR amplicons were gel-purified and submitted for sequencing on the Illumina HiSeq 2000 platform.

Mapping of high-throughput sequencing reads

First, reads were split with respect to their 5′-barcode sequence. After this, 5′-barcode and 3′-adaptor sequences were removed from reads. Reads were then aligned to University of California, Santa Cruz (UCSC) human hg18 assembly with TopHat [START_REF] Trapnell | TopHat: discovering splice junctions with RNA-Seq[END_REF] . Unmapped reads from TopHat were then mapped with Bowtie 50 to a custom set of sequences including 18S, 28S, 45S, 5S and 5.8S rRNA, repeat elements, small-nuclear RNAs (snRNAs), tRNAs, microRNAs and pre-microRNAs.

Transcript-level quantification and normalization for all high-throughput sequencing libraries

Read counts from all high-throughput sequencing libraries were normalized to the total number of mapped reads. When a single read aligned across the boundary of two different regions (for example, CDS and 3′ UTR), the read was divided proportionally to the aligned length in the given region. To quantify gene expression, reads per kilobase per million of mapped reads (RPKM) were calculated for the most abundant isoform of each gene.

Transcriptome-wide pairing of inverted and tandem Alu elements

Genomic coordinates for all Alu elements were obtained from the repeat-masker track of the UCSC genome browser. With the BedTools 51 intersectBed function with -s(strand) option, we obtained coordinates of all Alu elements located in 3′ UTRs, distal 3′ UTRs and introns. After this step, Alu elements in Watson and Crick strands were separated. To pair inverted Alu elements, we used the ClosestBed function between Alu elements in the Watson and Crick strands. The same steps were also performed to detect same-strand pairs on Watson-Watson and Crick-Crick pairs. In this case, tandem pairs that had an inverted Alu pair less than 2,000 nt apart were excluded from the analysis.

Definition of distal 3′-UTR regions

To define distal 3′-UTR regions, we used PAS-seq mapped reads. For this, peaks were called from the PAS-seq library with ASPeak. With the BedIntersect function (BedTools) we found all genes that had a polyadenylation site within 10,000 nt of the canonical polyadenylation site, provided that they were upstream of the transcription start site of the downstream gene. If multiple peaks were called within that interval, the called peak most distal to the canonical polyadenylation site was selected.

Counting of sequencing reads for Alu pairs separated by different distances

With the genomic coordinates of tandem and inverted Alu pairs described above, we created a new file for each region (3′ UTR, distal 3′ UTR and introns) that contained the genomic coordinates of each Alu element within every pair in addition to 1,000 nt upstream of the 5′most Alu element and 1,000 nt downstream of the 3′-most Alu element. After this, read counts from Stau1-WT CROSS, Stau1-mut CROSS and RNA-seq were obtained for each defined region within pairs and then normalized with RNA-seq RPKM value for the same interval. Normalized read counts for each interval were added for all Alu pairs located between 0 and 200 nt, 200 and 500 nt, 500 and 1,000 nt, 1,000 and 2,000 nt, and 2,000 nt and beyond.

Secondary-structure analysis of inverted Alu pairs

To analyze the secondary structure of inverted Alu pairs, we obtained the sequence of both Alu elements, including the region separating them. As a control, same-length sequences were randomly chosen from the list of genes devoid of Stau1-target sites. After the sequences were obtained, the Vienna Package RNAfold 2.1.1 (ref. 47) was used to predict secondary structures.

Analysis of ribosome-profiling reads

To calculate translational-efficiency changes upon knockdown or overexpression of Stau1, we used a generalized linear model (GLM). We used the number of sequencing reads mapping to the annotated coding region (or ORF) for each RefSeq transcript. In the GLM, we used the cell type (overexpression of Stau1WT, Stau1-shRNA), sequence-library preparation batch and type of sequence data (RNA-seq or ribo-seq) as predictor variables of the number of mapped reads per transcript. We had two biological replicates for all conditions, which were used to estimate a biological variability in the number of counts. We used a trended dispersion estimation method, following ref. 52. To extract translationefficiency changes upon a given treatment, we used the contrast between type of sequence data, ribo-seq versus RNA-seq, in each pair of conditions. Model of Stau1 RNA binding and its functional role in translation. Stau1 interacts with both actively translating ribosomes and secondary structures in CDS and 3′-UTR regions. Some 3′ UTRs contain highly complex secondary structures (for example, inverted Alu pairs) that serve as kinetically stable Stau1-binding sites. However, Stau1 also makes transient interactions with smaller secondary structures throughout CDS and 3′-UTR regions. Formation of these structures is a function of overall CDS and 3′-UTR GC content. Whereas interaction of Stau1 with 3′-UTR Alu pairs has a small positive effect on cytoplasmic mRNA levels, high Stau1 CDS occupancy both increases ribosome density and slightly decreases cytoplasmic mRNA levels.

Figure 1 .

 1 Figure 1. Mapping of Stau1 RNA-binding sites reveals coding regions and 3′ UTRs as major occupancy sites. (a) Scheme of the tagged WT and mut Stau1 proteins used in this study. (b) Scheme of tandem affinity purifications for footprinting (FOOT) and cross-linking (CROSS) library construction. (c) Pie charts showing the distribution of sequencing reads for each library. (d) Example of Stau1 cross-inking signal across the CDS of ALDOA (NM_184041.2). (e) Composite plot of the distribution of sequencing reads across the 5′ UTR, CDS and 3′ UTR of all genes for RNA-seq (red), Stau1-WT CROSS (blue) and Stau1mut CROSS (black) libraries. (f) Per-gene scatter plot of CDS ribo-seq read density versus CDS Stau1-WT CROSS read density with the associated Spearman correlation and calculated P value (n = 2 biological replicates).

Figure 2 .

 2 Figure 2. Inverted Alu pairs are an important class of Stau1-binding sites. (a,b) Distribution of sequencing reads obtained from RNA-seq (green), Stau1-WT CROSS (yellow), Stau1-mut CROSS (blue), Stau1-WT FOOT (brown), Stau1-mut FOOT (violet) and PAS-seq libraries (black) for the 3′ UTR of PAICS (NM_001079524) (a) and the strongly distal 3′ UTR of BRI3BP (NM_080626.5) (b). (c,d) Per-gene scatter plots of Stau1-WT CROSS and Stau1mut CROSS read counts under called 3′-UTR Stau1-WT CROSS peak positions with associated Spearman correlation and calculated P values (n = 2 biological replicates). Genes containing a 3′ UTR Alu pair are colored with respect to the distance between each tandem Alu pair (c) or inverted Alu pair (d). The dashed line corresponds to the 2.7 cutoff in the ratio of Stau1-WT over mut read counts.

Figure 3 .

 3 Figure 3. Characterization of the structural features of Stau1 Alu-binding sites. (a) Composite plot of Stau1-WT CROSS, Stau1-mut CROSS and RNA-seq read counts around tandem or inverted Alu pairs. Read counts normalized to host gene reads per kilobase per million mapped reads (RPKM) were determined for a region spanning 1 kb up-and downstream of the paired Alu elements separated by the indicated distance (n = 2 biological replicates). (b) Example of a 3′-UTR inverted Alu-binding site in C11orf58 (NM_014267.5) showing read counts per million mapped (pmm) reads for RNA-seq, Stau1-WT CROSS and Stau1-mut CROSS libraries. The centroid secondary structure for this Alu pair predicted with the Vienna folding package 47 is shown below center. (c,d) Length distribution of predicted helices (c) and loop (d) regions within secondary structures of 3′-UTR inverted Alu pairs or 3′-UTR sequences of identical size randomly picked from nontarget genes. P values corresponding to the comparison of helix and loop length distributions between Stau1 Alu targets and random 3′ UTRs were calculated with the Wilcoxon rank-sum test (n = 2 biological replicates).

Figure 4 .

 4 Figure 4. Examples of 3′-UTR non-Alu Staufen-binding sites. (a-d) Read distributions for RNA-seq (green), Stau1-WT CROSS (yellow) and Stau1-WT FOOT (brown) libraries on the 3′ UTRs of LMBR1 (NM_022458.3) (a), TEP1 (NM_007110.4) (b), IGF2BP1 (NM_006546.3) (c) and MDM2 (NM_002392) (d) (left) together with the corresponding centroid secondary structure colored for base-pairing probability as predicted by the Vienna folding package 47 (right). Numbers below the Stau1 WT FOOT track and in the predicted secondary structure correspond to Stau1-binding sites.

Figure 5 .

 5 Figure 5. Stau1 binding to 3′ UTRs correlates with GC content and predicted secondary-structure free energy. (a) Per-gene scatter plot of 3′-UTR predicted secondary-structure free energy normalized by the length of the 3′ UTR (kcal/mol/nucleotide) against Stau1 WT/mut 3′-UTR ratio (log 2 ). (b) Per-gene scatter plot of 3′-UTR GC content (%) against Stau1 WT/mut 3′-UTR ratio (log 2 ) with associated Spearman correlation and calculated P values. Red and yellow dots correspond to called Alu and non-Alu binding sites, respectively (n = 2 biological replicates).

Figure 6 .

 6 Figure 6. Stau1 occupancy on the CDS strongly correlates with GC content and predicted secondarystructure free energy. (a) Composite plot of the distribution of sequencing reads across the 5′ UTR, CDS and 3′ UTR of called Stau1-target genes for RNA-seq (red), Stau1-WT CROSS (blue) and Stau1-mut CROSS (black) ibraries. (b) Per-gene scatter plot (log 10 ) of total CDS Stau1-WT CROSS read counts/tota CDS Stau1-mut CROSS reads counts (CDS ratio) versus the ratio of Stau1-WT CROSS and Stau1-mut CROSS read counts under called 3′-UTR Stau1-WT CROSS peak positions (3′-UTR peak ratio). Red and yellow dots correspond to called Alu-and non-Alu-binding sites, respectively. (c) Per-gene scatter plot of 3′ UTR against CDS predicted secondary-structure free energy normalized by the length of the 3′ UTR (kcal/mol/nucleotide). (d) Per-gene scatter plot of 3′ UTR against CDS GC content (%). (e) Per-gene scatter plot of CDS predicted secondary-structure free energy normalized by the length of the CDS (kcal/mol/nucleotide) against Stau1 WT/mut 3′-UTR ratio (log 2 ). (f) Per-gene scatter plot of CDS GC content (%) against Stau1 WT/mut 3′-UTR ratio (log 2 ). All correlation coefficients and P values were calculated with the Spearman rank correlation (n = 2 biological replicates) throughout figure.

Figure 7 .

 7 Figure 7. Consequences of Stau1 binding on RNA levels and ribosome density. (a) Inset, western blot of lysates from control cells, cells expressing anti-Stau1 shRNA and cells overexpressing Flag-Stau1-WT. Bar graph, quantitation of inset blot (n = 2). Uncropped gel image is shown in Supplementary Figure 7a. (b) Workflow for ribosome profiling and RNA-seq analysis for cells expressing anti-Stau1 shRNA or overexpressing Flag-Stau1-WT. (c) Cumulative plots of cytoplasmic RNA levels (left) and ribosome density (right) fold change (log 2 ) between cells overexpressing Flag-Stau1-WT and cells expressing anti-Stau1 shRNA, based on Stau1 WT/mut CDS ratio. (d) Same as c but based on CDS GC content. (e) Box-plot representation of mRNA levels (left) and translation efficiency (right) fold change (log 2 scale) between cells overexpressing Flag-Stau1-WT and cells expressing anti-Stau1 shRNA for genes lacking Stau1 3′ UTR-binding sites (nontargets), genes with 3′-UTR inverted Alu target sites (Stau1 Alu targets) and genes with 3′-UTR non-Alu Stau1-target sites (Stau1 non-Alu targets). The upper and lower 'hinges' correspond to the first and third quartiles. The upper whisker extends from the hinge to the highest value within 1.5× interquartile range (or distance between the first and third quartiles) of the hinge. The lowest whisker extends from the hinge to the lowest value within 1.5× interquartile range of the hinge. Data

Figure 8 .

 8 Figure 8.

Nat Struct Mol Biol. Author manuscript; available in PMC 2015 October 14.

Acknowledgments

We would like to acknowledge M. Garber, A. Bicknell, A. Noma and J. Braun for comments on the manuscript and the University of Massachusetts Medical School Deep Sequencing Core for technical advice. We thank P.S. Chen for technical help in preparing plasmids and cell lines. We also thank H. Ozadam for technical advice in using RNA structure-prediction tools. Finally, we thank M. Janas, R. Lakshmi and D. Morrissey from Novartis for kindly sharing total RNA from Huh7 and HepG2 cells upon Stau1 and Stau2 knockdown. M.J.M. is supported as a Howard Hughes Medical Institute Investigator.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.


Susan Carpenter

Maninjay Atianand

Daniel Aiello

Emiliano Ricci

Pallavi Gandhi

Lisa L Hall

Meg Byron

Brian Monks

Meabh Henry-Bezy

Luke A J O'neill

Jeanne B Lawrence

Melissa J Moore

Daniel R Caffrey
email: daniel.caffrey@umassmed.edu

Katherine A Fitzgerald
email: kate.fitzgerald@umassmed.edu

A long noncoding RNA

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

hundreds of proteins involved in antimicrobial defense, cell migration, tissue repair, adaptive immunity and resolution of inflammation [START_REF] Medzhitov | Transcriptional control of the inflammatory response[END_REF][START_REF] Murray | Restraint of inflammatory signaling by interdependent strata of negative regulatory pathways[END_REF]. Recent studies have identified thousands of long non-coding RNAs (lncRNAs) in mammalian genomes [START_REF] Guttman | Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[END_REF][START_REF] Rinn | Genome regulation by long noncoding RNAs[END_REF][START_REF] Mortazavi | Mapping and quantifying mammalian transcriptomes by RNA-Seq[END_REF][START_REF] Guttman | Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs[END_REF][START_REF] Guttman | Modular regulatory principles of large non-coding RNAs[END_REF][START_REF] Birney | Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project[END_REF]. lncRNAs have emerged as major regulators of gene expression and are involved in various biological processes that include genomic imprinting, embryonic development, cell differentiation, tumor metastasis and regulation of the cell cycle [reviewed in [START_REF] Rinn | Genome regulation by long noncoding RNAs[END_REF]]. lncRNAs are differentially regulated in virus-infected cells [START_REF] Peng | Unique signatures of long noncoding RNA expression in response to virus infection and altered innate immune signaling[END_REF] and in dendritic cells following lipopolysaccharide (LPS) stimulation [START_REF] Guttman | Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[END_REF]. Very recently, a lncRNA called NeST, (nettoie Salmonella pas Theiler's [cleanup Salmonella not Theiler's]) was shown to control susceptibility to Theiler's virus and Salmonella infection in mice through epigenetic regulation of the interferon-γ locus [START_REF] Collier | Cutting Edge: Influence of Tmevpg1, a Long Intergenic Noncoding RNA, on the Expression of Ifng by Th1 Cells[END_REF][START_REF] Gomez | The NeST long ncRNA controls microbial susceptibility and epigenetic activation of the interferon-gamma locus[END_REF]. Although lncRNAs can be induced in innate immune cells [START_REF] Guttman | Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[END_REF][START_REF] Peng | Unique signatures of long noncoding RNA expression in response to virus infection and altered innate immune signaling[END_REF], it is not yet known whether lncRNAs act as regulators of gene expression in the innate immune response.

To identify lncRNAs that are transcribed during the innate immune response, we conducted whole-transcriptome analysis (RNA-seq) (8) of macrophages stimulated with the synthetic bacterial lipopeptide Pam 3 CSK 4, a TLR2 ligand (Fig. 1A). The TLR2 ligand induced the transcription of numerous protein-coding genes involved in the immune response (Fig. 1A inner track) as well as 62 lncRNAs (Table S1, Fig. 1A outer track). Consistent with previous studies, overall changes in lncRNA expression were less than that observed for protein coding genes. The most significantly induced lncRNAs tended to occur in chromosomal regions where there was also increased expression of immune genes, suggesting that these genes are co-regulated. lincRNA-Cox2 was amongst the most highly induced lncRNAs and is proximal to the prostaglandin-endoperoxide synthase 2 (Ptgs2/Cox2) gene (Fig. 1A). In addition to lincRNA-Cox2, two additional lncRNAs, lncRNA-Ehd1 and lncRNA-Lyn were also induced following TLR2 and TLR4 stimulation (Fig. S1A-B).

A previous study demonstrated that lincRNA-Cox2 was induced in dendritic cells following stimulation with lipopolysaccharide (LPS) [START_REF] Guttman | Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[END_REF]. However, it is not known whether lincRNA-Cox2 regulates the inflammatory response that is associated with TLR signaling. Using PCR amplification, we identified three splice variants of lincRNA-Cox2 (Fig. 1B, accession numbers JX682706, JX682707, JX682708). Variant 1 was the most abundant transcript and contains exons 1 and 4, which are common to all splice variants. Consequently, we designed primers for quantitative PCR (qPCR) and shRNA that targeted these regions. Using qPCR, we confirmed that LPS induced similar temporal patterns of expression of both lincRNA-Cox2 and its neighboring Ptgs2 (Cox2) gene in bone marrow-derived dendritic cells (BMDCs, Fig. 1C-D). Unlike Pam 3 CSK 4 (TLR2), Poly I:C, a synthetic double stranded RNA (TLR3) did not induce lincRNA-Cox2 or Ptgs2 (Cox2) significantly in BMDCs (Fig. 1E-F). LPS, Pam 3 CSK 4 and R848 (which activates TLR7/8) also induced the expression of lincRNA-Cox2 and Ptgs2 (Cox2) in macrophages (Fig. 1G-H). Both Listeria-infected macrophages (in vitro) and isolated splenocytes from Listeria-infected mice (24 hr post infection) had elevated levels of lincRNA-Cox2 (Fig. S1C-D). Induction of lincRNA-Cox2 and its neighboring gene Ptgs2 (Cox2) was dependent on the TLR signaling adaptor protein MyD88 (Fig. I-J) and on activation of the transcription factor NFκB (Fig. 1K-L).

We next examined the protein-coding capacity of lincRNA-Cox2 by assessing its association with polysomes within cells. Macrophage cells were treated with cycloheximide to trap ribosomes on RNA molecules and either left untreated or pretreated with EDTA (which disrupts all RNA-protein interactions) or with harringtonine (which specifically disrupts translation). Cell lysates were then fractionated by sucrose density gradients and ultracentrifugation and RNA analysed in all fractions by qPCR [START_REF] Ingolia | The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments[END_REF][START_REF] Ingolia | Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling[END_REF][START_REF] Ingolia | Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes[END_REF]. Using this approach we compared Gapdh, a well-translated mRNA with lincRNA-Cox2 and another lincRNA, lncRNA-Eps, that has previously been shown to be non-coding [START_REF] Hu | Long noncoding RNA-mediated anti-apoptotic activity in murine erythroid terminal differentiation[END_REF]. As expected Gapdh RNA sedimented with a high velocity through the sucrose gradient since it was associated with ribosomes. In contrast, lincRNA-Cox2 and lincRNA-Eps both remained in lighter fractions (Fig. S2). Treatment with EDTA or specific disruption of translation by harringtonine resulted in a shift of Gapdh from the higher velocity to the lower velocity fractions indicating that this mRNA was actively translated. In contrast, the profile of lincRNA-Cox2 or lincRNA-Eps were largely unaffected by harringtonine treatment.

We also examined lincRNA-Cox2 for the presence of potential open reading frames (ORFs). Most of the ORFs identified were found to have poor Kozak strength suggesting that it is unlikely that this RNA sequence is translated, (Fig. S3A-B). Collectively, these studies indicate that lincRNA-Cox2 is unlikely to encode a protein product.

To define the functional role of lincRNA-Cox2 in inflammatory gene expression, we generated macrophage cell lines in which lincRNA-Cox2 expression was silenced by shRNA (Fig. 2A). Silencing of lincRNA-Cox2 did not alter expression of its neighboring gene Ptgs2 (Cox2) (Fig. S4). To identify potential targets of lincRNA-Cox2, we conducted RNA-seq in both control and lincRNA-Cox2 silenced cells before and after stimulation with Pam 3 CSK 4 . Silencing of lincRNA-Cox2 increased the expression of 787 genes by 3-fold or greater in unstimulated cells (Table S2). A GO enrichment analysis indicated that genes related to the immune response were significantly overrepresented in these up-regulated genes (Table S3, Fig. 2B). A number of chemokines (Ccl5, Cx3cl1), chemokine receptors (Ccrl) and interferon-stimulated genes (ISGs) (Irf7, Oas1a, Oas1l, Oas2, Ifi204 and Isg15) were upregulated when lincRNA-Cox2 was silenced in unstimulated cells (Fig. 2B). In the same cells stimulated with Pam 3 CSK 4 , silencing of lincRNA-Cox2 resulted in attenuated expression of 713 genes by 3-fold or greater (Table S4). Examples of these genes include Tlr1, Il6 and Il23a.

We confirmed these findings using a non-enzymatic RNA profiling technology that employs bar-coded fluorescent probes to simultaneously analyze mRNA expression levels of differentially regulated genes (nCounter, Nanostring). In unstimulated cells, silencing of lincRNA-Cox2 led to a marked increase in expression of Irf7, Ccl5 (Rantes), and ISGs (Ifi204, Ifi205 and Viperin) relative to control cells (Fig. 2C, columns 1-2), while the Pam 3 CSK 4-induced expression of Tlr1 and Il6 was attenuated (Fig. 2C, columns 3-4). We confirmed these findings in three independent shRNA lines by measuring protein levels for Ccl5 (Rantes) and IL6 (Fig. 2D andE, Fig. S5). In contrast to these genes, the TLR2 dependent induction of IL1β was unaffected in cells lacking lincRNA-Cox2 (Fig. S6). We also observed reduced IL6 production in macrophages stimulated with Pam 2 CSK 4 and R848, ligands of TLR2/6 and TLR7, respectively (Fig. S7). We next silenced lincRNA-Cox2 in macrophages lacking the type I interferon α/β receptor (IFN α/βR KO) in order to distinguish between direct and indirect (IFN-dependent) targets of lincRNA-Cox2 (Fig. 2F-I, Fig. S8). Non-enzymatic RNA profiling of gene expression in these cells indicated that while Ccl5 was regulated by lincRNA-Cox2 in a manner that was independent of type IFN signaling, regulation of Irf7 and Ifi204 was secondary to type I IFN signaling. Despite the elevated expression of IFN pathway components, silencing of lincRNA-Cox2 did not render these cells permissive to TLR2 induced TBK1 activation, a measure of the Irf3 signaling pathway (Fig. S9). Finally, since shRNA silencing of lincRNA-Cox2 led to decreased expression of Tlr1, it was important to eliminate the possibility that we inadvertently impaired Pam 3 CSK 4 signaling via the TLR1/TLR2 heterodimer. To test this directly, we restored expression of mTLR1 in lincRNA-Cox2 silenced cells and confirmed that the differential regulation of Ccl5 and IL6 expression was not due to impaired expression of TLR1. Restoration of TLR1 did not alter the effects of lincRNA-Cox2 silencing on Ccl5 or IL6 protein levels (Fig. S10). Taken together, these data indicate that lincRNA-Cox2 regulates distinct classes of immune genes both basally and following TLR-stimulation.

We next conducted 'gain-of-function' studies by generating macrophages that ectopically expressed lincRNA-Cox2 (Fig. 3A). As expected, over-expression of lincRNA-Cox2 mirrored the effects of silencing the lncRNA. Macrophages that ectopically expressed lincRNA-Cox2 had decreased levels of Ccl5, Irf5, Irf7, Stat1, Viperin, and three members of the PYHIN protein family (Ifi202, Ifi204, Mnda) (Fig. 3B). Il6 was not detected when lincRNA-Cox2 was over-expressed in unstimulated cells. However, Il6 levels were significantly enhanced when lincRNA-Cox2 was over-expressed in Pam 3 CSK 4 stimulated cells (Fig. 3C). These results further demonstrate that lincRNA-Cox2 represses Ccl5, while simultaneously enhancing the expression of TLR-induced Il6.

lncRNAs can be found in the nucleus, cytoplasm or in both compartments [START_REF] Cheng | Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution[END_REF][START_REF] Wu | Poly A-transcripts expressed in HeLa cells[END_REF][START_REF] Louro | Long intronic noncoding RNA transcription: expression noise or expression choice?[END_REF]. We therefore examined the localization of lincRNA-Cox2 in macrophages using RNA fluorescence in situ hybridization (FISH), a methodology that allows simultaneous detection and localization of RNA transcripts in relation to cellular substructures. Significant amounts of lincRNA-Cox2 were clearly visible in both the nuclear and the cytosolic compartments of macrophages (Fig. 4A, Fig. S11). Analysis of RNA-seq data for different subcellular fractions of macrophages [START_REF] Bhatt | Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions[END_REF], confirmed that lincRNA-Cox2 was present in both the cytosol and nucleus (Fig. S12). Importantly RNA-FISH analysis demonstrated efficient targeting of lincRNA-Cox2 in the nuclear fractions of our shRNA silenced cells (Fig. S11C-F).

Many lncRNAs regulate transcription through their interactions with chromatin-modifying complexes or with heterogeneous nuclear ribonucleoproteins (hnRNP) [START_REF] Rinn | Genome regulation by long noncoding RNAs[END_REF][START_REF] Khalil | Many human large intergenic noncoding RNAs associate with chromatinmodifying complexes and affect gene expression[END_REF][START_REF] Huarte | A large intergenic noncoding RNA induced by p53 mediates global gene repression in the p53 response[END_REF][START_REF] Hasegawa | The matrix protein hnRNP U is required for chromosomal localization of Xist RNA[END_REF]. To identify binding partners for lincRNA-Cox2, we incubated in vitro-transcribed biotinylated lincRNA-Cox2 as well as an antisense lincRNA-Cox2 control RNA with nuclear or cytosolic extracts and subjected RNA binding proteins to Mass Spectrometry for identification. hnRNP-A/B and hnRNP-A2/B1 were identified as specific binding partners for lincRNA-Cox2 in both the cytosolic and nuclear fractions (data not shown). The ability of hnRNP-A/B and hnRNP-A2/B1 to bind lincRNA-Cox2 was confirmed by western blot analysis (Fig. 4B andC). hnRNPs are involved in several RNA-related biological processes such as transcription, pre-mRNA processing, mature mRNA transport to the cytoplasm, and translation [START_REF] Dreyfuss | From transcript to protein[END_REF][START_REF] Krecic | hnRNP complexes: composition, structure, and function[END_REF]. In addition, hnRNPs are known to form complexes with lncRNAs and are emerging as important mediators of lncRNA-induced transcriptional repression [START_REF] Huarte | A large intergenic noncoding RNA induced by p53 mediates global gene repression in the p53 response[END_REF][START_REF] Hasegawa | The matrix protein hnRNP U is required for chromosomal localization of Xist RNA[END_REF]. hnRNP-A/B has been linked to transcriptional repression of some genes (e.g. OPN and Sma-A) [START_REF] Gao | S-nitrosylation of heterogeneous nuclear ribonucleoprotein A/B regulates osteopontin transcription in endotoxin-stimulated murine macrophages[END_REF][START_REF] Gao | Transcriptional regulatory functions of heterogeneous nuclear ribonucleoprotein-U and -A/B in endotoxin-mediated macrophage expression of osteopontin[END_REF] and hnRNPA2/B1 associates with hnRNP-A/B [START_REF] Raju | In cultured oligodendrocytes the A/B-type hnRNP CBF-A accompanies MBP mRNA bound to mRNA trafficking sequences[END_REF]. Therefore, we hypothesized that lincRNA-Cox2 regulates transcription of immune genes by forming complexes with these hnRNP proteins. To test this hypothesis, we generated macrophages where expression of hnRNP-A/B and hnRNP-A2/B1 were silenced by shRNA (Fig. S13A-B). Knockdown of hnRNP-A/B and hnRNP-A2/B1 did not modulate the levels of lincRNA-Cox2 expression (Fig. S14), indicating that these hnRNPs did not regulate lincRNA-Cox2 levels. Similar to our lincRNA-Cox2 silencing studies (Fig. 2D), we detected a significant enhancement of Ccl5 protein in TLR2-stimulated cells and unstimulated cells that lacked hnRNP-A/B and hnRNP-A2/B1 (Fig. 4D). Multiplex RNA analysis also revealed elevated levels of Ccl5, Stat1, Tlr7, Icam1 and IκB RNA in hnRNP-silenced macrophages (Fig. 4E). There was considerable overlap between genes that were regulated by lincRNA-Cox2 and these two hnRNP proteins (Fig. S15, Table. S5). We also examined RNA polymerase II recruitment to the promoter of the Ccl5 and Irf7 promoters using RNA polymerase II Chromatin immunoprecipitation (ChIP). As expected, recruitment of RNA Pol II to the promoters of Ccl5 and Irf7 was increased when lincRNA-Cox2 or the hnRNPs were silenced (Fig. 4F-G, Fig. S16), indicating that lincRNA-Cox2, hnRNPA2/B1 and A/B negatively regulate transcription of these genes. In contrast to these genes, RNA Pol II displayed decreased recruitment to the promoter of Il6 only when lincRNA-Cox2 was knocked down (Fig. S16), an effect that was consistent with the hnRNPs having no effect on Il6 mRNA levels (Fig. 4E). Silencing of hnRNP-A2/B1 or hnRNP-A/B in cells that over-expressed lincRNA-Cox2 reversed the inhibitory effect of lincRNA-Cox2 on Ccl5 expression (Fig. 4H, Fig. S17). Taken together, these experiments confirm that hnRNP-A/B and A2/B1 form a complex with lincRNA-Cox2 in order to repress the transcription of immune genes.

In summary, these studies identify lincRNA-Cox2 as a critical component of the inflammatory response. The impact of this lncRNA on the TLR response was both dramatic and broad-acting with unprecedented effects not previously seen with other regulatory RNAs (e.g. microRNAs). The identification of hnRNP-A/B and A2/B1 as mediators of lincRNA-Cox2's transcriptional repressive functions underscore the importance of hnRNPs in lncRNA function. Recently, lincRNA-p21, identified as a repressor in the p53-dependent transcriptional response, was shown to associate with hnRNP-K which facilitates the proper genomic localization of hnRNP-K at repressed genes to modulate p53 dependent apoptosis [START_REF] Khalil | Many human large intergenic noncoding RNAs associate with chromatinmodifying complexes and affect gene expression[END_REF][START_REF] Huarte | A large intergenic noncoding RNA induced by p53 mediates global gene repression in the p53 response[END_REF][START_REF] Hasegawa | The matrix protein hnRNP U is required for chromosomal localization of Xist RNA[END_REF].

Innate immune responses have the capacity to both combat infectious microbes and drive pathological inflammation, which contributes to diseases such as atherosclerosis, autoimmunity and cancer. A multitude of regulatory checkpoints control TLR signaling and inflammatory responses. We propose a model whereby TLR signaling induces lncRNAs, such as lincRNA-Cox2, that serve as repressors and activators of genes through their physical interactions with various regulatory complexes. As such, lncRNAs represent a novel component of the innate immune response that can restrain and promote aspects of inflammatory signaling. Further characterization of these regulatory networks is likely to

Figure 1 .

 1 Figure 1. lincRNA-Cox2 expression is induced by TLR ligands in a MyD88 and NFκB dependent manner A, The Circos plot shows genome-wide differential expression (RNA-seq) between untreated bone marrow derived macrophages (BMDM) and BMDMs stimulated with Pam 3 CSK 4 (TLR1/2) (5 h). The inner track shows log 2 fold-change values for protein coding genes that are classified into immune genes (red, see methods) and other genes (blue). The outer track shows log 2 fold-change value for all lncRNAs. lincRNA-Cox2 is highlighted in red on Chromosome 1 (arrow). B, lincRNA-Cox2 encodes three splice variants. C-H, qRT-PCR was performed on bone marrow derived dendritic cells (BMDC) (C-F) or bone marrow derived macrophages (BMDM). Elevated levels of lincRNA-Cox2 and Ptgs2 were observed following LPS (TLR4) (C-D), Pam 3 CSK 4 (TLR2) (E-H), R848 (TLR7/8) (G-H) but not with Poly I:C (TLR3) (E-H) stimulation. I-J. Induction of lincRNA-Cox2 and Ptgs2 were found to be dependent on MyD88 following qRT-PCR on BMDMs obtained from wild type (WT) or MyD88 KO mice. K-L, BMDMs treated for 30min with an NFκB inhibitor (1 μg/ml), followed by stimulation with LPS (100 ng/ml) resulted in reduced expression levels of lincRNA-Cox2 (K) and Ptgs2 (L) as examined by qRT-PCR. Data represents mean ±SD from three independent experiments.

Figure 2 .

 2 Figure 2. lincRNA-Cox2 is a major regulator of immune genes A, qRT-PCR was carried out on BMDMs stably expressing lentiviral shRNA specific to lincRNA-Cox2 (shRNA) or a control shRNA. Expression of lincRNA-Cox2 was measured. B, RNA-seq was performed on lincRNA-Cox2 knockdown or control (ctl shRNA) BMDMs that were either stimulated with Pam 3 CSK 4 or unstimulated. The heat map shows mRNA levels for genes annotated in GO as immune genes. These genes are among the top 50 upregulated immune genes in unstimulated cells when lincRNA-Cox2 was silenced or the top 50 down-regulated immune genes in stimulated cells when lincRNA-Cox2 was silenced. The 100 genes were ranked by absolute log 2 fold-change values and the top 80 differentially expressed genes displayed. C, Heatmap representation of differentially regulated genes from a custom designed gene codeset performed on RNA extracted from Ctl or lincRNA-Cox2 knockdown cells stimulated with Pam 3 CSK 4 for 5 h. D-E, Cells were stimulated with Pam 3 CSK 4 for 24 h, increased Ccl5 (Rantes) (D) and reduced IL6 (E) cytokine levels were identified in lincRNA-Cox2 knockdown cells by elisa (n.d means not detected). F-I, lincRNA-Cox2 was silenced in interferon α/β receptor (IFN α/β R) KO cells. Expression levels of lincRNA-Cox2 (F), Ccl5 (Rantes) (G), Irf7 (H) and Ifi204 (I) were measured using qRT-PCR to define direct targets of lincRNA-Cox2.

Figure 3 .

 3 Figure 3. Differential gene expression following over expression of lincRNA-Cox2A, BMDM stably over-expressing lincRNA-Cox2 or a Ctl vector were generated. qRT-PCR was carried out and over-expression of lincRNA-Cox2 was confirmed. B, Heatmap representation of differentially regulated genes from a custom designed gene codeset performed on RNA extracted from Ctl or lincRNA-Cox2 over expressing BMDMs. C, qRT-PCR was carried out on Ctl or lincRNA-Cox2 over expressing cells stimulated with Pam 3 CSK 4 for 5 h, increased Il6 expression levels were identified using qRT-PCR. Data represents mean ±SD from three independent experiments.

Figure 4 .

 4 Figure 4. lincRNA-Cox2 is localized to both the cytosolic and nuclear compartments, interacts with hnRNP-A/B and A2/B1 to regulate immune genes A, BMDMs were labeled with a lincRNA-Cox2 probe using RNA FISH, and counterstained with DAPI (DNA). B-C, Biotinylated lincRNA-Cox2 or antisense RNA was incubated with nuclear extracts and interaction with endogenous hnRNP-A/B (B) or hnRNP-A2/B1 (C) assessed following IP/western (top panels). Expression levels of hnRNP-A/B (B) or hnRNP-A2/B1 (C) (lower panels) in input lysates were also examined. D, Cell lines with shRNA targeting hnRNP-A/B or hnRNP-A2/B1 were stimulated with Pam 3 CSK 4 , elevated Ccl5 (Rantes) production was identified in these cells by elisa. E, Heatmap representation of differentially regulated genes of Ctl, hnRNP-A/B or hnRNP-A2/B1 expressing BMDMs stimulated with Pam 3 CSK 4 (100nM) for 5 h. F-G, Silencing of lincRNA-Cox2, hnRNP-A/B or hnRNP-A2/B1 promotes recruitment of RNA Pol II to the Ccl5 promoter as determined by CHIP analysis. H, hnRNP-A/B or hnRNP-A2/B1 was knocked down using lentiviral shRNA in lincRNA-Cox2 over-expressing BMDMs, Ccl5 expression levels were measured using qRT-PCR.

Science.Author manuscript; available in PMC 2015March 27.

Acknowledgments

This work is supported by a HRB/Marie Curie Fellowship (MCPD/2010/04 to S.C) and by NIH grants (AI067497 to K.A.F). The authors would like to thank Adam Wespiser for generating heatmaps, Nirmal Singh for his helpful advise on carrying out RNA-seq and Lynda Stuart, Neal Silverman, Douglas Golenbock and Egil Lien for critical reading of this manuscript.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.