Abstract
MicroRNAs (miRNAs) modulate protein and mRNA expression through translational repression and/or mRNA decay. In this study, we combined SILAC-based proteomics and RNAseq to identify primary targets based on measurements of protein and mRNA repression and analysis of transcript 3'UTR sequences. The primary target set was used to compare different prediction algorithms, revealing higher stringency of selection by Targetscan and PITA compared with miRanda, at the expense of higher false negatives. A key finding was that significant and unexpected variations occurred in the kinetics of repression as well as the sensitivity to exogeneous miRNA concentration. Bimodal thresholds were observed, which distinguished responses to low (10 nm) versus high (50-100 nm) miRNA, as well as the onset of repression at early (12-18 h) versus late (36-48 h) times. Similar behavior was seen at the transcript level with respect to kinetics of repression. The differential thresholds were most strongly correlated with ΔΔG, the net free energy of miRNA-target interactions, which mainly reflected inverse correlations with ΔGopen, the free energy of forming 3'UTR secondary structures, at or nearby the miRNA seed matching sites. Thus, our working model is that protein binding or other competitive mechanisms variably interfere with the accessibility of miRISC to the transcript binding site. In addition, biphasic responses were observed in a subset of proteins that were partially down-regulated at early times, and further down-regulated at later times. Taken together, our findings provide evidence for varying modes of miRNA target repression, which lead to different thresholds of target responses with respect to kinetics and concentration, and predict that certain transcripts will show graded responses in sensitivity and fold-change under cellular conditions that lead to varying steady state miRNA levels.
Highlights
From the ‡Department of Chemistry and Biochemistry, §Howard Hughes Medical Institute University of Colorado, Boulder, Colorado 80309
MiRISC interacts with mRNA-bound poly(A) binding protein C (PABPC) through a complex between AGO and the GW182 trinucleotide-repeat-containing protein
Protein Abundance Changes in Response to miR-22— WM239A cells derived from metastatic melanoma were labeled by SILAC with heavy (H) or light (L) isotopically-labeled ArgϩLys, and transfected with 50 nM of nontargeting (NT) miRNA or miR-22, previously identified as an miRNA whose expression is regulated by oncogenic mutant B-Raf signaling in melanoma [39]
Summary
Reagents—Heavy isotopically labeled Arg (13C615N4-Arg) and Lys (13C615N2-Lys) and medium-labeled Arg (13C6-Arg) were from Cambridge Isotope Laboratory (Andover, MA). Proteins were quantified using the microplate BCA protein assay, and 125 g each of miRNA-treated cell lysates and NT-treated cell lysates were mixed and processed using the filter-aided sample preparation (FASP) protocol [28]. MaxQuant used 7 ppm maximum initial mass deviation for the precursor ion, and 0.5 Da MS/MS tolerance, searching six top MS/MS peaks per 100 Da. False discovery rates were 0.01 for both peptide and protein identifications, with 0.5 maximum posterior envelope probability (PEP) for peptides, six amino acid minimum peptide length, and two minimum total peptides, of which at least one peptide must be unique (i.e. non-razor) for each protein identification. Protein Half-life Measurements—Protein half-lives were estimated as described [32], plating unlabeled cells (5 ϫ 105) on 6 cm dishes overnight, and initiating with 3 ml RPMI/FBS containing heavy-labeled ArgϩLys (t ϭ 0 h). Genes were quantified only when fragment per kilobase of transcript per million mapped fragments (FPKM) were greater than or equal to one under two conditions (e.g. miR-22 and NT)
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