The majority of mammalian genes contain one or more alternative polyadenylation sites. Choice of polyadenylation sites was suggested as one of the underlying mechanisms for generating longer/shorter transcript isoforms. Here, we demonstrate that mature mRNA transcripts can undergo additional cleavage and polyadenylation at a proximal internal site in the 3′-UTR, resulting in two stable, autonomous, RNA fragments: a coding sequence with a shorter 3′-UTR (body) and an uncapped 3′-UTR sequence downstream of the cleavage point (tail). Analyses of the human transcriptome has revealed thousands of such cleavage positions, suggesting a widespread post-transcriptional phenomenon producing thousands of stable 3′-UTR RNA tails that exist alongside their transcripts of origin. By analyzing the impact of microRNAs, we observed a significantly stronger effect for microRNA regulation at the body compared to the tail fragments. Our findings open a variety of future research prospects and call for a new perspective on 3′-UTR-dependent gene regulation. Most mammalian genes contain alternative polyadenylation sites. Here, the authors provide evidence that mRNA can be cleaved post-transcriptionally to generate mRNAs with shorter 3-´UTRs and stable autonomous uncapped 3´-UTR sequences. Accumulation of transcripts with short 3′-UTRs is widespread across tissues and is positively correlated with cell proliferation7. However, recent studies have demonstrated that the choice of APA sites has minimal effects on stability and translational efficiency9. Thus, the functional significance of this regulation has remained poorly understood.3′-UTRs serve as major docking platforms for regulatory proteins and RNAs, including microRNAs (miRNAs). Hence, it was initially assumed that APA increases the stability of the mRNAs and thus increases protein output10. The presence of a capped 3′-UTR transcript separately from its associated CDS has also been documented by Mercer et al.11 for some genes in humans, mice, and flies. Despite the potential high impact of these results on the current understanding of the transcriptome, no large-scale study of this phenomenon has been yet undertaken.Recent results showed that mRNAs from defined neuron populations exhibit widespread unbalanced expression of cognate 3′-UTRs relative to respective upstream coding sequences (CDSs)In this study, we show that thousands of mature poly(A) mRNA transcripts could often be cleaved post-transcriptionally into two fragments: a “body” unit found upstream of the cleavage site and a downstream uncapped “tail” unit. By re-annotating the human transcriptome for novel cleavage positions, we show stronger regulatory effects by miRNA sites in the body fragment compared to 3′-UTR sites downstream of the cleavage point.7. Manual examination of RNA-seq data12 revealed many genes with a distinctive gap in their read coverage along the 3′-UTR, adjacent to known APA sites13  data from naive mouse T cells, where 3′-UTR shortening is widespread and associated with the transition of the cells from the naive to activated state 15 Fig. . These gSsr1, Bcl2, and Rab2a genes. Initially, total RNA extraction showed no substantial or minor change of expression between the three amplicons  to compare the relative expression levels of the regions upstream, downstream, and across the gap of p > 0.01). To further substantiate these results we performed Northern blot analysis on Actb transcript  using probes for either the gene body or the tail  for tail fragments of six selected genes that presented a substantial gap in APA site according to published RNA-seq data  selected RNA (control), and calculated the average read coverage across the putative cleavage points predicted by our computational model based on TEX-treated RNA. The predicted cleavage points were characterized by well-positioned troughs of low read coverage compared to the surrounding regions Fig. , suggestBased on our initial observations that cleavage sites are associated with APA sites, we postulated that the processing mechanism is APA-dependent.13. Our results, as seen in Fig. p < 2e−7).To test this hypothesis, we compared the cleavage points as inferred by the HMM model following TEX treatment in HEK-293 cells to the polyadenylation sites documented in HEK-293 cells in the APA repository PolyASiteST6GALNAC4 and RAP1B). 3′-UTR of wild-type (WT) and mutated PAS were cloned into pGL3 luciferase reporter constructs. qRT-PCR analysis of cytoplasmic RNA fraction treated with TEX showed significant reduction in the WT tail fragments but not in the tail fragments of the mutated PAS  motifs in the 3′-UTRs of two genes  compared to the proximal peak (TEX-insensitive body). Indeed, we observed a dramatic decrease in the relative height of the distal peak  following TEX treatment  in the distal peak height compared to the proximal peak following TEX treatment in α-amanitin-treated cells  with the nearest 3′-end RNA-seq peak (APA site) in the same cell type (U2OS). For 80% of the cleaved transcripts, the predicted cleavage site appeared within 100 nt from the most proximal 3′-end RNA-seq peak Fig. . These rp ≤ 1.2e−11, Fig. p ≤ .2.8e−10, Supplementary Figs. To corroborate that the cleavage generates two independent transcripts, we tested whether the transcript tail and body were affected differently by miRNAs. To this end, we conducted transfection experiments of two miRNAs, hsa-miR-92a-3p and hsa-let-7a-5p, in HEK-293 cells Fig. . The twop ≤ 9.4e−7, see Fig. p ≤ 2.2e−16, and Supplementary Fig. Interestingly, the conventional analysis of the entire transcripts showed a smaller change in expression than that of the transcript body  between the CDS and the 3′-UTR was significantly smaller than the difference between the body and tail (p ≤ 7.9e−26 in transcripts with sites in either body/CDS or in tail/3′-UTR). The same was demonstrated for let-7a transfection  mRNAs, located mainly in the cytoplasm, are cleaved and polyadenylated post-transcriptionally at APA sites in their 3′-UTRs, and regions that were considered to be a part of the canonical transcripts are in fact autonomous uncapped “tail” units. Most likely, this widespread phenomenon, which occurs in thousands of genes, was overlooked due to the stable nature of the tails that makes them hard to detect using next-generation RNA-seq data. To overcome this difficulty, we systematically enriched for body or tail units based on the presence of 5′-cap modifications, followed by deep RNA-seq.By analyzing these data we re-annotated the human transcriptome for novel cell type-specific cleavage positions, and identified thousands of 3′-UTR RNA tails that exist alongside their transcripts of origin. We further established our results by explicitly mapping transcript ends using 3′-end RNA-seq, and related the cleavage locations to known APA sites. These results indicate that in addition to termination and polyadenylation at the canonical polyadenylation site, additional cleavage and polyadenylation occurs within many transcripts. Treating these cells with the transcription inhibitor α-amanitin resulted in the accumulation of both “body” and “tail” units, suggesting that this 3′-UTR cleavage is transcriptionally independent and involves the processing of mature mRNA.16 who described that APA isoforms with shorter 3′-UTRs are more frequent in the cytoplasm. In addition, although it is accepted that cleavage and polyadenylation occurs co-transcriptionally, our study as well as others18 demonstrates that cleavage and polyadenylation at APA sites may occur post-transcriptionally as well. These two observations could be explained by mRNA retention in the nucleus followed by additional cleavage and export to the cytoplasm. Indeed, cleavage and polyadenylation in the nucleus are presented in Prasanth et al.17, describing that some transcripts (such as CTN-RNA) are retained in the nucleus and are cleaved post-transcriptionally upon stress conditions to produce new mRNA transcripts with shorter 3′-UTRs. In addition, Jenal et al.18 synthesized RNA fragments with WT or mutated PAS proximal APA sites and then incubated with nuclear extract of HeLa cells. In this in vitro assay, cleavage occurred at RNA fragments containing WT APA but not in RNA fragments containing mutated PAS18. Additionally, our observations could be also explained by cleavage and polyadenylation at APA sites in the cytoplasm. However, to date, there is no evidence of such mechanisms.Our data suggest that cleaved RNAs are present in the cytoplasm more frequently than in the nucleus. These findings are supported by Furger and colleagues19.While the observation and characterization of thousands of uncapped tail fragments raise an intriguing question of their biological function and importance, we cannot overrule the possibility that some of these fragments are byproduct results from APA usage due to a fail-safe mechanism ensuring full transcription and correct splicing are obtained to produce mature 3′ ends20. Therefore, 3′-UTR shortening could be used as a global regulatory tool to increase the effectiveness of binding sites upstream of the cleavage point by shortening their distance from the transcript 3′ end. Indeed, repression by miRNAs was more effective in transcripts with shortened 3′-UTRs compared to those with longer 3′-UTRs21.miRNAs are known to regulate mRNA transcripts by binding primarily to the 3′-UTRs. Hence, we assessed the impact of our findings by evaluating the effect of the transfection of a given miRNA on its targets by evaluating the expression of the “body” and “tail” fragments with binding sites independently rather than the entire transcript, as has been done in conventional analyses. Our analysis demonstrated that following miRNA overexpression, the entire transcript shows a significantly smaller change in expression compared to the transcript body. We find this to be especially significant, as in many cases transcripts that were not found to be down-regulated significantly by the “conventional” analysis of the full transcript were found to be down-regulated when only the body was considered. Previous studies demonstrated that miRNA binding sites near the ends of transcripts are more effectiveOur study provides a mechanistic explanation for these findings, as most likely the binding site of the miRNA only affected the body and the tail was not affected by the miRNA regulation. Moreover, our findings have a high impact on previous miRNA expression analyses, as most miRNA studies evaluated their regulatory effect using cDNA microarrays, which were usually designed to hybridize to the 3′-UTR ends of transcripts. Our data indicate that such an analysis could be systematically biased and might overlook the actual decrease in “body” mRNA expression. We believe that our results will similarly impact additional fields, e.g., studies related to RNA binding proteins.Overall, our study sheds new light on the transcriptome and calls for a new perspective on the post-transcriptional regulation of genes through their 3′-UTRs. Furthermore, the discovery of thousands of processed 3′-UTR RNAs uncovers a significant class of RNA with diverse potential biological properties.C57BL6/J mice were maintained and bred under specific pathogen-free conditions in the Hebrew University animal facilities. All mouse studies were performed under protocol MD-16-14863 approved by the Hebrew University Institutional Animal Care and Use Committee. All mice were used for experiments between ∼8 and 12 weeks.T cells were isolated from C57BL/6J (WT) mice spleens with an EasySep™ Mouse CD90.2 Isolation Kit (#18951) according to the manufacturer’s instructions (STEMCELL Technologies).Two million isolated T cells were subjected to Nuclear and cytoplasmic RNA extraction using the Cytoplasmic and Nuclear RNA Purification Kit  following additional step of an on-column DNA removal using DNase I (NEB-M0303S).cDNA was synthesized using ProtoScript First Strand cDNA Synthesis Kit with oligo-dT primers . qRT-PCR was then performed using QuantStudio 12K Flex Real Time PCR system with a Power SYBR green PCR Master Mix Kit (Applied Biosystems).50 °C 2 min, 1 cycle.95 °C 10 min, 1 cycle.95 °C 15 s -> 60 °C 1 min, 40 cycles.95 °C 15 s, 1 cycle.60 °C 1 min, 1 cycle.95 °C 15 s, 1 cycle.Reaction was performed as follows:t-test was used with 95% confidence interval.Data were normalized to Human/Mouse endogenous control (UBC) and analyzed using the ΔΔCt model unless otherwise indicated. Each experiment was performed in triplicates and was repeated three times. Student’s Lists of all primers used in these experiments are presented in Supplementary Tables ® RACE 5′/3′ Kit  according to the manufacturer’s protocol. Then, PCR was preformed to RACE-Ready cDNA using the kit forward universal Primer and reverse Gene-Specific Primers listed in the primers list. PCR products were purified from agarose gel using NucleoSpin Gel and PCR Clean-up Kit  and were sequenced using ThermoFisher 96-capillary 3730xl DNA Analyzer. See Supplementary Table Total RNA was extracted with QIAzol Reagent . One microgram of total RNA was subjected to SMARTer32P]αATP. See Supplementary Table PolyA+RNA samples (0.5 µg) were denatured for 10 min at 65 °C in RNA loading buffer containing 40% formamide and 1.3 M formaldehyde, and then separated on 1.6% agarose gel containing 1.17 M formaldehyde in 20 mM MOPS, 2 mM Na-Citrate and 1 mM EDTA (pH 8.0). RNA was transferred to Zeta-Probe membrane (Bio-Rad) by capillary transfer in 10× SSC (1.5 M NaCl and 150 mM Na-citrate). For Actb probing specific riboprobes were used. For synthesis of the riboprobes a template containing the T7 promoter upstream to Actb antisense were PCR-amplified using oligonucleotides presented in the “primers list”. In vitro RNA transcription of the riboprobe was done using 600 ng of the PCR template, 0.5 mM of each CTP, GTP, and UTP, 20 µM ATP, and 30 µCi [RNA was extracted from cells using QIAzol Reagent  and treated with DNase I (NEB-M0303S). Five micrograms of RNA for each sample was processed with the NEBNext Poly(A) mRNA Magnetic Isolation Module  and further processed with the NEBNext Ultra Directional RNA Library Prep Kit  or 3′–3′-mRNA-Seq Library Prep Kit .For preclearing, 4 µl mouse IgG1 anti-Flag  was added to 40 µl Protein G Agarose beads  suspended in 0.5 ml PBS for 1.5 h in tumbling 4 °C. After two washes with PBS, the anti-Flag-coated beads were added to the poly(A) selected RNA extract in 0.5 ml binding buffer ) and incubated for 1.5 h in 4 °C. Precleared RNA samples were then subjected to 5′-CAP RNA immunoprecipitation using 5 µl (1:100 dilution) anti-Cap antibody  and incubated O/N in 4 °C. Then, RNA immune complexes were incubated with 40 µl of Protein G Agarose beads for 4 h in RT following four washing steps using wash buffer . Beads were then re-suspended with 100 µl elution buffer (wash buffer containing 3 µl proteinase K NEB-P8107S and 0.1% SDS) and incubated for 15 min at 65 °C. The elution step was repeated one more time. Finally, 800 µl QIAzol was added to the 200 µl eluted RNA.Three replicates were used for this experiment.RNA was poly(A) selected using NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB-E7490S). The elution step was skipped at this point and instead poly(T) beads/RNA complexes were washed with 50 mM Tris-HCl in DEPC water and then subjected to “on beads” capping using biotinylated GTP with the Vaccinia Capping System according to the manufacturer’s instructions ). Beads were then washed twice with the NEBNext Poly(A) mRNA Magnetic Isolation Module and proceeded with the RNA elution protocol. After elution, labeled RNA with biotinylated GTP was pulled down using Dynabeads MyOne Streptavidin C1  according to the manufacturer’s instructions.Three replicates were used for this experiment.For RNA-seq analysis DNase I-treated, poly(A) selected RNA was treated with Terminator 5′-Phosphate-Dependent Exonuclease  according to the manufacturer’s instructions. Reaction was deactivated and RNA was purified using RNA Clean-Up and Conc MICRO-Elute Kit .Three replicates were used for this experiment.ST6GALNAC4 or RAP1B (WT/mutant) into the XbaI site located at 3′-UTR of pGL3-Control vector.pGL3 luciferase reporter constructs were created by cloning of 3′-UTR sequences of See Supplementary Table U2OS, HEK-293, and HEK-293T cell lines were obtained from ATCC and grown in Dulbecco’s modified Eagle’s medium, supplemented with 10% fetal calf serum, 100 units/ml penicillin, and 100 μg/ml streptomycin at 37 °C.For transfection, HEK-293 cells were plated on 35 mm plates in 40% confluency and grown for 16 h.Transfection of pGL3 luciferase reporter vector was performed using TransIT-LT1 transfection reagent according to thr manufacturer’s instructions. Cells were collected for analysis 24 h after transfection.Transfection of miRNA, synthetic miR-92a-3p, let-7, or negative control dsRNA was performed using TransIT-X2TM Dynamic Delivery System according to the manufacturer’s instructions. Cells were collected for analysis 40 h after transfection. Three replicates were used for this experiment.To inhibit RNA polymerase II transcription activity, U2Os cells were incubated with α-amanitin  for 6 or 9 h at 37 °C.All cell lines were tested for mycoplasma contamination using EZ-PCR Mycoplasma Test Kit .Trimming and filtering of raw reads: NextSeq basecall files were converted to FASTQ files using the bcl2fastq (v.2.15.0.4) program with default parameters.QC preprocessing: Raw reads were inspected for quality issues with FastQC (v.0.11.2), and were quality trimmed at both ends to a quality threshold of 32. Adapter sequences were then removed using cutadapt (version 1.7.1), through the Trim Galore! interface (version 0.3.7), leaving only reads of length above 15 nt. The remaining reads were further filtered to remove very low quality reads, using the fastq_quality_filter , with a quality threshold of 20 at 90% or more of the read positions.Genomic mapping of RNA-seq data: The processed FASTQ files were mapped 22 to the human genome and transcriptome (hg19). Reads that after processing were left as a pair, as well as reads for which only one of the pair mates remained, were used for further analyses. Mapping allowed up to 2 mismatches per read, a maximum gap of 5 bases, and a total edit distance of 7.Trimming and filtering of raw reads: NextSeq basecall files were converted to FASTQ files using bcl2fastq (v.2.17.1.14). Reads were screened and preprocessed similarly to the described above, an additional step of removing polyA sequences from the 3′ ends of reads was performed with cutadapt, using a 75-mer oligo-A sequence as the “adapter” and a minimal overlap of 2.Mapping and peak calling: The processed FASTQ files were then mapped to the human transcriptome and genome using TopHat (v.2.0.13)22, allowing up to 3 mismatches per read, a maximum gap of 5 bases, and a total edit distance of 8. Peaks were called using mapped reads from all samples, using findPeaks 23, with “-region -size 200 -minDist 250 -strand” parameters. Peaks found within transcripts or up to 5 kb downstream of promoters were included in a GTF file and processed by the DEXSeq package.Analysis was performed separately for each transcript/treatment . First, the average read coverage for both treatment and control were calculated for overlapping 50 bp windows (in 20 bp offsets) along the coding regions and 3′-UTR (excluding introns). Coverage values were then transformed to log scale.Y = aX + b for each treatment, where Y is the log-transformed set of coverages along the transcript for every treatment, and X is the log-transformed coverages for control. This allowed us to overcome experiment-specific bias and infer the offset and ratio between mean read coverage (per window) in every treated condition and control. We then applied the linear model to yield the expected read coverage for each transcript along both coding and 3′-UTR windows, and deviations from the regression model were calculated.Next, we trained a linear regression model to correct for different transformations of the results in control and various treatments. For this, we considered all windows that overlap coding regions , and fitted a linear regression model 25 with two internal states corresponding to “Body” and “Tail” regions, allowing a single transition from the “Body” to the “Tail” state. Each state was characterized with a Gaussian emission of continuous variables (with shared variance). The parameters of the model were optimized using the Baum–Welch (Expectation–Maximization) algorithm, using maximum likelihood estimations. Finally, the most probable Body/Tail cleavage point was identified for every transcript/treatment as the maximum likelihood transition point between the Body and Tail states, using the Viterbi algorithm.Next, we trained an HMM26 with default parameters for the differential gene expression analysis, comparing the data between miRNA and control transfections. Transcripts with perfect base pairing to a 7-mer miRNA seed were considered as the miRNA targets.For this analysis, a custom annotation defining the body and tail regions as independent transcripts was built based on the statistically significant cleavage sites found in the 3′-UTR of transcripts by our HMM model in HEK-293 cells treated with TEX (“TEX-HMM cleavage sites”). The custom annotation was based on the hg19 RefSeq annotation from UCSC. The end coordinate of the last exon of the conventional transcript was changed to the coordinate of the TEX-HMM cleavage sites. A new “tail” transcript was added to the annotation and defined as a single exon transcript with its start site defined as the TEX-HMM cleavage site plus 1 and its end as the end coordinate of the original transcript. The gene_id, gene_name, and transcript_id of the new “tail” transcript remained identical to the original transcript with the addition of “B” at the end. This custom annotation was then used as the annotation file for the remainder of the differential gene expression analysis, which used HTSeq-count for counting reads in features , and DESeq2See Supplementary Note https://github.com/eranroz/polyA. The data that support the findings of this study are available from the corresponding author upon request.The sequencing data generated during the course of this study are available at the Gene Expression Omnibus (GEO) under accession GSE84068. The HMM software is available via github at Supplementary InformationDescriptions of Additional Supplementary FilesPeer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Thalassia testudinum at two sites (Back‐Reef and Mid‐Lagoon) in Puerto Morelos Reef Lagoon, Mexico, by measuring dispersal of pollen and seeds in situ, and by fine‐scale spatial autocorrelation analysis with eight polymorphic microsatellite DNA markers. Prevalence of inbreeding was verified by estimating pairwise kinship coefficients; and by analysing the genotypes of seedlings grown from seeds in mesocosms. Average dispersal of pollen was 0.3–1.6 m (max. 4.8 m) and of seeds was 0.3–0.4 m (max. 1.8 m), resulting in a neighborhood area of 7.4 m2 (range 3.4–11.4 m2) at Back‐Reef and 1.9 (range 1.87–1.92 m2) at Mid‐Lagoon. Neighborhood area (Na) derived from spatial autocorrelation was 0.1–20.5 m2 at Back‐Reef and 0.1–16.9 m2 at Mid‐Lagoon. Maximal extensions of the genets, in 19 × 30 m plots, were 19.2 m (median 7.5 m) and 10.8 m (median 4.8 m) at Back‐Reef and Mid‐Lagoon. There was no indication of deficit or excess of heterozygotes nor were coefficients of inbreeding (FIS) significant. The seedlings did not show statistically significant deficit of heterozygotes (except for 1 locus at Back‐Reef). Contrary to our expectations, we did not find evidence of bi‐parental inbreeding in this dioecious seagrass with large genets but small genetic neighborhoods. Proposed mechanisms to avoid bi‐parental inbreeding are possible selection against homozygotes during fecundation or ovule development. Additionally, the genets grew highly dispersed ; such highly dispersed guerrilla‐like clonal growth form likely increases the probability of crossing between different potentially unrelated genets.The high prevalence of dioecy in marine angiosperms or seagrasses  is thought to enforce cross‐fertilization. However, seagrasses are clonal plants, and they may still be subject to sibling‐mating or bi‐parental inbreeding if the genetic neighborhood is smaller than the size of the genets. We tested this by determining the genetic neighborhoods of the dioecious seagrass  Distances of dispersal of pollen transported under water or at the water surface are generally limited, in the order of meters  with the following goals: (1) To deduce the potential for bi‐parental inbreeding from estimates of gene dispersal and size of the genets; (2) To verify the prevalence of inbreeding by estimating pairwise kinship coefficients, and by testing seedlings for deficiency of heterozygotes.The frequency of occurrence of bi‐parental inbreeding in dioecious clonal plants will depend on the dispersal potential of the sexual propagules in combination with their clonal growth strategies, which determine the size (extension) of the genets and the spatial distribution of their ramets. The species under study, Thalassia testudinum dominates extensive seagrass communities along the tropical and subtropical coasts of the Western Atlantic, where it provides essential ecosystem services such as provision of food and shelter to fauna, coastal protection, and carbon sequestration. It is a perennial clonal plant with varying abundance of sexual reproductive structures, depending on local conditions  are released in strands or masses of neutrally to slightly negatively buoyant mucilage , which is bordered on the seaward site by a fringing reef between 500 and 1800 m from the coast. Terrestrial influence is virtually absent because of the lack of surface rivers and salinity is 35.7‰. Monthly mean surface water temperatures (1993–2005) varied between 25.1°C in the winter and 29.9°C in the summer  and Mid‐Lagoon  at a distance of approximately 100 and 900 m from the reef crest, respectively. Seed dispersal was determined at an additional mid‐lagoon site .Four field simulations of seed dispersal were performed in September and October 2003  and a weighted lid lined on the inside with a rubber seal to warrant perfect sealing. The pollen was placed in the wells just before release in the 96 wells, making sure no air was trapped under the lid. The assembly was then placed on the bottom of the sea floor at the release point , which measured single point velocities in a 0.25 cc sampling volume at intervals of 5 sec for 1 h. This Doppler velocity meter was located at ~50 m distance from the experimental area in the seagrass bed. The ADV was placed in a small manually cleared patch and the sensor was positioned to measure velocities at ~2 cm above the sea bottom which corresponded with the height of natural and experimental pollen release.During the experiments in 2005, water velocity was measured with an Argonaut ADVThe distribution of pollen dispersal was derived from the experiments in 2006 (Table 3) and was the portion of the total number of trapped grains at each distance, with a correction of sampling effort per distance interval (no traps/circumference). A Hodges‐Ajne statistical test for circular distribution was applied to test whether the pollen grains were uniformly distributed in a circle Zar .Na=AFπ(σp2/2+σs2) . Where AF is an aerial correction factor for kurtosis of the distribution curve of dispersal distances  as T. testudinum at Mid‐Lagoon and Back‐Reef sites was determined in sample areas of 570 m2 (19 × 30 m). Within this area 620 grid points were selected at 1 m intervals and 100 coordinates (sampling points) were randomly selected  of  m2 19 × 0 m. Withk et al. .™ . DNA extractions were performed using DNeasy® Plant Minikit , following the manufacturer's instructions. DNA concentrations of the extractions were determined using a NanoDrop 2000 spectrophotometer  and DNA samples were diluted to 15 ng μL−1 for amplification. Polymerase Chain Reactions (PCR) were performed using eight microsatellite loci: TTMS‐GA6, TTMS‐GA8, TTMS‐TGA39, TTMS‐TCT58, TTMS‐GGT59, TTMS‐GT112, TTMS‐GT77, and TTMS‐Th1MS  was used as size standard. Alleles for each sample were scored using GeneMapper® software v4.0 (Applied Biosystems Inc.). The obtained genotypes were analyzed with Genclone v2.0  was calculated. This probability illustrates the likelihood of a repeated genotype being of sexual origin given the allelic diversity of each population, taking into account the estimated inbreeding coefficient FIS /(N−1), where N is the number of genotyped samples and G the number of MLGs in the population , expected heterozygosity or Nei's gene diversity HE (Nei A), and the inbreeding coefficient (FIS) were calculated with ARLEQUIN v3.5.1.2  and genets to infer the clonal subrange , we calculated the kinship coefficient  Ritland . A spatiThe average kinship coefficients were estimated for 19 distance classes with 1.5‐m intervals  with a final (20th) class of 30–35.5 m. The first size class 0–1.5 m allows for the inclusion of most near‐neighbor pairs.blog). For each sampling locality, spatial coordinates were randomly permuted 10,000 times among individuals, for each distance class, in order to test if the observed mean kinship values were different from those expected under random distribution of genotypes. The slope of the corresponding regressions was used to estimate gene dispersal distances.Pairwise kinship estimates were regressed on the spatial distance to estimate the slope of the linear regression (Sp = −blog/(1−FIJ(1.5 m)), and neighborhood size, Nb = −(1 − FIJ(1.5 m))/blog, were estimated, from the blog of the regression of the autocorrelation analysis (following Vekemans and Hardy F(1.5 m) is the average FIJ between individuals belonging to the first distance class.The rate of decrease in pairwise kinship with distance, σ to 20σ, where σ is the axial standard deviation of gene dispersal distances; Vekemans and Hardy σ) was determined for each sampling locality as the standard deviation of gene dispersal distance (σ = (Nb/(4πDe))0.5 as described in Volis et al. (e is the effective population density derived here from ramet (shoot) density multiplied with clonal richness (R). Finally, neighborhood area (Na) was calculated as a circular area with a radius of σ (min) to 20σ (max).Kinship is expected to decrease linearly with the spatial distance for a restricted range ). The point where both correlograms merge and F(r) is zero is the approximate distance at which clonality has no further effect on genetic structure  was determined by plotting ramet and genet level pairwise kinship coefficients together with the probability of clonal identity  for 2 months until they had formed a well‐developed juvenile shoot with enough foliar tissue for DNA extraction. A total of 162 seedlings were obtained. DNA extraction and genotyping followed methodology of section “Population genetics”, with the addition of two microsatellite loci  or 4.8m (Back‐Reef) away (Table S2). The dispersal range of pollen was higher at Back‐Reef than Mid‐Lagoon  at Back‐Reef and 1.9 m2 (range 1.87–1.92 m2) at Mid‐Lagoon.The neighborhood area  with a maximum of 12 alleles observed in locus GA12 in Back‐Reef and 13 alleles of the same locus in Mid‐Lagoon.pgen varied between 3.4 × 10−13 and 1.09 × 10−7, indicating a one in a million chance to get the same genotype at random given the local genetic diversity. Ramets with identical MLGs had a psex (fis) that varied between 4.67 × 10−64 and 1.8 × 10−6, indicating a one in a almost two hundred thousand that the duplication is the results of a sexual event and not asexual duplication.Different MLGs most likely resulted from sexual reproduction as FST −0.0018, P < 0.01). The average number of alleles per locus was 9 for Back‐Reef and 8 for Mid‐Lagoon. The average observed heterozygosity (Ho) per locus was 0.728 (range 0.460–0.888) in Back‐Reef, and 0.710 (range 0.420–0.898) in Mid‐Lagoon. The average expected heterozygosity was 0.714 (range 0.444–0.859) and 0.695 (range 0.433–0.854) in Back‐Reef and Mid‐Lagoon, respectively. . All loci were in HW equilibrium and no excess or deficiency of heterozygotes was found; also no significant linkage disequilibrium was observed.After removing the duplicate genotypes, genetic differentiation between sites resulted negative (it was corrected to 0), with Mid‐Lagoon and Back‐Reef effectively being part of the same genetic population  was 0.66 at Back‐Reef and 0.75 at Mid‐Lagoon, with respective maximal genet sizes (distance between the pairs of ramets belonging to the same genet that are furtherst away from eachother) of 19.2 m (median 7.5 m) and 10.8 m (median 4.8 m), however, genets could have been extended outside the studied plots. The clonal structure at both sites was similar and consisted of few large genets intermixed with many genetically unique ramets Fig. . The ramFIJ at the ramet level at distance class 1.5 m was 0.0367 at Back‐Reef and 0.0586 at Mid‐Lagoon and for the genet level it was 0.0354 at Back‐Reef and 0.0401 at Mid‐Lagoon.Kinship coefficients were highest at the first distance class and rapidly decreased with distance. bP < 0.05)log Sp were 0.0112 and 0.0144 for ramet level, and 0.0362 and 0.0407 for genet level in Back‐Reef and Mid‐Lagoon, respectively. The estimated neighborhood size (Nb) consisted of 89 and 69 individuals for ramet level, and 28 and 25 individuals for genet level in Back‐Reef and Mid‐Lagoon, respectively. The effective density of genets (De) consisted of 436 genets m−2 at Back‐Reef, and 410 genets m−2 at Mid‐Lagoon. Total variance of gene dispersal (σg2) was calculated for Back‐Reef and Mid‐Lagoon for ramet level  and genet level (0.005 in both sampling sites). The axial standard deviation of gene dispersal distance (σ) was 0.128 and 0.116 at ramet level, and 0.071 and 0.069 at genet level for Back‐Reef and Mid‐Lagoon, respectively. At ramet level, the neighborhood areas (Na) were 0.05–20.45 m2 and 0.04–16.87 m2 at Back‐Reef and Mid‐Lagoon, respectively. At genet level, the neighborhood areas were 0.02–6.34 m2 (Back‐Reef) and 0.01–5.98 m2 for the genet level at Mid‐Lagoon , but neither significant deficiency nor excess of heterozygotes was detected at GT77 . The seedlings from Mid‐Lagoon had two loci (GA6 and GT104) in HW disequilibrium, but we could not detect whether this could have been caused by a deficiency or excess of heterozygotes in further tests . Overall, no general trend in global deficiency or excess of heterozygotes in the seedlings was observed at neither site (Table The seedlings from Back‐Reef presented two loci (GA72 and GT77) in HW disequilibrium; locus GA72 had a deficiency of heterozygotes  were similar to those based on in situ dispersal of seeds and pollen . Thus, the neighborhood size derived from two independent approximations  are in agreement with each other, and mutually supportive. In situ dispersal of the seeds and pollen were measured under average hydrodynamic regimes during the reproductive season. The accordance of direct measures of pollen and seed dispersal with indirect genetic dispersal, indicates that effective local dispersal (resulting in settlement) occurs under average hydrodynamic regimes; and that incidental storms or hurricanes do not play a significant role in local gene dispersal. During storms, seeds released by fruits attached to mother plants may not travel far because their dispersal is not much affected by hydrodynamics (see below); and the pollen grains likely dilute in the water column, decreasing the probability reaching female flowers further away. The hydrodynamic regime at the Back‐Reef is rather high in comparison with many other T. testudinum meadows; thus we expect the area at this site to fall in the upper range of the local genetic neighborhood for this species.Local gene dispersal of T. testudinum has a dual seed dispersal strategy, where ~10–15% of the fruits with seeds detaching from the maternal plant, floating to the surface to be transported over longer distances by currents and winds . The filamentous pollen of Z. marina travelled further than pollen of T. testudinum  at the ramet level were low (between −0.0005 and 0.0586 at ramet level) and no indirect evidence of inbreeding was found (FIS was insignificant). Genetic evidence did not support the presence of inbreeding (heterozygote deficiency) or outbreeding (excess of heterozygotes) during seedling development. Only one locus of the seedlings from Back‐Reef showed a significant heterozygote deficiency. However, incompatibility of mates may occur during pollination where selection against homozygotes would result in aborted ovules. Selection against sib‐mates at pollination would require a genetic incompatibility mechanism on the stigmas or styles of the female flowers. Pollen–stigma interactions are understudied for seagrasses and have only been reported for three temperate species, dioecious Amphibolis antarctica, hermaphrodite Posidonia australis, and monoecious Zostera marina  enforces cross‐fertilization and in theory should enhance outcrossing and thus promote genetic variability at the cost of reduced sexual output. But separation of sexes offers no protection against sib‐mating. The small neighborhood area of k et al.  for thisT. testudinum. In a study by Van Dijk and Van Tussenbroek , smaller genets and a more complex distribution of the genets. The small clonal subrange (between 7 and 11 m) found in this study is in accordance with these findings. This clonal subrange was also low in comparison with other seagrass species, such as Cymodocea nodosa  and sib‐mating  to increase mate‐availability by allowing intermixing of male and female genets , or its guerrilla‐like growth form, which is an unusual clonal growth strategy for a climax species.In conclusion, considering the wide‐spread clonal growth combined with limited pollen and seed dispersal, no indications of bi‐parental inbreeding for None declared.Table S1. Current velocities at Back‐Reef and Mid‐Lagoon at Puerto Morelos reef lagoon during 2005, together with wind speed and direction.Table S2.Thalassia testudinum. Dispersal patterns of pollen with hydrodynamic parameters in the Puerto Morelos Reef Lagoon during pre‐trials in 2005.Table S3. Polymorphic microsatellite markers for Thalassia testudinum (modified from Van Dijk et al. Table S4. Characteristics of the microsatellite loci for Thalassia testudinum at the two study sites after removal of replicate genotypes.Figue S1. Photograph of an expanding meadow of Thalassia testudinum, showing long runners without aggregation, indicating a guerrilla clonal growth strategy.Click here for additional data file.


Nucleic acid nanotechnology has great potential for future therapeutic applications. However, the construction of nanostructured devices that control cell fate by detecting and amplifying protein signals has remained a challenge. Here we design and build protein-driven RNA-nanostructured devices that actuate in vitro by RNA-binding-protein-inducible conformational change and regulate mammalian cell fate by RNA–protein interaction-mediated protein assembly. The conformation and function of the RNA nanostructures are dynamically controlled by RNA-binding protein signals. The protein-responsive RNA nanodevices are constructed inside cells using RNA-only delivery, which may provide a safe tool for building functional RNA–protein nanostructures. Moreover, the designed RNA scaffolds that control the assembly and oligomerization of apoptosis-regulatory proteins on a nanometre scale selectively kill target cells via specific RNA–protein interactions. These findings suggest that synthetic RNA nanodevices could function as molecular robots that detect signals and localize target proteins, induce RNA conformational changes, and programme mammalian cellular behaviour. Nucleic acid nanotechnology has great potential for future therapeutic applications. Here the authors build protein-driven RNA nanostructures that can function within mammalian cells and regulate the cell fate. From this field, the concept of ‘nanomachine3’ or ‘molecular robots4’ has been investigated, because nucleic acids have the potential to change their conformations and functions based on the principle of simple Watson–Crick base pairing. For example, dynamic DNA nanostructures, such as the DNA walker5, the DNA motor6 and the DNA nanomachine9, have been constructed using DNA–DNA interactions. For biological applications, it is important to develop functional nanodevices that detect various environmental signals , induce structural changes and produce desired functions . Several DNA nanostructures have been generated for potential biomedical and biotechnology applications, such as target cell-surface detection11, imaging13, drug delivery15 and chemical reaction control16. For example, a DNA-based nanorobot has been designed to detect cancer cell-surface receptors and release a drug in target cells10. Stimuli-responsive DNA nanohydrogels with size-controllable properties17 and pH- or chloride-sensing DNA nanodevices have been constructed inside cells19. In addition to DNA, RNA has attracted the attention of bioengineers because of the structural diversity of RNA molecules  structures)21. Several RNA nanostructures, such as triangles, squares, nanorings, three-way junctions and prisms, have been constructed in vitro35 and some have been used for cellular applications through the attachment of a functional molecule, such as RNA 32 or protein 34, on the designed RNA structures. Synthetic RNA scaffolds that control the assembly of enzymes for hydrogen production in bacteria have also been reported26. However, the construction of nanostructured devices that control mammalian cellular behaviour by detecting or accumulating intracellular protein signals has not yet been demonstrated.In the nucleic acid nanotechnology field, a variety of nanostructures have been designed and constructed to utilize the programmable features of nucleic acids and the defined size and periodicity of the double-helical structure36. Several long noncoding RNAs have been shown to function as natural scaffolds that can control the localization and function of chromatin regulatory proteins37. The naturally occurring RNP interactions often control a variety of biological functions through dynamic regulation of the structures and activities of intracellular RNA or protein. Thus, we considered building synthetic RNP nanostructured devices by mimicking natural RNP complexes that have the following properties: (1) RNA-nanostructured devices detect and localize target RNA-binding proteins both in vitro and inside cells; (2) the conformation of the RNA devices is dynamically changed through specific RNP interactions; and (3) the actuation of the RNA devices produces functional outputs dependent on the extracellular and intracellular environment.Inside a cell, many RNA molecules cannot function alone. RNA molecules together with RNA-binding proteins construct nanostructured RNA–protein (RNP) complexes. For example, the ribosome, which is composed of ribosomal RNAs and proteins, is a nature-made, sophisticated RNP nanomachine that catalyses protein synthesis based on the information coded in genes. Clustered regularly interspaced short palindromic repeat-CRISPR-associated proteins (CRISPR-Cas9) are another example of RNP complex-mediated nanodevices that enable the editing of a target region of genomes in a customized mannerHere we report protein-driven RNA nanostructured devices that function in vitro and within live mammalian cells. Specific RNP interactions induce both structural and functional changes in the RNA nanodevices. The actuated RNA devices produce various outputs, such as the activation and repression of RNA aptamers Fig.  and the 38. The K-turn adopts a flexible conformation in a protein-free state, whereas L7Ae strongly stabilizes the K-turn with an approximately 60° bending angle. We have previously designed triangle-like RNP nanostructures in vitro using three K-turn-L7Ae motifs inserted into three vertexes of nanostructures composed of double-stranded RNAs33. However, it has remained a challenge (1) to induce both dynamic conformational and functional changes in RNA-nanostructured devices in response to a protein signal and (2) to construct protein-driven, functional RNA nanostructures inside mammalian cells.To design protein-responsive RNA nanodevices that induce dynamic conformational change, we employed the box C/D kink-turn (K-turn) RNA and a small RNA-binding protein from archaea, called L7Ae, as an appropriate RNP interaction motifred arrows).To induce an apparent conformational change via the RNP interaction, we designed two RNA strands (long (L) and short (S) strands) in which Watson–Crick base pairs are formed within the strands but where the two ends of the double-stranded RNA do not interact with each other Fig. a and 2a.38 provided no retarded bands  of 2Kt-33-Tri in the presence of L7Ae, but we did not observe this signal for the defective RNA, 2dKt-33-Tri, suggesting that FRET occurred likely due to the structural transition of 2Kt-33-Tri induced by the L7Ae-K-turn interaction.Conformational changes that regulate the distance between the two ends of structured RNAs support our concept, i.e., that we can control the distance and arrangement of RNA-conjugated molecules on the two edges through protein sensing as an input signal. To confirm our idea, we attached fluorescent Cy3 and Cy5 molecules to the edge of 2Kt-33-Tri and analysed the L7Ae-induced RNA conformational change using Förster resonance energy transfer  using the L7Ae-induced RNA structural change. We chose the split RNA aptamer To modulate the function of biMGA, we optimized the length of the linker region between the RNA devices and biMGA, and the stem sequence of biMGA  and a decrease in the Cy5 signal at 665 nm (F665) by preventing FRET due to the formation of Z-shape RNP nanostructures. Thus, we first optimized the sequence of the short RNA duplex at the ends of the RNA devices in vitro by measuring the change in the F565/F665 value before and after the RNP formation. We engineered 2Kt-17-Z by introducing Watson–Crick base pairs (from 4 to 8 bp) at the 5′- and 3′-ends of the RNA device  are directly used to construct RNP nanostructures in cells. Synthetic RNA devices (2Kt-17-Z-7) and chemically modified mRNA 40 coding for L7Ae were co-transfected into HeLa cells. The expression of L7Ae was confirmed by capillary-based immunodetection  and examined whether the RNP interaction-mediated accumulation of L7Ae-dCasp-8 on the RNA scaffold induces cell death  by detecting and assembling an apoptosis regulatory protein, caspase-8 (Casp-8), through specific RNP interactions on designed RNA scaffolds formed in target cells. In naturally occurring systems, Casp-8 contains the death effector domain (DED), which is responsible for the assembly and activation of Casp-8 molecules through proximity-induced oligomerizationath Fig. . To contath Fig. .40 and confirmed that the fusion of dCasp-8 to L7Ae maintained L7Ae-K-turn interactions in cells , whereas the defective RNA scaffold (14rcKt-33-Tri) with L7Ae-dCasp-8 did not . This result indicated that L7Ae-dCasp-8 alone does not induce cell death effectively, but the accumulation of L7Ae-dCasp-8 on the RNA devices facilitates cell-death induction. In addition, we observed that the dead cell populations increased as the number of Kt on the RNA scaffolds increased (9Kt and 14Kt), whereas the number of rcKt on the scaffolds did not affect cell death Kt-33-Tri and L7Ae were confirmed in vitro by EMSA and AFM Fig.  and 24. ath Fig. . These r43 and transfected modRNAs encoding various protein variants  together with RNA scaffolds (14Kt-33-Tri or 14rcKt-33-Tri) ri) Fig. . We confri) Fig. . Notablyri) Fig. . Togethe44. To develop a Lin28A-responsive cell-killing system with RNP nanostructures, we constructed Lin28A-fused dCasp-8 (Lin28A-dCasp-8) and a Lin28A-binding RNA scaffold, 9pre-let7d-33, by replacing the K-turn motif of 9Kt-33-Tri with pre-let-7d (RNA sequences derived from let-7d miRNA precursors), which interacts with Lin28A . Conversely, in cells that express Lin28A, the effect of cell death should be modulated by competitive binding between Lin28A and Lin28A-dCasp-8 to 9let7d-33, which should reduce the level of Casp-8 activation on the RNA scaffold . Transfection of 9pre-let7d-33 with Lin28A-dCasp-8-coding modRNA to HeLa cells showed effective cell death comparable to the effect of 9Kt-33-Tri with L7Ae-dCasp-8-coding modRNA, whereas Lin28A-dCasp-8 or 9pre-let7d-33 alone did not induce cell death, confirming that the RNP interaction is essential for triggering cell death. In addition, we observed that co-transfection of Lin28A-coding modRNA together with 9pre-let7d-33 and Lin28A-dCasp-8-coding modRNA reduced the number of dead cells  and has important roles in cancer, tissue regeneration and the self-renewal of stem cells28A Fig. , left. Cold Fig. , right. lls Fig. , suggestlls Fig. .Fig. 5RNTo further investigate whether the system could selectively control cell fate by detecting endogenous expression levels of Lin28A, we constructed a stable HeLa-Lin28A cell line that expresses Lin28A from the genome . We linked this switch to an RNA device, 14Kt-33-Tri . Protein-responsive RNA-nanostructured devices may regulate various biological processes, because the spatiotemporal regulation of naturally occurring RNP complexes has a crucial role in the control of gene expressions, signal transductions and cell-fate conversions.46 reported that DISC contains ninefold more Casp-8 than Fas-associated protein with death domain (FADD) and they proposed a DED chain assembly model for DISC formation. They demonstrated that the recruitment of many Casp-8 molecules to a single FADD molecule was required for DISC formation and subsequent apoptotic cell death. The DISC model seems to agree well with our observation that the assembly of more than nine L7Ae-dCasp-8 proteins on a single RNA scaffold facilitated effective Casp-8 activation and cell-death induction  on a designed RNA scaffold is important for the induction of effective cell death. In naturally occurring systems, the assembly and activation of Casp-8 are induced by the formation of death-inducing signalling complex (DISC), which is a key step in the death receptor-mediated activation of apoptotic pathways. Dickens et al.ion Fig. . The celion Fig. , suggest47  and by regulating Casp-8-mediated apoptosis signals on the RNA scaffold. Transfection of full-length Casp-8- or L7Ae-Casp-8-encoding mRNA did not induce cell death ath Fig. , indicatath Fig. .40; (2) modRNA can produce RNA-binding proteins without an immune response40; (3) the function of RNA devices can be transiently actuated inside cells, reducing the risk of potential side effects; and (4) the self-folding of RNA strands in vitro before transfection may facilitate the efficient detection of intracellular proteins via RNP interactions in the cytoplasm and the subsequent control of cell functions, because neither transcription nor RNA export from the nucleus are required in the case of RNA delivery; (5) RNA delivery approaches may have diverse medical applications, such as RNA vaccines48, cell reprogramming49, cell purification45 and cell-fate control40.To develop functional nanodevices in mammalian cells for future therapeutic applications, the introduction and formation of nanostructures inside target cells should be safe and efficient. Thus, we thought that the formation of RNP nanostructures in cells by RNA-only delivery is ideal for the following reasons: (1) the gene delivery of synthetic RNA rather than DNA is more likely to avoid potential genomic damage caused by random genomic integration of devices8, DNA nanorobots that recognize the cell surface and control cell signalling by utilizing the interaction between DNA aptamers and protein10, and protein-triggered DNA nanomachines on gold nanoparticles that control the assembly and cleavage of DNA components50. However, the detection of proteins by DNA nanodevices has been limited to in vitro or cell surfaces. Protein-assembled RNA nanostructures have also been constructed in vitro34, although these RNA nanostructures had rather static scaffolds and their conformations and functions were not controlled by the protein signal. Our RNA nanostructured devices can be actuated via their conformational changes both in vitro and inside mammalian cells in response to extracellular and intracellular protein signals, respectively. In addition, our RNA devices are signal transducers that are capable of converting the input protein signal into the work of RNA  or protein  by employing a dynamic RNA scaffold. We believe that RNA and RNP nanodevices can be employed as a material to construct molecular robots that regulate their structures and produce desired functions by sensing various types of extracellular and intracellular information.The construction of protein-responsive nucleic acid nanodevices in vitro has been reported. Examples of such nanodevices include nanomechanical DNA origami that detects proteins at the single-molecule levelBamHI and XbaI sites were purchased from Life Technology. Preparation of 9pre-let7d-33-long plasmid was delegated to FASMAC. The DNA templates were inserted into the BamHI–XbaI sites of pcDNA4/TO to give the desired plasmids. We used pcDNA3.1-L7Ae-myc-His6 and pBS-mFLICE51 (or pCSII-EF-Flag-mCasp8 CS-IRES-GFP43) for the construction of L7Ae-dCasp-8 and L7Ae-Casp-8 (or L7Ae-dCasp-8-CS and L7Ae-Casp-8-CS) plasmids. The dCasp-8 and full length Casp-8 was amplified from pBS-mouse Casp-8 by PCR using the corresponding primer sets, digested with EcoRI and XhoI, and inserted into the EcoRI–XhoI site of pcDNA3.1-L7Ae-myc-His6 to give pcDNA3.1-L7Ae-dCasp-8-myc-His6, pcDNA3.1-L7Ae-Casp-8-myc-His6 and the corresponding CS mutants. Lin28A was amplified from pLIN28Amyc-T2A-tagRFP by PCR using Lin28A-Fwd and Lin28A-Rev, digested with EcoRI and NotI, and inserted into the EcoRI–NotI site of pcDNA3.1-L7Ae-dCasp-8-myc-His6 to give pcDNA3.1-Lin28A-dCasp-8-myc-His6.DNA templates of 14Kt-33-Tri-long and 14Kt-33-Tri-short and 14Kt-27,28-Zig-long and 14Kt-27,28-Zig-short with DNA templates and primers were purchased from Greiner Japan. DNA templates for in vitro transcription were prepared by PCR using KOD-Plus-Neo DNA Polymerase (TOYOBO). The DNA templates were purified using a MinElute PCR Purification Kit (QIAGEN), according to the manufacturer’s protocol. RNAs were transcribed by a MEGAshortscript Kit (Ambion), according to the manufacturer’s protocols. The transcribed RNAs were purified by denaturing polyacrylamide gel electrophoresis (PAGE). After the recovery of the RNAs, the RNA concentration was measured using NanoDrop (Thermo Scientific). The DNA templates of 1Kt-33-Tri and 1rcKt-33-Tri were generated by PCR using synthetic oligo-DNAs. The DNA templates of 3, 6, 9 and 14Kt-33-Tri, and the corresponding mutants  were amplified from pcDNA4/TO-14Kt-33-Tri by PCR using the corresponding primer sets. The DNA templates were transcribed using a MEGAshortscript Kit (Ambion) and a modified protocol. Psuedouridine-5’-triphosphate and 5-methylcytidine-5’-triphosphate (TriLink BioTechnology) were used instead of uridine-5’-triphosphate and cytidine-5’-triphosphate. The transcription of 9pre-let7d-33 was conducted with only cytosine modification to maintain the RNP interaction between pre-let-7d and Lin28A. Reaction mixtures were incubated at 37 °C for 4–16 h. TURBO DNase (Ambion) was added to the reaction mixtures to remove template DNAs. After incubation at 37 °C for 30 min, the RNAs were purified using a FavorPrep Blood/Cultured Cells total RNA extraction column (Favorgen Biotech) and then treated with Antarctic Phosphatase (New England Biolabs). After incubation at 37 °C for 30 min, the resulting RNAs were purified by an RNeasy MinElute Cleanup Kit (QIAGEN) and denaturing PAGE.DpnI restriction enzyme (TOYOBO) for 30 min at 37 °C. The PCR products were purified using a MinElute PCR Purification Kit (QIAGEN), according to the manufacturer’s protocol. DNA templates for modRNA synthesis were generated by PCR using the primer set T7FwdG3C (0.3 µM) and Rev120A (0.3 µM) and using the PCR products of the 5′-UTR PCR (10 nM), 3′-UTR PCR (10 nM), and a coding region (0.2 ng µl−1). DNA templates for enhanced green fluorescent protein (EGFP)-myc-coding modRNA synthesis were generated by PCR using the primer set CGCT7G2 (0.3 µM) and Rev120A (0.3 µM), the PCR products of 5′-UTR PCR (10 nM) and an EGFP coding region. DNA templates for miRNA-responsive modRNAs (miRNA switches) were prepared by using the PCR products of miRNA target site-containing 5′-UTR (5′-UTR-T21-5p PCR or 5′-UTR-T302a-5p PCR) instead of the above 5′-UTR PCR. The PCR products were purified with the above-mentioned protocol.The 5′- and 3′-untranslated regions (UTRs) were generated by using PCR with the proper sets of synthetic oligo-DNAs. The protein-coding regions of the desired proteins were prepared by PCR with the corresponding protein-containing plasmids using proper primer sets. The template plasmids were digested with the mRNAs were generated from the corresponding DNA templates using a MEGAscript T7 Kit (Ambion) and a modified protocol. Psuedouridine-5’-triphosphate and 5-methylcytidine-5’-triphosphate (TriLink BioTechnology) were used instead of uridine-5’-triphosphate and cytidine-5’-triphosphate. A mixture of Anti-Reverse Cap Analog (TriLink) and guanosine-5’-triphosphate (4:1) was used instead of guanosine-5’-triphosphate. Reaction mixtures were incubated at 37 °C for 4 h and then treated with TURBO DNase (Ambion) at 37 °C for 30 min. The RNAs were purified using a FavorPrep Blood/Cultured Cells total RNA extraction column (Favorgen Biotech) and then treated with Antarctic Phosphatase (New England Biolabs) at 37 °C for 30 min. The resulting mRNAs were purified using an RNeasy MinElute Cleanup Kit (QIAGEN).Cy3- and Cy5-labelled RNAs were prepared by ligation using T4 RNA ligase (Ambion), pCp-Cy3 (Jena Bioscience) and pCp-Cy5 (Jena Bioscience). Cy3- and Cy5-labelling was performed with 150 pmol RNA using 10 U T4 RNA ligase, 3 nmol pCp-Cy3 or pCp-Cy5 and 10% (v/v) dimethyl sulfoxide in 10 µl at 16 °C for 36–48 h. The Cy3- and Cy5-labelled RNAs were purified using an RNeasy MinElute Cleanup Kit (Qiagen). After the recovery of RNAs, the RNA concentration was measured in a NanoDrop (Thermo Scientific). The labelling efficiencies of Cy3 and Cy5 were calculated from the absorbance of Cy3 and Cy5, respectively.2 and 150 mM KCl were heated at 80 °C for 3 min and then cooled at room temperature for 10 min. After the addition of L7Ae (50–1000 nM), the mixtures were incubated at room temperature for 10–30 min. The mixtures were mixed with 10 × loading buffer  and were subjected to electrophoresis through a native polyacrylamide gel with 0.5 × Tris/Borate/EDTA buffer at room temperature. After electrophoresis, the gels were stained for 10 min with SYBR Green I and II (Lonza). Images of the gels were collected using a Gel Doc EZ Imager (BIO-RAD) or a Typhoon FLA 7000 laser scanner .Mixtures of L-RNA and S-RNA (each 20–50 nM) in 20 mM phosphate buffer, pH 7.0, 1.5 mM MgCl−1 and a resonant frequency of ~300–600 kHz in water were used to scan the sample surface. The 320 × 240-pixel images were collected at a scan rate of 0.2 frames per second. A fresh mica surface was coated with 0.1% 3-aminopropyltriethoxy silane. The samples  were prepared in RNP binding buffer and diluted five- (for RNP nanostructures) or tenfold (for RNA nanostructures) with AFM observation buffer (20 mM Tris-HCl (pH 7.6) and 10 mM MgCl2). Then, they were applied to the mica for 5 min at room temperature and were washed with buffer solution. The image sequences were analysed using ImageJ (http://imagej.nih.gov/ij/) software.The RNA and RNP nanostructures were observed in solution. Their samples were prepared as described for the EMSA. The AFM images were collected using Nano Live Vision (Research Institute of Biomolecules Metrology Co.) or BIXAM (Olympus). Small cantilevers  having a spring constant of ~0.1 N m2 and 150 mM KCl. Tween-20 (0.1%) and either protein buffer  or L7Ae (600 nM) dissolved in protein buffer was added to the mixtures and incubated at room temperature for 10–30 min. The excitation wavelength was 512 nm and the emission and excitation bandwidths were 10 nm. Fluorescence measurements were carried out with Infinite M1000 Plate Reader (TECAN).Cy3- and Cy5-labelled RNAs (each 50 nM) were heated at 80 °C for 3 min and then cooled at room temperature for 10 min in 20 mM phosphate buffer (pH 7.0) containing 1.5 mM MgCl2 and 150 mM KCl. MG (2 µM), Tween-20 (0.1%) and either protein buffer  or L7Ae (600 nM) dissolved in protein buffer were added to the mixtures and incubated at room temperature for 10–30 min. The excitation wavelength was 610 nm and the emission and excitation bandwidths were 10 and 20 nm, respectively. Fluorescence measurements were carried out with Infinite M1000 Plate Reader (TECAN).biMGA-conjugated RNA nanostructures (100 nM) were heated at 80 °C for 3 min and then cooled at room temperature for 10 min in 20 mM phosphate buffer (pH 7.0) containing 1.5 mM MgCl2 in Dulbecco’s modified Eagle’s medium–High Glucose  containing 10% fetal bovine serum (Japan Bio Serum), non-essential amino acids (Invitrogen), sodium pyruvate (Sigma) and antibiotic antimycotic solution (Sigma). HeLa cells stably expressing Lin28A  were established by transfecting pIRES-puro vector containing Flag-tagged Lin28A sequence into HeLa cells. Positive clones were obtained by selection with puromycin (0.5 μg ml−1) for 2 weeks. Human iPS cells  were maintained in feeder-free condition52 with StemFit AK03 (Ajinomoto) on laminin-511 E8 .HeLa cells  were cultured at 37 °C in 5% CO2 and 150 mM KCl were heated at 80 or 95 °C for 3 min and then cooled at room temperature for 10 min. The mixtures of RNA nanostructures (0.1–8 pmol) and modRNAs (10–500 ng) were used for RNA delivery using Stemfect RNA Transfection Reagent (STEMGENT) according to the manufacturer’s protocol.Mixtures of L-RNA and S-RNA (each 0.1–4 μM) in 20 mM phosphate buffer, pH 7.0, 1.5 mM MgCl4 cells were seeded into 12-well plates. After a 24 h culture, RNA nanodevices (8 pmol) and modRNA (500 ng) were co-transfected into the cells using 2 µl of Stemfect RNA Transfection Reagent (STEMGENT). The medium was changed after 2 h. After 12 or 24 h of transfection, the cells were washed with phosphate-buffered saline (PBS) and incubated in 200 µl of 0.25% Trypsin-EDTA  at 37 °C for 3 min. After the addition of 100 µl of Dulbecco’s PBS containing 2% fetal bovine serum, the cells were analysed with a FACSAria cell sorter (BD Bioscience). A 532 nm laser and a 575/25 nm optical filter were used to detect Cy3 fluorescence. A 532 nm laser and a 670/30 nm optical filter were used to detect the Cy5 fluorescence attributed to FRET. In the flow cytometric analysis, dead cells were removed using a dot plot of forward scatter (FSC) versus side scatter (SSC). The evaluation of the FRET signal change by the actuation of an RNA nanodevice was performed by calculating the mean of the ratio between the Cy3 and Cy5 fluorescence intensities. Enhancement of the Cy3/Cy5 mean was calculated from the Cy3/Cy5 mean normalized to cells transfected with 2dKt-17-Z-7 and either MS2CP- or L7Ae-coding modRNA. The obtained means were normalized to the mean of negative control cells transfected with MS2CP-conding modRNA and 2dKt-17-Z-7 for calculating relative Cy3/Cy5 mean intensities.A total of 5 × 104 cells were seeded into 24-well plates. After a 24 h culture, the corresponding RNA nanostructures (0.1 pmol) and modRNA (10 ng) were co-transfected into the cells using 1 µl of Stemfect RNA Transfection Reagent (STEMGENT). After 2 h, the medium was changed. Cell images were taken after 24 h of transfection. The supernatants and cells were recovered and stained with Pacific Blue-labelled Annexin V (Life Technologies). Dead cells were stained with SYTOX AADvanced Dead Cell stain (Life Technologies), if needed. The stained cells were analysed using a FACSAria cell sorter and LSR Fortessa (BD Bioscience). A 405 laser and a 488 nm laser were used for the excitation of Pacific Blue and SYTOX AADvanced Dead Cell Stain, respectively; 450/50 and 660/20 nm optical filters were used to detect the fluorescence of Pacific Blue and SYTOX AADvanced Dead Cell Stain, respectively. Cells with high Pacific Blue fluorescence intensity were counted as apoptotic and dead cells.A total of 5 × 104 cells (HeLa cells and hiPSCs) were seeded into 24-well plates. After a 24 h culture, the corresponding RNA nanostructure (0.1 pmol for HeLa cells and 0.2 pmol for hiPSCs) and miRNA-responsive modRNA switch (10 ng for HeLa cells and 30 ng for hiPSCs) were transfected into the cells using 1 µl of Stemfect RNA Transfection Reagent (STEMGENT). MiR-21-5p inhibitor (2 pmol) and miR-302a-5p mimic (0.5 pmol) were used for HeLa cells. MiR-21-5p mimic (2 pmol) and miR-302a-5p inhibitor (2 pmol) were used for hiPSCs. The following protocols were similar to ‘Cell-death assay.’A total of 5 × 104 cells  were seeded into 24-well plates. After a 24 h culture, the corresponding RNA nanostructures  and Lin28A-dCasp-8 - or L7Ae-dCasp-8 -coding modRNA were co-transfected into the cells using 1 µl of Stemfect RNA Transfection Reagent (STEMGENT). Lin28A- or MS2CP-coding modRNA (40 ng for HeLa cells) was used as a competitor for the data in Fig. A total of 5 × 105 HeLa cells were seeded into six-well plates and incubated for 24 h. The corresponding RNA nanostructures (0.4 pmol) and modRNA (40 ng) were co-transfected to the cells using 4 µl of Stemfect RNA Transfection Reagent (STEMGENT). After 2 h, the medium was changed. The measurement of caspase activity was performed after 6 h of transfection. The caspase activity was measured using an APOPCYTO Casp-8 Fluorometric Assay Kit (MBL), according to the manufacturer’s protocol. IETD-AMC (100 μM) and IETD-FMK (10 μM) were used as caspase substrate and inhibitor, respectively.A total of 2 × 104 HeLa cells were seeded into 24-well plates and incubated for 24 h. K-turn (Kt) motif-containing EGFP (Kt-EGFP)-coding modRNA (100 ng) and a mixture of L7Ae- or L7Ae-dCasp-8-coding modRNA with MS2CP-coding modRNA  were co-transfected into the cells using 1 µl Stemfect RNA Transfection Reagent (STEMGENT). The medium was changed after 2 h. After 24 h transfection, the cells were recovered after Trypsin-EDTA treatment and analysed with LSR Fortessa (BD Bioscience). A 488 nm laser and a 530/30 nm optical filter were used to detect EGFP fluorescence. In the flow cytometric analysis, dead cells were removed using a dot plot of FSC versus SSC. The evaluation of translational repression was performed by calculating the mean of the EGFP fluorescence intensities. The obtained means were normalized to the mean of cells transfected with Kt-EGFP-coding modRNA (100 ng) and MS2CP-coding modRNA (250 ng).A total of 5 × 105 cells were seeded into six-well plates. Cells transfected with various amounts of modRNAs encoding L7Ae-myc-His6, L7Ae-dCasp-8-myc-His6 and Lin28A were prepared in a similar manner as described in Cell-death assay using 4 μl Stemfect RNA Transfection Reagent (STEMGENT). After 24 h transfection, the supernatant and adhesive cells were collected and washed by PBS. The collected cells were lysed in M-PER Mammalian Protein Extraction Reagent (Thermo) for 5 min, then the debris of the cells were removed by centrifugation. The total amount of protein in the supernatant was determined by BCA assay kit (Thermo). GAPDH, Myc-tag and Lin28A were detected by an automated capillary-based immunodetection system  using 100-fold diluted Anti-GAPDH , 20-fold diluted Anti-Myc  and 100-fold diluted Anti-Lin28A  antibodies as primary antibodies, respectively. HRP-conjugated Anti-rabbit  and 100-fold diluted Anti-goat  antibodies were used as secondary antibodies. The expression levels of the target proteins were quantified by Compass software (ProteinSimple).A total of 2 × 10t-test . The levels of significance are denoted as *P < 0.05, **P < 0.01, ***P < 0.001, NS not significant. All data are presented as the mean ± SD.The statistical significance was analysed by Welch’s The data that support the findings of this study are available from the corresponding author upon request.Supplementary Information


ADAR genes. Striving to explain this discrepancy, we uncover a functional mechanism of A-to-I editing in regulating mRNA abundance. We show that A-to-I editing stabilizes RNA secondary structures and reduces the accessibility of AGO2-miRNA to target sites in mRNAs. The editing-dependent stabilization of mRNAs in turn alters the observed editing levels in the stable RNA repertoire. Our study provides valuable insights into the functional impact of RNA editing in human cells.RNA editing introduces single nucleotide changes to RNA, thus potentially diversifying gene expression. Recent studies have reported significant changes in RNA editing profiles in disease and development. The functional consequences of these widespread alterations remain elusive because of the unknown function of most RNA editing sites. Here, we carry out a comprehensive analysis of A-to-I editomes in human populations. Surprisingly, we observe highly similar editing profiles across populations despite striking differences in the expression levels of  RNA A-to-I editing introduces single nucleotide changes to RNA, but its role in cells remains unclear. Here, the authors analyse A-to-I editomes in human samples and find that A-to-I editing stabilizes RNA secondary structures and reduces the accessibility of AGO2-miRNA to target sites in mRNAs. Adenosine-to-inosine (A-to-I) editing is the most abundant type of RNA editing in mammals, catalyzed by the protein family called adenosine deaminases acting on RNA (ADAR). The inosine nucleotide is subsequently recognized as guanine (G) by the translation machinery. Thus, RNA editing can induce amino acid changes in coding regions (referred to as recoding editing sites). A number of such recoding sites have been shown to be essential to cellular function and development4.RNA editing is a mechanism that alters RNA nucleotides in the co-transcriptional and post-transcriptional stages of gene expression7. Recent genome-wide studies have identified significant global alterations of A-to-I editing levels in various diseases, including cancer, neurological and vascular diseases12. These discoveries call for detailed investigations of the functional roles of RNA editing, especially those in non-coding regions.Although recoding events clearly show the biological significance of RNA editing, the majority of RNA editing occurs in non-coding regions of the mammalian transcriptome with unknown function15, RNA localization17, and RNA stability12. In particular, the impact of RNA editing on RNA stability has been the focus of a number of studies, given its potentially profound impact on gene expression. For example, inosine-containing transcripts are digested by the endonuclease V enzyme, providing a direct mechanism for the control of RNA stability by A-to-I editing18. A-to-I editing and ADAR proteins can also indirectly affect RNA stability by influencing the abundance or sequences of microRNA (miRNA) molecules, potent regulators of gene expression24.Previous in-depth studies of a small number of editing sites have identified several functional pathways of RNA editing that influence different aspects of gene expression, such as splicing28. However, there are seemingly contradictory reports on the predicted prevalence of RNA editing sites that alter miRNA target sequences. Some studies proposed that RNA editing tends to avoid miRNA target sites30, while others suggested that RNA editing is enriched in miRNA target regions31. Considering these results, further investigation and rigorous experimental validation are needed to elucidate the functional roles of non-coding editing sites in miRNA targeting.In addition to affecting miRNA sequences or expression, it has been speculated that A-to-I editing may modify the sequences of miRNA target sites in the 3′ untranslated regions (UTRs) of mRNAs. This hypothesis is attractive as it may explain the functional roles of many editing sites in the non-coding 3′ UTR regions. In fact, specific examples where A-to-I editing creates or destroys miRNA target sites have been foundRecent technologies have enabled the production of an extraordinary amount of RNA sequencing (RNA-Seq) data, which has driven large-scale analyses of RNA editing and its functional mechanisms. One potentially powerful approach is to analyze the differences in RNA editomes present in a large number of individuals. This strategy can uncover the biological, functional or environmental factors that cause variations in RNA editing. In this study, we report a comprehensive analysis of RNA editing across five human populations. Many editing sites are shared among individuals and have similar editing levels, which are not fully explained by the expression of ADAR proteins. Through detailed analyses and corroborating experiments, we characterize a mechanism through which RNA editing in 3′ UTRs affects miRNA targeting and regulates messenger RNA (mRNA) abundance. Instead of introducing nucleotide changes to miRNA target sites, we show that RNA editing stabilizes RNA secondary structures and reduces the accessibility of AGO2-miRNAs to target sites in mRNA 3′ UTRs. This mechanism may explain the functional roles of many RNA editing sites located in non-coding regions. It also highlights the importance of RNA secondary structure in post-transcriptional gene regulation.32. RNA-Seq data were analyzed using our previously developed methods34 (see Methods).To obtain a global view of RNA editing in human populations, we analyzed RNA-Seq data sets of 462 human individuals of the 1000 Genomes project. These subjects represent five populations: Utah residents with northern and western European ancestries , Finnish in Finland , British in England and Scotland , Toscani in Italy , and Yoruba in Ibadan, Nigeria . Lymphoblastoid cell lines were established from blood and polyA-selected RNA samples were sequenced34. Henceforth, we will restrict our analyses to A-to-G sites, since non-A-to-G substitutions constituted a minor fraction and demonstrated little overlap among individuals in general.A total of 28,848 distinct editing sites were identified in the 462 samples. In a single subject, the number of predicted editing sites ranged from ~ 200 to ~ 3500, which approximately correlated with RNA-Seq read coverage of the samples Fig. . On aver35 and DARNED36 databases, respectively. These 8080 editing sites were located in 909 genes  ≥ 2 reads with the edited nucleotide and an editing level ≥ 0.1 in ≥ 3 individuals of a population; and (2) read coverage ≥ 10 in ≥ 10% individuals of a population. These cutoffs were chosen to achieve a trade-off between the total number of selected sites and the fraction of A-to-G sites . The five populations demonstrated similar distributions of editing site prevalence Fig. , with thWe next examined the genomic, regulatory and functional features of rare  and prevalent  editing sites. The regional location within a transcript was highly similar for the two groups . The same observation holds if ADAR protein expression levels were used  of rare and prevalent editing sites showed a significant difference Fig. . In partres Fig. . Notablyres Fig. . This obres Fig. , it is enes Fig. . NeverthADAR1 in innate immunity42 and that A-to-I editing occurs specifically during sexual reproduction in the wheat scab fungus Fusarium graminearum43.Compared to those with rare editing sites, genes harboring prevalent editing sites were enriched in functional categories related to cell-cycle regulation and cell proliferation. Intriguingly, the most significantly enriched categories were sexual reproduction and negative regulation of viral genome replication  in 3′ UTRs that also harbor RNA editing sites. Interestingly, we observed that editing sites closer to miRNA target sites had higher editing levels than more distant sites  and control data sets obtained from K562 cells by the ENCODE project. Consistent with the observed positive correlation between AGO2 expression and editing levels in the human populations  among individuals with similar ion Fig. ? (2) Amoels Fig. ?Fig. 5AGADAR1 expression. The data are consistent with this expectation .For the first question, according to the model where AGO2-miRNAs preferentially target unedited mRNAs, we expect to observe higher target gene expression levels for individuals with higher ion Fig. . Furtherets Fig. . For thenes Fig. . ImportaADAR1 levels to genes whose expression was reduced upon ADAR1 KD (vs. control) . This editing-dependent modulation of mRNA abundance in turn induces an apparent increase in observed editing levels, thereby reducing the observed difference in editing levels of individuals with high vs. low ADAR1 expression. Figure ADAR1 levels. Importantly, this model is also consistent with the earlier observation that AGO2 expression correlates positively with average editing levels  of local RNA regions (7 nt windows) in the neighborhood of RNA editing sites (Methods). As the thermodynamic properties of inosines are not defined, previous studies have used guanosines, with closest properties to inosines, to replace inosines in RNA structure predictions42. Using this approach, we found that substituting A’s with G’s at the editing sites induced a significant decrease in accessibility near the edited A  fell into miRNA seed target regions. Next, we asked whether RNA secondary structures were different between the edited and unedited sequences. We used RNAplfoldd A Fig. . This led A Fig. .Fig. 6Ed48. RNA structures folded using G’s at the editing sites were also enriched with C’s and U’s at the opposing positions, thus forming G-C or G-U pairing . Thus, the edited nucleotides more often base-pair than the unedited nucleotides (80% vs. 52%). These results are consistent with the above observation of reduced accessibility  in the presence of G’s compared to A’s.On the basis of the RNAfold predictions, about half of the unedited A’s were opposite to a U nucleotide in the RNA structure folded using A’s at the editing sites Fig. . This opWe next asked whether editing-induced structural changes correlate with altered miRNA target gene expression. We analyzed the expression difference of miRNA target genes between individuals with and those without edited sites in these genes. Interestingly, we observed a significant negative correlation between the target gene expression difference  and the relative accessibility at the closest miRNA target site (accessibility of G vs. A) Fig. . It shouGOLGA3 and GINS1 genes, which both contain RNA editing sites and miRNA target sites in their 3′ UTRs. These 3′ UTR regions were cloned into the 3′ UTR of a firefly luciferase minigene   is less prone to AGO2 regulation  cells  lls Fig. , which cAGO2 OE and KD cells. This observation suggests that the differential expression between unedited and pre-edited minigenes is likely due to changes in the RNA secondary structure, rather than sequence alterations to the miRNA binding sites. To further confirm this hypothesis, we created additional mutant minigenes with matches of identical base-pairing to the original edited minigene versions: GC for GOLGA3 and CG for GINS1. Similar to the original edited minigenes, these mutant minigenes were also less prone to AGO2 regulation than the unedited versions  or RNA secondary structure, both of which may change AGO2-miRNA targeting of these genes. To investigate these possibilities, we constructed mutant versions of the minigenes with a GG mismatch in the predicted RNA secondary structure at the RNA editing site Fig. . We obseons Fig. . These eADAR genes across populations.In this study, we carried out a global analysis of RNA editomes in human populations using RNA-Seq data of 462 individuals. The large amount of data afforded a global view of RNA editing profiles across human populations. In general, editing sites differ greatly in their prevalence in a population, ranging from being very rare to nearly 100% prevalent. Compared to rare sites, prevalent editing sites are stronger substrates for ADAR editing and are associated with higher editing levels. These observations hold true for all five populations included in this study. Indeed, our analyses showed that editing profiles of the five populations are largely similar, with small differences in editing levels of specific sites. This similarity in editing profiles strikingly contrasted the significantly different expression levels of the To understand this apparent discrepancy, we conducted a series of in-depth analyses, supported by experimental validations, which led to the discovery of a new functional mechanism by which RNA editing can modulate mRNA abundance. Our results suggest that the AGO2-miRNA complex preferentially targets and destabilizes the unedited version of mRNAs that harbor editing sites in their 3′ UTRs. Furthermore, this differential regulation is likely mediated by a difference in RNA secondary structure caused by RNA editing. In general, the edited copy of an mRNA has a more stable secondary structure and renders lower accessibility for miRNA targeting than the unedited copy.17 and inosine-specific degradation18, it was speculated that editing generally reduces the expression levels of its target genes. However, recent large-scale studies in brain revealed a comprehensive landscape of correlation between mRNA abundance and RNA editing levels (or ADAR expression levels), which consists of both positive and negative correlations50. These observations imply that RNA editing may affect mRNA abundance through a variety of mechanisms, which calls for systematic analyses to reveal such mechanisms.It has been a challenge in the field to better understand the functional impact of RNA editing, especially in human cells where most editing occurs in non-coding regions. Based on known examples of A-to-I editing sites affecting nuclear retention of mRNAs48. Our data suggested that the unedited versions of mRNAs are more prone to miRNA targeting, explaining the existence of positive correlation between mRNA abundance and RNA editing levels. Previously, ADAR and A-to-I editing were reported to affect miRNA biogenesis through different mechanisms, including editing in primary miRNA transcripts that may alter mature miRNA sequences, editing-dependent suppression of miRNA biogenesis steps, or editing-independent regulation due to interaction/competition between ADAR and miRNA biogenesis machineries51. In addition to affecting miRNA sequences or abundance, it is conceivable that A-to-I editing may alter the sequences of mRNA 3′ UTRs targeted by miRNAs. This hypothesis has been proposed repeatedly30, given that A-to-I editing and miRNA target sites both occur frequently in 3′ UTRs. However, computational analyses showed that RNA editing tends to avoid miRNA target sites30, which argues against this simple hypothesis. Thus, our results revealed a new mechanism that is different from previous speculations: miRNA targeting is affected by RNA structural changes mediated by A-to-I editing rather than direct alteration of miRNA target site sequences.Our study revealed one such mechanism where 3′ UTR-associated A-to-I editing affects mRNA degradation by modulating RNA secondary structure and accessibility for miRNAs at their target genes. It should be noted that a general stabilization of RNA secondary structure by A-to-I editing has been reported previously52. Nevertheless, the collective changes exerted by many RNA editing sites together may be very influential to cellular function. In addition, such functional impact of RNA editing may be highly important under certain conditions, such as environmental stress or diseases, a subject for future investigations.It should be noted that the endogenous gene expression changes caused by RNA editing are generally small in this study, which is likely due to the relatively low level of RNA editing for most sites (editing level being 0.1–0.2) and the fact that miRNA targeting mostly causes small to modest changes in mRNA expression levels54, or genetic variations ) that may affect the structural substrates of ADAR editing56. In contrast, our study suggests that the functional consequence of RNA editing (in affecting mRNA abundance) may in turn alter the observed editing levels. Editing-dependent changes in AGO2-miRNA targeting explain, at least partially, the apparent positive correlation between AGO2 expression and RNA editing levels and the seemingly conflicting observations of large variations in ADAR expression and small variations in RNA editing levels across populations. Nevertheless, our study does not preclude the existence of additional mechanisms (such as those mediated by RBPs and SNPs) that may explain population-wide RNA editing similarity, which should be examined in the future. In addition, future studies are needed to investigate the underlying mechanism that accounts for the large variations in ADAR expression across populations.Our findings also revealed the complexity in the underlying mechanisms that account for the observed RNA editing variations. A conventional view of regulation of RNA editing levels focuses on direct mechanisms that influence ADAR-mediated RNA editing. Known mechanisms of such regulation include contributions by RBPs other than ADARs49. These studies indicate that RNA editing may have important functional implications in biology and disease. However, the functional consequence of the majority of non-coding editing sites remains elusive. Our study addresses this pressing question and provides insights into the functional impact of RNA editing in human cells.Recently, an increasing number of studies revealed global or specific changes in RNA editing profiles in various human diseases or biological processes32 were downloaded from the Geuvadis Data Browser (http://www.ebi.ac.uk/Tools/geuvadis-das) and mapped to the human genome (hg19) and Ensembl transcriptome57 using RASER33 with the parameters –m 0.05 –b 0.03. Potential mismatch positions were identified. A likelihood model was constructed to identify mismatch sites possibly due to sequencing errors. This model examines quality scores of the nucleotides, mismatch positions in the read and read coverage to calculate a log-likelihood ratio (LLR) for each mismatch site. Sites with LLR < 2 were removed as likely sequencing errors5. These mismatch sites were further filtered to require: (1) read coverage per site ≥ 10; (2) number of edited reads ≥ 2; and (3) editing level  ≥ 0.1. Furthermore, mismatch sites were discarded if they overlapped known SNPs (dbSNP v14158), simple repeats , homopolymers of more than 4 nts, or located in introns within 4 nts of a splice junction (using UCSC and Ensembl gene annotations57)6. At last, we used GIREMI to call the final editing sites with the aim of distinguishing editing sites from unknown SNPs without requiring genome sequencing data34.Raw Fastq files of the Geuvadis RNA-Sequencing project59.The genomic context of editing sites was determined using Ensembl gene annotations. If an editing site overlapped multiple types of genomic regions, its context definition was prioritized as follows: CDS > 3′ UTR > 5′ UTR > non-coding (nc)Exon > Intron-close > Intron-deep > Intergenic. ncExons refer to exonic regions in non-coding transcripts. Intronic editing sites were divided into those within 300 nucleotides and those that are further away from an exon-intron boundary . The location of editing sites relative to Alu elements was determined using Alu repeats annotated by RepeatMasker60. Selected GO terms were required to be significantly enriched (p < 0.05) with ≥ 2-fold enrichment among genes harboring prevalent editing sites, compared to the expected frequency determined using the background gene set (genes with rare editing sites). In addition, GO categories were discarded if < five genes were included. A similar analysis was conducted using genes with rare editing sites as the test set and those with prevalent editing sites as background.Enriched gene ontology (GO) terms were obtained for 238 genes with prevalent editing sites compared to 722 genes with rare editing sites using PANTHERp < 0.01). In addition, the absolute difference in average editing levels was required to be greater than 0.067, which corresponds to a difference of one edited read if the total read coverage was 15.The significance of the number of shared edited sites between two populations was calculated using a hypergeometric test, with the background set being all 8080 editing sites considered in this study. Differentially edited sites were identified via a Wilcoxon Rank-Sum test comparing the editing levels of sites testable in two populations , was larger than 10, and there were at least two different editing levels among these individuals. The least squares solution of the linear equation was computed using numpy.linalg.lstsq in Python2.7.Linear regression was carried out separately for every editing site, taking the editing level as the dependent variable and all three 61 on a pair of populations treating individuals as replicates. Significantly differentially expressed RBPs were identified by an adjusted p value < 0.01. A list of 584 RBPs was compiled from different sources . Only RBPs with an average RPKM level > 1 across all individuals were considered.Differential expression analysis was performed using DESeq44 were downloaded from the UCSC Table Browser (genome.ucsc.edu). In addition, miRcode45 predicted target sites of conserved miRNAs were also included. For further analysis, we only included targets sites that were located in 3′ UTRs and restricted to predicted target genes of the 20 most highly expressed miRNAs in lymphoblastoid cell lines measured by small RNA-Seq (GEO accession code GSE41437)66. Normalized miRNA expression levels for 452 individuals were downloaded from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/files/E-GEUV-3/GD452.MirnaQuantCount.1.2N.50FN.samplename.resk10.txt).Genomic coordinates of conserved miRNA target sites predicted by TargetScan5.1ADAR1, but similar AGO2-miRNA levels . This comparison was carried out separately for each miRNA and we required a minimum of 3 individuals in each group. The comparisons of gene expression levels between individuals with high and low AGO2-miRNA levels, but similar ADAR1 levels  . RNA-Seq reads were mapped using RASER similarly as described above for the Geuvadis data set. Gene expression levels  were calculated using in-house scripts. Expression level of each gene in the two replicated samples of each condition was averaged first, followed by calculation of fold changes between groups of individuals. Editing sites in AGO2 KD and control cells were identified as described above.Duplicated RNA-Seq data sets of 68. RNAplfold46 was used to calculate the probability for accessibility of a 7mer in the same region using the parameters –u 7 -L 300 –W 400. The secondary structure and accessibility measure of the edited sequence was calculated by replacing the “A” with a “G” at the editing site. If multiple editing sites exist in the same 3′ UTR, each editing site was analyzed separately and included in the final result.The RNA secondary structure was predicted for the region of ±500 nucleotides around an editing site using RNAfold68 using the reference (unedited) sequence. The 9 nt window was centered at the position given on the x-axis.In Fig. 46. Then, for each position around the editing site, the probability for accessibility of a 7mer region in the RNA structure was calculated. This calculation was carried out for both the edited and unedited version of the 1001 nt sequence. The relative accessibility of a 7mer was calculated as the log2 difference in accessibilities at each position in the edited and unedited sequences (with editing site defined as position 0 and the 7mer centered at a given position). An average for each position was then taken over all editing sites considered in this analysis. In addition, as a control, the same calculation was done for random A nucleotides. Control random A’s were chosen to satisfy all of the following: (1) not editing sites, (2) located in 3′ UTRs, and (3) located 300 to 500 nt away from editing sites. We replaced such A’s with G’s and calculated the relative accessibility.The relative accessibility Fig.  was calcTo directly analyze the consequences of editing-dependent structural changes at predicted miRNA sites Fig. , we comp68. If the editing site was unpaired but one of its immediate neighbors was paired, the nucleotide next to the opposite nucleotide of the paired neighboring nucleotide was taken as the opposite nucleotide of the editing site. In all other cases where the editing site was unpaired, the opposite nucleotide was identified as ‘N’ (see Supplementary Software).The nucleotides opposite to editing sites in the folded RNA structure Fig.  were ideGOLGA3, GINS1, and C1GALT1 were inserted into the 3′ UTR of the firefly luciferase reporter via Sac I and Xba I cloning sites  was cloned into the pLVX-Tight-puro vector via BamH I and Not I sites.Partial 3′ UTRs of AGO2 shRNA plasmids or 3 × Flag-hAGO2 plasmids were co-transfected into 293T cells  with pΔ8.9 and pVSV-G in ratio 4:3:2. pTet-on Advanced and pMD2.G vectors were co-transfected into GP2-293 cells  in ratio 1:1 to produce Tet-on retrovirus. All viruses were collected at 48 h after transfection. To produce Tet-on HEK293 cells, HEK293 cells  were infected by the Tet-on retrovirus for 48 h followed by 500 μg per ml G418 selection. Then HEK293 cells were infected by the control or AGO2 shRNA lentivirus, and HEK293-Tet-on cells were infected by the control or 3 × Flag-hAGO2 lentivirus for 48 h followed by 2 μg per ml puromycin selection.To make lentivirus, control or AGO2 knockdown were cultured with DMEM medium containing 10% FBS and 0.5 μg per ml puromycin. HEK293 cells with AGO2 overexpression were cultured with DMEM medium cantaining 10% FBS, 0.5 μg per ml puromycin and 100 μg per ml G418.HEK293 cells were cultured with DMEM medium containing 10% FBS. HEK293 cells with AGO2 knockdown and overexpression cells were plated into 6-well plates 1 day before the experiment. AGO2 overexpression was induced by 2 μg per ml doxycycline. Then 50 ng reporter and 50 ng reference plasmids (Renilla luciferase) were co-transfected into cells. Cells were collected 48 h after transfection.A total of 2.5 μg miR-24 or miR-26 sponge vector were transfected into HEK293 cells. After 24 h of transfection, 2.4 μg sponge, 50 ng reporter and 50 ng reference vector were co-transfected into pre-neutralized HEK293 cells. Cells were collected at 48 h after transfection. Control, AGO2 overexpression was induced by 2 μg per ml doxycycline. Cells were lysed in RIPA buffer and protease inhibitor  48 h after induction, and the total cell lysates were resolved with SDS-PAGE gels. The following antibodies were used: Flag antibody  and beta-actin antibody  from Santa Cruz, AGO2 antibody  from Abcam. The HRP-linked secondary antibodies from Santa Cruz  were used and the blots were visualized with the ECL kit . The uncropped Western blot images for AGO2 OE and AGO2 KD with controls are shown in Supplementary Fig. Total RNA was isolated using TRIZOL reagent and treated with DNase I . A total of 2 μg total RNA was reverse-transcribed using SuperScript III (Invitrogen). The qPCR was performed using the PowerUp™ SYBR® Green Master Mix (Thermo Scientific) and a LightCycler 480 system (Roche) according to manufacturer’s instructions.Computer scripts used to analyze RNA secondary structures and accessibility are included as Supplementary Software 32 were downloaded from http://www.ebi.ac.uk/ena/data/view/PRJEB3366. RNA-Seq data sets of ADAR1 KD, AGO2 KD and corresponding control data sets were downloaded from the ENCODE data repository . Normalized miRNA expression levels for 452 individuals were downloaded from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/files/E-GEUV-3/GD452.MirnaQuantCount.1.2N.50FN.samplename.resk10.txt). Read counts of miRNAs in lymphoblastoid cell lines measured by small RNA-Seq were downloaded from GEO 66.All data sets used in this study are publicly available. Raw RNA sequencing data (paired-end sequencing of polyA-selected RNA) for 462 individuals of the Geuvadis RNA-Seq projectSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Software 1Supplementary Software 2