Intact RNA structurome reveals mRNA structure-mediated regulation of miRNA cleavage in vivo

Abstract MicroRNA (miRNA)-mediated cleavage is involved in numerous essential cellular pathways. miRNAs recognize target RNAs via sequence complementarity. In addition to complementarity, in vitro and in silico studies have suggested that RNA structure may influence the accessibility of mRNAs to miRNA-induced silencing complexes (miRISCs), thereby affecting RNA silencing. However, the regulatory mechanism of mRNA structure in miRNA cleavage remains elusive. We investigated the role of in vivo RNA secondary structure in miRNA cleavage by developing the new CAP-STRUCTURE-seq method to capture the intact mRNA structurome in Arabidopsis thaliana. This approach revealed that miRNA target sites were not structurally accessible for miRISC binding prior to cleavage in vivo. Instead, we found that the unfolding of the target site structure plays a key role in miRISC activity in vivo. We found that the single-strandedness of the two nucleotides immediately downstream of the target site, named Target Adjacent nucleotide Motif, can promote miRNA cleavage but not miRNA binding, thus decoupling target site binding from cleavage. Our findings demonstrate that mRNA structure in vivo can modulate miRNA cleavage, providing evidence of mRNA structure-dependent regulation of biological processes.

. The list of oligos and primers sequences. Table S2. CAP-STRUCTURE-seq constraints improve the structure prediction of structure in the 18S rRNA.  Figure S1. The illustration of the spurious chemical reactivity signals caused by degradation products. The final chemical reactivity contains the RNA structure information from both degraded RNAs and intact RNAs. Additionally, degraded mRNAs are capable of introducing false positive signals in the reverse transcription stalling methods. For example, the No.3 SHAPE reactivity is not caused by chemical modification but from a degradation event. Figure S2. Establishment of CAP-STRUCTURE-seq. A, NAI was titrated to achieve single hit kinetics in structure probing. Gel analysis of 18S rRNA structure probing in the presence of 1 µg of total A. thaliana RNA at NAI concentration of 75 mM, 150 mM and 300 mM (left). Time course analysis of in vivo SHAPE modification of 18S rRNA in A. thaliana etiolated seedlings with durations of 1 min, 5 min, 15 min and 30 min (right). FL, full length. Seq Lanes, sequencing lanes. B, Bioanalyzer assay indicated Terminator enzyme could digest 5' phosphate transcripts. The peaks of 18S and 28S rRNAs, which have 5' phosphate, decreased dramatically after Terminator treatment (bottom) compared to non-treatment (top).  (1), is inversely related to sequence complementarity. A SCPS score of zero is equivalent to perfect complementarity between the miRNA and the target site. We predicted the plant miRNAs targets of those expressed miRNAs by TargetFinder. Then, we chose the targets which overlapped with all the validated miRNA targets from four different genome-wide studies (23-26). B, The pipeline for combining miRNAome, (-)SHAPE and Degradome to calculate transcriptome-wide Cleavage Efficiency (CE) in vivo. Detailed explanation is in the Methods and the Supplementary Methods. C and D, Degradome reads distribution in WT (C) and xrn4 mutant (D) around identified miRNA cleavage sites (2). The zero position on the horizontal axis indicates the miRNA cleavage sites at the tenth position of miRNA complementary sites (as illustrated beneath the horizontal-axis). E, Target site sequence complementarity does not linearly correlate with miRNA cleavage efficiency (CE). Some target sites with mismatches (SCPS=2.0 and 3.0) cleave more efficiently than perfectly matched target sites (SCPS=0). Mann-Whitney-U significance tests were performed. 820 positive CE target sites were analyzed.   . CAP-STRUCTURE-seq provides the complete map of the 18S rRNA in vivo structure at nucleotide resolution. A, The complete 18S rRNA (length 1,808 nt) phylogenetic structure is colour-coded according to the SHAPE reactivity generated from CAP-STRUCTURE-seq (SHAPE reactivity >0.75 marked in red; SHAPE reactivity 0.5-0.75 marked in orange; SHAPE reactivity < 0.5 marked in grey). The table quantifies the correspondence between the 18S rRNA phylogenetic structure and the high and low reactivity groups. In the entire 18S rRNA (length = 1,808 nt), 75.1% of nucleotides that show high in vivo SHAPE reactivity in our data set correspond to single-stranded regions in the phylogenetic structure (true positive), whereas, 64.1% of the nucleotides that show low in vivo SHAPE reactivity correspond to base-paired regions in the phylogenetic structure (true negative). The 35.9% of the nucleotides that show low in vivo SHAPE reactivity but correspond to singlestranded regions in the phylogenetic structure (false negative) are presumably protected by either ribosomal proteins or non-base-pairing tertiary RNA structure. Of the 24.9% reactive nucleotides that are annotated as base-paired in the phylogenetic structure (false positive), 75% of these nucleotides are positioned either at the end of a helix or adjacent to a helical defect such as a bulge or loop. These locations are known to lead to structural flexibility(3) B, Sequence alignment of A. thaliana, S. cerevisiae and H. sapiens, shows that the non-conserved nucleotides (red asterisks) have high SHAPE reactivities in A. thaliana. These nucleotides are single-stranded in the corresponding region of human (brown, PDB: 4V6X) and yeast (cyan, PDB: 4v7r) 18S rRNA. The crystal structures are presented by Chimera with the nucleotides labelled according to the A. thaliana location.   Figure S9. Validation of TAM functionality by a designed structure assay. A, Cartoon representation of the protoplast transformation assay to validate the TAM functionality using a designed structure assay. SL, stem loop structure motif. The miRNA156 target sites (blue comb) followed by 0 or 2 Adenines (As) and ending with a SL. The prefixes, "0A" and "2A", indicate the number of Adenines. B, In vivo RNA structures of 0A_SL and 2A_SL. C, The non-cleaved substrate mRNAs of the structures in B was measured by qRT-PCR (dark yellow bars). The antisense target sites were used as controls (teal bars). Data are mean +/-SEM from three independent biological replicates. P value < 0.01 by Student's t-test. We used ViennaRNA tools version 2.4.9 to predict RNA structure. Our CAP-STRUCTURE-seq is the first SHAPE library in plants, and so contains structural information for all four nucleotide bases. Thus, we compared our new method with our previous DMS Structure-seq data in plants, which contains only A/C structure information (4). For the plant 18S rRNA phylogeny structure, there are several regions that have not been confidently determined due to the lack of co-variation (5). For instance, the region from nucleotide 748 to 869 was left completely single-stranded due to insufficient co-variation evaluation (Supplementary Figure S6). This is why the PPV/Sensitivity values of the full length 18S rRNA are not very high, and even with idealized data constraints they only attain 0.57/0.58. However, these values are much higher than the in silico prediction. Structure prediction of the 18S rRNA using the CAP-STRUCTURE-seq data yielded improvements not only when compared to the in silico prediction but also to the prediction based on DMS Structure-seq data. We further selected six conserved structure regions along the full 18S rRNA (5). Again, the structure predictions using CAP-STRUCTURE-seq data improved on the in silico and Structure-seq results for these six regions. Surprisingly, for three of these regions the CAP-STRUCTURE-seq-based predictions improve on the structure predicted by the idealized data. Based on the comparison with the DMS-based Structure-seq data on the 18S rRNA, structure prediction with our CAP-STRUCTURE-seq outperforms DMS Structure-seq. The idealized constraints were produced by mapping paired and single-stranded nucleotides in the phylogenetic structure to SHAPE reactivities of 0 and 1, respectively. RNA degradation is relatively efficient (6) and there are many different pathways that can generate degradation products in the degradome library besides miRNA cleavage, for example, deadenylation-mediated mRNA decay, non-sense mediated decay (NMD) or XRN4mediated co-translational mRNA decay (reviewed by (7)). miRNAs regulate mRNAs through translational repression, mRNA de-stabilization and mRNA cleavage. In animals, it has been suggested that translational repression is prevalent, which can then be followed by destabilization, such as shortening of the poly-A tails at the 3' end and removal of the cap at the 5' end (8). In plants, cleavage is the dominant pathway for miRNAs to regulate their target mRNAs (9). In order to compare the ability of different miRNAs to cleave their targets on a global scale, we need to quantify the cleavage efficiency (CE) of miRNAs at their target sites.

References
Our CE calculation is based on two underlying facts (2, 10): miRNA-mediated cleavage is the major mRNA turnover pathway for target genes; the 5' cleaved products are located within binding sites, which are temporally stable. Therefore, the degradation signal within target sites reflects the cleavage products from miRISC cleavage. These two facts were also confirmed by our analysis below.
Firstly, to confirm that the degradation signal within target sites is mainly from miRNAmediated cleavage, we mapped the 5'end of our WT A. thaliana degradome reads to previously validated cleavage sites (2). We found that most of the read ends were mapped at the tenth nucleotide of the miRNA complementary sites (Supplementary Figure S3C), which provides strong evidence of miRNA cleavage, as other degradation pathways rarely prefer to leave the 5' cleavage end exactly at the tenth position of miRNA complementary sites. Additionally, to confirm that the 5' cleaved products are temporally stable, we mapped the read ends of the cleavage products in the xrn4 mutant to previously validated cleavage sites (2). The cleavage site distribution in xrn4 mutant exhibited the same pattern as WT (Supplementary Figure S3D), which is consistent with the notion that miRNA cleavage products are temporally stable intermediates, resistant to cellular XRN4 exonuclease in A. thaliana, although the precise mechanism is currently unknown (10,11). Thus, we counted the degradation reads within target sites as the outcomes of cleavage products from miRISC cleavage.
Since AGO1 and miRNA are an enzyme complex, we defined the cleavage efficiency (CE) in a similar way to enzyme activity. In detail, the catalytic ability of an enzyme can be defined as the amount of product generated by one unit of enzyme from one unit of substrate, which led us to define: Cleaved transcripts can be characterized by the 5' phosphate featured on 3' cleavage products. We constructed a degradome library to capture cleavage products. RNA abundance of degradation products can be measured by the Reads Per Kilobase of transcript per Million mapped reads (RPKM). RPKM is a measure of relative RNA concentration in the whole transcriptome. In the degradome, the RPKM of each mRNA means relative degraded mRNA fragment number compared to all degradation products, i.e.,

[ ] ≈ .
To quantify the miRNA-mediated degradation products, we counted and designated the reads mapped within each target site as the products of miRNA-mediated cleavage. Therefore, we can label the miRNA-mediated Degradome RPKM as mirDegradome [ The total degradation products, total RNAs and total miRNAs should be constant and be reflected by the library sequencing depth. Therefore, α is a constant.
The population of each mRNA is constant over time due to the dynamic equilibrium of an intact mRNA and its degraded products (12). Therefore, = +