Liver Regenerating

Zc3h15[Coding-mRNA]

miR-140-5p[miRNA]

NA[mRNA]

Liver Tissues

Mus musculus (mouse)

BMC Genomics 2013 Apr 18 14, 264 doi:10.1186/1471-2164-14-264 PMID:23597149

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

qRT-PCR

We determined miRNA recruitment at selected time points posthepatectomy previ- ously shown to correspond to the G1 (1h), S (36H) and M (48H) phases of the hepatocyte cell cycle, and in the quies- cent liver, corresponding to G0 [30,31]. Cell cycle stage was confirmed by activation of genes associated with G1 (jun, fos, myc), S (cyclin D1) and M (Foxm1) cell cycle phases determined by RNA-seq analysis of samples from these time points. Following UV-crosslinking of the RISC to microRNA and mRNA, we quantified miRNAs immunoprecipitated with an antibody that recognizes Argonaute 1 through 4 proteins and therefore immunoprecipitates all RISCs [32] by ultra-high throughput sequencing. Next, we turned our attention to the mRNAs that were identified in the RISC using our HITS-CLIP assay. We speculated that miRNA regulation would be more signifi- cant for those mRNAs that were highly enriched in the RISC relative to their overall abundance in the tissue. Therefore, to identify ‘expression-weighted footprints’ ,we used RNA-seq to quantify total mRNA expression in qui- escent liver, and 1 h, 36 h and 48 h posthepatectomy. Next, we calculated RISC enrichment for all mRNAs rela- tive to their overall abundance . To identify potential ceRNA networks, we selected all mRNAs that had one major footprint targeted by a single miRNA. For this analysis, footprints within 20 bp of each other were merged. If (1) the second strongest distinct footprint was less than 25% of the strongest footprint, and (2) the most loaded miRNA in the strongest footprint was 10x higher than the second-most loaded miRNA in the footprint, then the mRNA and miRNA were included in a ceRNA network. This process was performed for each time point. Networks for each miRNA were then merged across all time-points to create summary networks.