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Caimiao Wei

Publications and source records attributed to Caimiao Wei.

2 recordsLinked to original sources

The dominant negative thyroid hormone receptor beta-mutant {Delta}337T alters PPAR{alpha} signaling in heart.

PPARalpha and TR independently regulate cardiac metabolism. Although ligands for both these receptors are currently under evaluation for treatment of congestive heart failure, their interactions or signaling cooperation have not been investigated in heart. We tested the hypothesis that cardiac TRs interact with PPARalpha regulation of target genes and used mice exhibiting a cardioselective Delta337T TRbeta1 mutation (MUT) to reveal cross-talk between these nuclear receptors. This dominant negative transgene potently inhibits DNA binding for both wild-type (WT) TRalpha and TRbeta. We used UCP3 and MTE-1 as principal reporters and analyzed gene expression from hearts of transgenic (MUT) and nontransgenic (WT) littermates 6 h after receiving either specific PPARalpha ligand (WY-14643) or vehicle. Interactions were determined through qRT-PCR analyses, and the extent of these interactions across multiple genes was determined using expression arrays. In the basal state, we detected no differences between groups for protein content for UCP3, PPARalpha, TRalpha2, RXRbeta, or PGC-1alpha. However, protein content for TRalpha1 and the PPARalpha heterodimeric partner RXRalpha was diminished in MUT, whereas PPARbeta increased. We demonstrated cross-talk between PPAR and TR for multiple genes, including the reporters UCP3 and MTE1. WY-14643 induced a twofold increase in UCP3 gene expression that was totally abrogated in MUT. We demonstrated variable cross-talk patterns, indicating that multiple mechanisms operate according to individual target genes. The non-ligand-binding TRbeta1 mutation alters expression for multiple nuclear receptors, providing a novel mechanism for interaction that has not been previously demonstrated. These results indicate that therapeutic response to PPARalpha ligands may be determined by thyroid hormone state and TR function.

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Sample size for detecting differentially expressed genes in microarray experiments.

BACKGROUND: Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human). RESULTS: We hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile. CONCLUSIONS: Factors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

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