Characterization of microRNA expression profiles in normal human tissues

<p>Abstract</p> <p>Background</p> <p>Measuring the quantity of miRNAs in tissues of different physiological and pathological conditions is an important first step to investigate the functions of miRNAs. Matched samples from normal state can provide essential baseline references to analyze the variation of miRNA abundance.</p> <p>Results</p> <p>We provided expression data of 345 miRNAs in 40 normal human tissues, which identified universally expressed miRNAs, and several groups of miRNAs expressed exclusively or preferentially in certain tissue types. Many miRNAs with co-regulated expression patterns are located within the same genomic clusters, and candidate transcriptional factors that control the pattern of their expression may be identified by a comparative genomic strategy. Hierarchical clustering of normal tissues by their miRNA expression profiles basically followed the structure, anatomical locations, and physiological functions of the organs, suggesting that functions of a miRNA could be appreciated by linking to the biologies of the tissues in which it is uniquely expressed. Many predicted target genes of miRNAs that had specific reduced expression in brain and peripheral blood mononuclear cells are required for embryonic development of the nervous and hematopoietic systems based on database search.</p> <p>Conclusion</p> <p>We presented a global view of tissue distribution of miRNAs in relation to their chromosomal locations and genomic structures. We also described evidence from the <it>cis</it>-regulatory elements and the predicted target genes of miRNAs to support their tissue-specific functional roles to regulate the physiologies of the normal tissues in which they are expressed.</p

The microRNA body map : dissecting microRNA function through integrative genomics

While a growing body of evidence implicates regulatory miRNA modules in various aspects of human disease and development, insights into specific miRNA function remain limited. Here, we present an innovative approach to elucidate tissue-specific miRNA functions that goes beyond miRNA target prediction and expression correlation. This approach is based on a multi-level integration of corresponding miRNA and mRNA gene expression levels, miRNA target prediction, transcription factor target prediction and mechanistic models of gene network regulation. Predicted miRNA functions were either validated experimentally or compared to published data. The predicted miRNA functions are accessible in the miRNA bodymap, an interactive online compendium and mining tool of high-dimensional newly generated and published miRNA expression profiles. The miRNA bodymap enables prioritization of candidate miRNAs based on their expression pattern or functional annotation across tissue or disease subgroup. The miRNA bodymap project provides users with a single one-stop data-mining solution and has great potential to become a community resource

Real-time quantification of microRNAs by stem–loop RT–PCR

A novel microRNA (miRNA) quantification method has been developed using stem–loop RT followed by TaqMan PCR analysis. Stem–loop RT primers are better than conventional ones in terms of RT efficiency and specificity. TaqMan miRNA assays are specific for mature miRNAs and discriminate among related miRNAs that differ by as little as one nucleotide. Furthermore, they are not affected by genomic DNA contamination. Precise quantification is achieved routinely with as little as 25 pg of total RNA for most miRNAs. In fact, the high sensitivity, specificity and precision of this method allows for direct analysis of a single cell without nucleic acid purification. Like standard TaqMan gene expression assays, TaqMan miRNA assays exhibit a dynamic range of seven orders of magnitude. Quantification of five miRNAs in seven mouse tissues showed variation from less than 10 to more than 30 000 copies per cell. This method enables fast, accurate and sensitive miRNA expression profiling and can identify and monitor potential biomarkers specific to tissues or diseases. Stem–loop RT–PCR can be used for the quantification of other small RNA molecules such as short interfering RNAs (siRNAs). Furthermore, the concept of stem–loop RT primer design could be applied in small RNA cloning and multiplex assays for better specificity and efficiency

Unsupervised hierarchical clustering of normal human tissues based on the variation of miRNA abundance demonstrates similar patterns as shown in Figure 2

<p><b>Copyright information:</b></p><p>Taken from "Characterization of microRNA expression profiles in normal human tissues"</p><p>http://www.biomedcentral.com/1471-2164/8/166</p><p>BMC Genomics 2007;8():166-166.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1904203.</p><p></p> Normalized Cfor each assay was transformed into ΔCagainst the average Cof all assays examined and clustered without centering the data. A pseudocolor scale outlines the Cvalues represented in the heat map. A detailed view of the clustering patterns of normal tissues is on the right

An enlarge view of the eight groups of most differentially expressed miRNAs

<p><b>Copyright information:</b></p><p>Taken from "Characterization of microRNA expression profiles in normal human tissues"</p><p>http://www.biomedcentral.com/1471-2164/8/166</p><p>BMC Genomics 2007;8():166-166.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1904203.</p><p></p> The pseudocolor scale is the same as that in Figure 3

The list of predicted target genes for miR-199a/199b/214 was refined by their expression in 19 normal tissue types extracted fromthe GNF database

<p><b>Copyright information:</b></p><p>Taken from "Characterization of microRNA expression profiles in normal human tissues"</p><p>http://www.biomedcentral.com/1471-2164/8/166</p><p>BMC Genomics 2007;8():166-166.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1904203.</p><p></p> Blue bars on the right side of the heat map, genes with brain-specific expression; red bars, genes with PBMC-specific expression. The pseudocolor scale represents the gene expression level that has been transformed to the log2-based ratio to the average signal of all genes extracted

Unsupervised hierarchical clustering of the normal human tissues based on the variation of miRNA expression correlates with the anatomical locations and physiological functions of the tissues

<p><b>Copyright information:</b></p><p>Taken from "Characterization of microRNA expression profiles in normal human tissues"</p><p>http://www.biomedcentral.com/1471-2164/8/166</p><p>BMC Genomics 2007;8():166-166.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1904203.</p><p></p> Normalized Cfor each assay was transformed into ΔCagainst the average Cof all assays examined and clustered after mean-centering the data for each miRNA but no centering was done for the tissues. A detailed view of the clustering patterns of normal tissues is on the right. The blue bar on the left side of the heat map represented the group of miRNAs primarily expressed in placenta, and the red bar indicated the miRNAs with significant increased expression in epithelial tissues including the gastrointestinal organs. A pseudocolor scale bar represented the fold change relative to the mean of the data for each miRNA

The abundance of miRNAs in all tissues represented by the estimated average copy numbers of all miRNAs examined, as well as by the average copy numbers of miRNAs in each of the eight most differentially expressed groups

<p><b>Copyright information:</b></p><p>Taken from "Characterization of microRNA expression profiles in normal human tissues"</p><p>http://www.biomedcentral.com/1471-2164/8/166</p><p>BMC Genomics 2007;8():166-166.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1904203.</p><p></p> Y-axis is the estimated copy number per cell (assuming 30 pg of total RNA in each cell), and the order of normal tissues at the X-axis is arranged by the clustering patterns shown in the Figure 3