Search

DNA microarray technology has been extensively used for gene expression analysis of both eukaryotic and prokaryotic organisms. For eukaryotic gene expression profiling, the poly(A)-based reverse transcription of messenger RNA (mRNA) followed by T7 RNA polymerase-based in vitro transcription is generally required to produce enough RNA targets for hybridization with the microarray chips. However, the same method cannot be directly applied to prokaryotic mRNAs due to the lack of poly(A) sequences at the 3′ ends. Conventional methods usually require large amounts of starting RNAs and lead to high background noise. Recently developed amplification methods enable smaller amounts of prokaryotic RNA to be used from samples with species-specific primers, oligo(dT) primers, or random primers. In this study, three target preparation methods, including the direct labeling, polyadenylation-involved oligo-dT priming, and random priming amplification (respectively referred to as DL, PAOD, and RPA hereafter) were evaluated through expression profiling of a heat shock model of Escherichia coli. The PAOD method was found to be more sensitive and more specific in differential gene expression measurements than either DL and RPA, even when the E. coli RNA was only a small proportion of the simulated eukaryotic host RNA. The results suggest that PAOD is the preferred target preparation method for prokaryotic transcriptome. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. To discover novel regulatory pathways during myeloid differentiation, we performed miRNA as well as mRNA expression profiling of in vitro-differentiating HL-60 cells treated with 12-O-tetradecanoylphorbol-13-acetate (TPA). The main findings were up-regulation of miR-146a/b, miR-21, miR-221, miR-222, miR-155, miR-26a and down-regulation of miR-199a*, miR-181c, miR-142-3p, miR-92. After integrating the miRNA and mRNA expression data into a Transcriptome Interaction Database by Molecule Annotation System (MAS) software, a number of differently expressed mRNAs were revealed as potential targets of these miRNAs. [ABSTRACT FROM AUTHOR]

Target prediction for animal microRNAs (miRNAs) has been hindered by the small number of verified targets available to evaluate the accuracy of predicted miRNA-target interactions. Recently, a dataset of 3,404 miRNA-associated mRNA transcripts was identified by immunoprecipitation of the RNA-induced silencing complex components AIN-1 and AIN-2. Our analysis of this AIN-IP dataset revealed enrichment for defining characteristics of functional miRNA-target interactions, including structural accessibility of target sequences, total free energy of miRNA-target hybridization and topology of base-pairing to the 5′ seed region of the miRNA. We used these enriched characteristics as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprecipitation-enriched parameters (mirWIP), which optimizes sensitivity to verified miRNA-target interactions and specificity to the AIN-IP dataset. MirWIP can be used to capture all known conserved miRNA-mRNA target relationships in Caenorhabditis elegans at a lower false-positive rate than can the current standard methods. [ABSTRACT FROM AUTHOR]

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design. [ABSTRACT FROM AUTHOR]