PPP1R16A, the membrane subunit of protein phosphatase 1beta, signals nuclear translocation of the nuclear receptor constitutive active/androstane receptor.

Constitutive active/androstane receptor (CAR), a member of the nuclear steroid/thyroid hormone receptor family, activates transcription of numerous hepatic genes upon exposure to therapeutic drugs and environmental pollutants. Sequestered in the cytoplasm, this receptor signals xenobiotic exposure, such as phenobarbital (PB), by translocating into the nucleus. Unlike other hormone receptors, translocation can be triggered indirectly without binding to xenobiotics. We have now identified a membrane-associated subunit of protein phosphatase 1 (PPP1R16A, or abbreviated as R16A) as a novel CAR-binding protein. When CAR and R16A are coexpressed in mouse liver, CAR translocates into the nucleus. Close association of R16A and CAR molecule on liver membrane was shown by fluorescence resonance energy transfer (FRET) analysis using expressed yellow fluorescent protein (YFP)-CAR and CFP-R16A fusion proteins. R16A can form dimer through its middle region, where protein kinase A phosphorylation sites are recently identified. Translocation of CAR by R16A correlates with the ability of R16A to form an intermolecular interaction via the middle region. Moreover, this interaction is enhanced by PB treatment in mouse liver. R16A specifically interacted with PP1beta in HepG2 cells despite the highly conserved structure of PP1 family molecules. PP1beta activity was inhibited by R16A in vitro and coexpression of PP1beta in liver can prevent YFP-CAR translocation into mouse liver. Taken together, R16A at the membrane may mediate the PB signal to initiate CAR nuclear translocation, through a mechanism including its dimerization and inhibition of PP1beta activity, providing a novel model for the translocation of nuclear receptors in which direct interaction of ligands and the receptors may not be crucial.

Development of a spot reliability evaluation score for DNA microarrays.

We developed a reliability index named SRED (Spot Reliability Evaluation Score for DNA microarrays) that represents the probability that the calibrated gene expression level from a DNA microarray would be less than a factor of 2 different from that of quantitative real-time polymerase chain reaction assays whose dynamic quantification range is treated statistically to be similar to that of the DNA microarray. To define the SRED score, two parameters, the reproducibility of measurement value and the relative expression value were selected from nine candidate parameters. The SRED score supplies the probability that the expression level in each spot of a microarray is less than a certain-fold different compared to other expression profiling data, such as QRT-PCR. This score was applied to approximately 1,500,000 points of the expression profile in the RIKEN Expression Array Database.

Absolute expression values for mouse transcripts: re-annotation of the READ expression database by the use of CAGE and EST sequence tags.

The RIKEN expression array database (READ) provides comprehensive gene expression data for the mouse, which were obtained as relative values from microarray double-staining experiments with E17.5 mRNA as common reference. To assign absolute expression values for mouse transcripts within READ, we applied the E17.5 reference sample to CAGE (cap analysis of gene expression) and expressed sequence tag (EST) high-throughput tag sequencing. Newly assigned values within the READ database were validated by comparison to expression data from serial analysis of gene expression, CAGE and EST experiments. These experiments confirmed the great significance of the absolute expression values within the improved READ database. The new Absolute READ database on absolute expression data is available under.

Calculation of Spot Reliability Evaluation Scores (SRED) for DNA Microarray Data.

INTRODUCTIONIn terms of cost per measurement, the use of DNA microarrays for comprehensive and quantitative expression measurements is vastly superior to other methods such as Northern blotting or quantitative reverse transcriptase polymerase chain reaction (QRT-PCR). However, the output values of DNA microarrays are not always highly reliable or accurate compared with other techniques, and the output data sometimes consist of measurements of relative expression (treated sample vs. untreated) rather than absolute expression values as desired. In effect, some measurements from some laboratories do not represent absolute expression values (such as the number of transcripts) and as such are experimentally deficient. This protocol addresses one problem in some microarray data: the absence of accurate measurements. Spot reliability evaluation score for DNA microarrays (SRED) offers a reliability value for each spot in the microarray. SRED does not require an entire microarray to assess the reliability, but rather analyzes the reliability of individual spots of the microarray. The calculation of a reliability index can be used for different microarray systems, which facilitates the analysis of multiple microarray data sets from different experimental platforms.

Calculation of absolute expression values for DNA microarray data.

INTRODUCTIONIn terms of cost per measurement, the use of DNA microarrays for comprehensive and quantitative expression measurements is vastly superior to other methods such as Northern blotting or quantitative reverse transcriptase polymerase chain reaction (QRT-PCR). However, the output values of DNA microarrays are not always highly reliable or accurate compared with other techniques, and the output data sometimes consist of measurements of relative expression (treated sample vs. untreated) rather than absolute expression values as desired. In effect, some measurements from some laboratories do not represent absolute expression values (such as the number of transcripts) and as such are experimentally deficient. To address the problem that some microarray data sets fail to reflect the number of mRNA molecules sufficiently in a given sample (i.e., fail to provide absolute expression levels), additional methods are required. The procedure described here provides a new method for converting microarray data to absolute expression values with the use of external data such as expressed sequence tags (ESTs) and cap analysis of gene expression (CAGE) tags.