RISC RNA sequencing for context-specific identification of in vivo microRNA targets

Abstract

Rationale: MicroRNAs (miRs) are expanding our understanding of cardiac disease and have the potential to transform cardiovascular therapeutics. One miR can target hundreds of individual mRNAs, but existing methodologies are not sufficient to accurately and comprehensively identify these mRNA targets in vivo.

Objective: To develop methods permitting identification of in vivo miR targets in an unbiased manner, using massively parallel sequencing of mouse cardiac transcriptomes in combination with sequencing of mRNA associated with mouse cardiac RNA-induced silencing complexes (RISCs).

Methods and results: We optimized techniques for expression profiling small amounts of RNA without introducing amplification bias and applied this to anti-Argonaute 2 immunoprecipitated RISCs (RISC-Seq) from mouse hearts. By comparing RNA-sequencing results of cardiac RISC and transcriptome from the same individual hearts, we defined 1645 mRNAs consistently targeted to mouse cardiac RISCs. We used this approach in hearts overexpressing miRs from Myh6 promoter-driven precursors (programmed RISC-Seq) to identify 209 in vivo targets of miR-133a and 81 in vivo targets of miR-499. Consistent with the fact that miR-133a and miR-499 have widely differing "seed" sequences and belong to different miR families, only 6 targets were common to miR-133a- and miR-499-programmed hearts.

Conclusions: RISC-sequencing is a highly sensitive method for general RISC profiling and individual miR target identification in biological context and is applicable to any tissue and any disease state.

Figure 1. Adverse effects of RNA amplification on RNA abundance profiling

(Center) Schematic diagram outlines methods for RNA sequencing library preparation, with and without RNA amplification. (Left) RNA-Sequencing count data log₂(FPKM+1) from the same mouse cardiac mRNA, nonamplified or after amplification. Red dots indicate mRNAs observed in nonamplified but not amplified preparations. (Right) RNA-Sequencing count data from 100 ng (high-input) or 0.5 ng (low-input) polyA⁺ RNA from the same mouse heart, without amplification. Orange lines are linear regression best fit, green lines are lines of unity.

Figure 2. RISC immunoprecipitation and RISC-Sequencing of normal mouse hearts

(a) Ago-2 immunoblot of Ago2 immunoprecipitates from 50, 200 and 1000 mg mouse cardiac homogenate. (b) mRNA abundance (log₂ adjusted FPKM) in mouse cardiac RISCome and transcriptome. Orange line is best fit linear regression; green is line of unity. (c) Volcano plot (plot of fold-change in expression value vs P-value) for mouse heart RISCome/transcriptome (n/c = no change). Red indicates RISC-enrichment (significant in upper right quadrant); blue indicates RISC-depletion. (d) Proportion of mouse heart RISC-enriched mRNAs (n=1,645) and RISC-depleted mRNAs (n=75) predicted to be targets for any of 139 cardiac-expressed miRs. Red and blue bars indicate mRNAs from the upper right and upper left quadrants of (c), respectively.

Figure 3. Comparison of RISC score and RISC abundance for miR target prediction

(a) Number of RISC-associated mRNAs predicted to be targets for any of 139 cardiac-expressed miRs in each RISCome abundance quintile. * = P<0.01 vs same transcriptome abundance quintile by χ² test. (b) RISC mRNA enrichment score versus transcript mRNA abundance (FPKM) vs, for the cardiac transcriptome (only transcripts with FPKM to 200 are shown). Colors indicate different transcript abundance quintiles. (c) RISC mRNA enrichment score versus RISCome mRNA abundance for 1,645 significantly RISC-enriched transcripts) vs RISC enrichment score (only transcripts with FPKM to 400 are shown). Colors indicate different RISC-enriched mRNA abundance quintiles.

Figure 4. RISC-Sequencing of miR-133a transgenic mouse hearts

(a) Volcano plot of RISC score differences and associated p-values between miR-133a transgenic and normal hearts. Red is hyper-enriched, blue is depleted; grey indicates less than 1.3-fold change. Upper right quadrant includes genes with RISC scores ≥ 1.3-fold higher in miR-133a heart, P<0.001 (χ² test). (b) Proportion of TargetScan-predicted miR-133a targets in the cardiac transcriptome, nontransgenic RISCome, and miR-133a transgenic RISCome.

Figure 5. Luciferase reporter assays for putative miR-133a target 3′UTRs

(Top) 3′ UTRs of putative miR-133a target genes were cloned into a dual Renilla/firefly luciferase reporter vector and co-transfected with miR-133a precursor expression plasmid. (Bottom) Luciferase signals for miR-133a (black bars) as percentage of the Renilla/firefly ratio observed with co-transfection empty expression plasmid (white bars). * = P<0.05 vs control by Student’s t-test.

Figure 6. Gene Ontology (GO) analysis of miR-133a and miR-499 RISCome hyper-enriched mRNAs

(a) Sequence homology of members of the miR-133 and miR-208/499 families. (b) Venn diagram of hyper-enriched mRNAs in mir-133a (left) and mir-499 (right) programmed cardiac RISComes with respective GO categories. * = over-represented category compared to distribution of all genes across the transcriptome (see Methods).