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Non-inhibited miRNAs shape the cellular response to anti-miR.

Androsavich JR, Chau BN - Nucleic Acids Res. (2014)

Bottom Line: However, disentangling primary target derepression induced by miRNA inhibition from secondary effects on the transcriptome remains a technical challenge.These transcripts physically dissociate from AGO2-miRNA complexes when anti-miR is spiked into liver lysates. mRNA target displacement strongly correlated with expression changes in these genes following in vivo anti-miR dosing, suggesting that derepression of these targets directly reflects changes in AGO2 target occupancy.These data strongly suggest that miRNA co-regulation modulates the transcriptomic response to anti-miR.

View Article: PubMed Central - PubMed

Affiliation: Regulus Therapeutics Inc., 3545 John Hopkins Ct, San Diego, CA 92121, USA jandrosavich@regulusrx.com.

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Validation of novel miR-122 primary targets in mouse liver. AGO2-RIP competition responses of 37 miR-122 target candidates measured by Nanostring nCounter technology in the presence of (A) 400 nM or (B) 4000 nM anti-122. Fold-changes are relative to PBS. Green bars mark genes with statistically significant reduction of mRNA in IP with treatment (P < 0.05 by ANOVA with Dunnett correction for multiple comparisons, n = 3, error bars represent SEM). Results were normalized based on the geometric mean of Rnf167 and Nras, the most stable of all reference genes tested (Supplementary Figure S2). (C) Summary of genes significantly reduced in IP with varying doses of anti-122, represented as fraction of total (N = 37). (D) Correlation between RIP responses at 400 nM anti-122 and in vivo gene expression changes measured by Nanostring. Significantly upregulated genes resulting from anti-122 administration (unpaired t-test, FDR = 5%, n = 3) are shown in pink, others in gray. Statistical significance from RIP experiments is indicated by squares (significant) and circles (non-significant). Genes with significant RIP responses (grouped with a black outline) were fit by least-squares linear regression (r = −0.91, P (one-tailed) < 0.0001, Pearson correlation). In contrast, genes with non-significant RIP responses were poorly correlated with gene expression (r = −0.04, P = 0.4313). (E) Fraction of significantly upregulated genes with significant (pink; Ntotal = 16) or non-significant (gray; Ntotal = 21) RIP responses at any tested anti-122 concentration. Additional data are in Supplementary Figure S2-S3.
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Figure 2: Validation of novel miR-122 primary targets in mouse liver. AGO2-RIP competition responses of 37 miR-122 target candidates measured by Nanostring nCounter technology in the presence of (A) 400 nM or (B) 4000 nM anti-122. Fold-changes are relative to PBS. Green bars mark genes with statistically significant reduction of mRNA in IP with treatment (P < 0.05 by ANOVA with Dunnett correction for multiple comparisons, n = 3, error bars represent SEM). Results were normalized based on the geometric mean of Rnf167 and Nras, the most stable of all reference genes tested (Supplementary Figure S2). (C) Summary of genes significantly reduced in IP with varying doses of anti-122, represented as fraction of total (N = 37). (D) Correlation between RIP responses at 400 nM anti-122 and in vivo gene expression changes measured by Nanostring. Significantly upregulated genes resulting from anti-122 administration (unpaired t-test, FDR = 5%, n = 3) are shown in pink, others in gray. Statistical significance from RIP experiments is indicated by squares (significant) and circles (non-significant). Genes with significant RIP responses (grouped with a black outline) were fit by least-squares linear regression (r = −0.91, P (one-tailed) < 0.0001, Pearson correlation). In contrast, genes with non-significant RIP responses were poorly correlated with gene expression (r = −0.04, P = 0.4313). (E) Fraction of significantly upregulated genes with significant (pink; Ntotal = 16) or non-significant (gray; Ntotal = 21) RIP responses at any tested anti-122 concentration. Additional data are in Supplementary Figure S2-S3.

Mentions: With the RIP competition assay in hand, we next sought to identify which target candidates from a shortlist of computational predictions best responded to anti-miR-122. A custom Nanostring nCounter codeset was designed with probes recognizing 37 genes predicted by the TargetScan algorithm (11) to be regulated by miR-122 (Table 1 (27–35)). An additional four genes without miR-122 sites were also included, among which the top two most stable genes were used as reference genes (Supplementary Figure S2). Similar to earlier results, these candidate transcripts were differentially displaced by anti-miR-122 from immunoprecipitated fractions (Figure 2A–C, Supplementary Figure S3). Based on the extent of displacement, we were able to classify candidate genes as either ‘RIP-responsive’ or ‘RIP-non-responsive’. Fourteen candidates (38%) showed statistically significant displacement with 400 nM anti-miR-122 compared to control (Figure 2A). An additional two candidates responded at the next highest tested anti-miR-122 concentration (4000 nM; Figure 2B), making for a total of 16 candidates (43%) that could be confirmed as being anti-miR-122 sensitive. These targets are consequently likely to be direct targets of miR-122. Consistently, all but one RIP-responsive gene (94%) showed significant derepression in vivo following anti-miR-122 treatment (Figure 2E). Notably, expression changes of RIP responsive genes were very strongly correlated (r = −0.91) with levels of RIP response (Figure 2D), indicating that changes in expression in vivo are proportional to the amount of target RNA released from RISC complexes by anti-miR. In contrast, a similar correlation did not exist with RIP-non-responsive candidates. Most non-responsive genes were also not significantly upregulated following in vivo anti-miR-122 treatment (Figure 2E). There were, however, a few exceptions: a total of nine genes were significantly upregulated, but RIP-non-responsive at any tested anti-miR-122 concentration (summarized in Table 1). Potentially, expression changes in these latter genes occur as a result of secondary effects of miR-122 inhibition, rather than as a direct consequence. Therefore, despite the fact that these genes contain a miR-122 seed-match and are upregulated in the presence of anti-122, they are less likely to be direct biomarkers of miRNA modulation. These results demonstrate how RIP competition assays can serve as a filter combined with expression analysis to assist in identifying high-confidence primary anti-miR PD markers.


Non-inhibited miRNAs shape the cellular response to anti-miR.

Androsavich JR, Chau BN - Nucleic Acids Res. (2014)

Validation of novel miR-122 primary targets in mouse liver. AGO2-RIP competition responses of 37 miR-122 target candidates measured by Nanostring nCounter technology in the presence of (A) 400 nM or (B) 4000 nM anti-122. Fold-changes are relative to PBS. Green bars mark genes with statistically significant reduction of mRNA in IP with treatment (P < 0.05 by ANOVA with Dunnett correction for multiple comparisons, n = 3, error bars represent SEM). Results were normalized based on the geometric mean of Rnf167 and Nras, the most stable of all reference genes tested (Supplementary Figure S2). (C) Summary of genes significantly reduced in IP with varying doses of anti-122, represented as fraction of total (N = 37). (D) Correlation between RIP responses at 400 nM anti-122 and in vivo gene expression changes measured by Nanostring. Significantly upregulated genes resulting from anti-122 administration (unpaired t-test, FDR = 5%, n = 3) are shown in pink, others in gray. Statistical significance from RIP experiments is indicated by squares (significant) and circles (non-significant). Genes with significant RIP responses (grouped with a black outline) were fit by least-squares linear regression (r = −0.91, P (one-tailed) < 0.0001, Pearson correlation). In contrast, genes with non-significant RIP responses were poorly correlated with gene expression (r = −0.04, P = 0.4313). (E) Fraction of significantly upregulated genes with significant (pink; Ntotal = 16) or non-significant (gray; Ntotal = 21) RIP responses at any tested anti-122 concentration. Additional data are in Supplementary Figure S2-S3.
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Figure 2: Validation of novel miR-122 primary targets in mouse liver. AGO2-RIP competition responses of 37 miR-122 target candidates measured by Nanostring nCounter technology in the presence of (A) 400 nM or (B) 4000 nM anti-122. Fold-changes are relative to PBS. Green bars mark genes with statistically significant reduction of mRNA in IP with treatment (P < 0.05 by ANOVA with Dunnett correction for multiple comparisons, n = 3, error bars represent SEM). Results were normalized based on the geometric mean of Rnf167 and Nras, the most stable of all reference genes tested (Supplementary Figure S2). (C) Summary of genes significantly reduced in IP with varying doses of anti-122, represented as fraction of total (N = 37). (D) Correlation between RIP responses at 400 nM anti-122 and in vivo gene expression changes measured by Nanostring. Significantly upregulated genes resulting from anti-122 administration (unpaired t-test, FDR = 5%, n = 3) are shown in pink, others in gray. Statistical significance from RIP experiments is indicated by squares (significant) and circles (non-significant). Genes with significant RIP responses (grouped with a black outline) were fit by least-squares linear regression (r = −0.91, P (one-tailed) < 0.0001, Pearson correlation). In contrast, genes with non-significant RIP responses were poorly correlated with gene expression (r = −0.04, P = 0.4313). (E) Fraction of significantly upregulated genes with significant (pink; Ntotal = 16) or non-significant (gray; Ntotal = 21) RIP responses at any tested anti-122 concentration. Additional data are in Supplementary Figure S2-S3.
Mentions: With the RIP competition assay in hand, we next sought to identify which target candidates from a shortlist of computational predictions best responded to anti-miR-122. A custom Nanostring nCounter codeset was designed with probes recognizing 37 genes predicted by the TargetScan algorithm (11) to be regulated by miR-122 (Table 1 (27–35)). An additional four genes without miR-122 sites were also included, among which the top two most stable genes were used as reference genes (Supplementary Figure S2). Similar to earlier results, these candidate transcripts were differentially displaced by anti-miR-122 from immunoprecipitated fractions (Figure 2A–C, Supplementary Figure S3). Based on the extent of displacement, we were able to classify candidate genes as either ‘RIP-responsive’ or ‘RIP-non-responsive’. Fourteen candidates (38%) showed statistically significant displacement with 400 nM anti-miR-122 compared to control (Figure 2A). An additional two candidates responded at the next highest tested anti-miR-122 concentration (4000 nM; Figure 2B), making for a total of 16 candidates (43%) that could be confirmed as being anti-miR-122 sensitive. These targets are consequently likely to be direct targets of miR-122. Consistently, all but one RIP-responsive gene (94%) showed significant derepression in vivo following anti-miR-122 treatment (Figure 2E). Notably, expression changes of RIP responsive genes were very strongly correlated (r = −0.91) with levels of RIP response (Figure 2D), indicating that changes in expression in vivo are proportional to the amount of target RNA released from RISC complexes by anti-miR. In contrast, a similar correlation did not exist with RIP-non-responsive candidates. Most non-responsive genes were also not significantly upregulated following in vivo anti-miR-122 treatment (Figure 2E). There were, however, a few exceptions: a total of nine genes were significantly upregulated, but RIP-non-responsive at any tested anti-miR-122 concentration (summarized in Table 1). Potentially, expression changes in these latter genes occur as a result of secondary effects of miR-122 inhibition, rather than as a direct consequence. Therefore, despite the fact that these genes contain a miR-122 seed-match and are upregulated in the presence of anti-122, they are less likely to be direct biomarkers of miRNA modulation. These results demonstrate how RIP competition assays can serve as a filter combined with expression analysis to assist in identifying high-confidence primary anti-miR PD markers.

Bottom Line: However, disentangling primary target derepression induced by miRNA inhibition from secondary effects on the transcriptome remains a technical challenge.These transcripts physically dissociate from AGO2-miRNA complexes when anti-miR is spiked into liver lysates. mRNA target displacement strongly correlated with expression changes in these genes following in vivo anti-miR dosing, suggesting that derepression of these targets directly reflects changes in AGO2 target occupancy.These data strongly suggest that miRNA co-regulation modulates the transcriptomic response to anti-miR.

View Article: PubMed Central - PubMed

Affiliation: Regulus Therapeutics Inc., 3545 John Hopkins Ct, San Diego, CA 92121, USA jandrosavich@regulusrx.com.

Show MeSH
Related in: MedlinePlus