Consensus miRNA expression profiles derived from interplatform normalization of microarray data

Abstract

Eukaryotic gene expression is controlled at the post-transcriptional level by small noncoding RNAs called microRNAs (miRNA). miRNAs play important roles during early development and participate in gene regulatory circuits in the cell. Different high-throughput expression analysis methods including microarrays, bead-based detection, and small RNA cloning have been applied to quantitatively detect miRNAs in various tissues, cell types, and biological conditions. High-throughput expression data was collected from public repositories and processed to create a database of miRNA expression profiles. Several commonly used normalization methods were compared to identify suitable methods for cross-platform comparison of high-throughput miRNA expression data. The database provides interlaboratory and interplatform validated reference expression levels for miRNAs. The normalized expression profiles were validated by querying for well-established features of miRNA expression. Firstly, expression profiles of several tissue-specific miRNAs showed good agreement between the database and previously reported profiles. We have also identified a set of miRNAs that are constitutively expressed across mammalian tissues. Secondly, we used the database to compare the expression patterns of miRNAs belonging to the let-7 family, where the divergence in expression patterns implies that they may have diversified functionally. Lastly, we compared expression profiles of intronic and clustered miRNAs. Expression profiles of intronic miRNAs and clustered miRNAs showed either very good, or in certain cases, very poor correlation with the host gene. Interplatform comparison of miRNA expression profiles thus provides a resource of consensus expression profiles that can be used in the future for studying miRNA function and regulation.

FIGURE 1.

A flowchart summarizing the steps carried out to generate the normalized data.

FIGURE 2.

The box plots were generated using a subset of normalized data. Four brain samples and six PC3 and prostate samples were selected from bead-based (B1, B2) and oligonucleotide microarray studies (M1–M4). The distribution of log transformed un-normalized values (A), mean normalization (B), quantile normalization (C), normalization with respect to mean expression level of sixteen constitutive miRNAs (D), Z score normalization (E) is shown.

FIGURE 3.

(A) Tissue-specific expression profiles of miRNAs based on Z ratios calculated as described in the Mterials and Methods section. The red boxes signify a z ratio of >1.96, which corresponds to a P-value of <0.05 for tissue-specific expression. (B) A subset of the miRNAs in A show a high basal level of expression across different tissues.

FIGURE 4.

(A) Cluster of 130 miRNA expression profiles across 23 different tissues. Z scores for miRNA expression profiles were calculated as described in Materials and Methods. Average Z scores were calculated wherever multiple replicates of the same sample were available. Clustering was performed using Self Organizing Maps. (B) Identification of constitutive miRNAs. Coefficient of variation for Z scores of each miRNA was calculated. miRNAs with <0.65 CV were selected, and their expression profiles visualized by generating a heatmap of Z scores of 23 tissues.

FIGURE 5.

Expression levels of different members of the let-7 family of miRNAs across different samples. Z scores for expression of eight members of the hsa-let-7 family were calculated. The expression profiles for 140 experimental conditions were clustered using Self Organizing Maps.

FIGURE 6.

Gene structure of MCM7 and the three miRNAs (hsa-miR-25, hsa-miR-106b, and hsa-miR-93) in intron 12 and 13. (A) Z score for miRNA expression was calculated as described earlier. Scatterplots of miRNA expression over 105 experimental conditions were plotted against each other. Correlation plots of expression profiles between intronic miRNAs, (B) between hsa-miR-93 and hsa-miR-25, (C) between hsa-miR-106b and hsa-miR-25.