The transition between morula and blastocyst stage during preimplantation development represents the first differentiation event of embryogenesis. Morula cells undergo the first cellular specialization and produce two well-defined populations of cells, the trophoblast and the inner cell mass (ICM). Embryonic stem cells (ESCs) with unlimited self-renewal capacity are believed to represent the in vitro counterpart of the ICM. Both mouse and rat ESCs can be derived from the ICM cells, but their in vitro stability differs. In this study we performed a microarray analysis in which we compared the transcriptome of mouse and rat morula, blastocyst, and ICM. This cross-species comparison represents a good model for understanding the differences in derivation and cultivation of ESCs observed in the two species. In order to identify alternative regulation of important molecular mechanisms the investigation of differential gene expression between the two species was extended at the level of signaling pathways, gene families, and single selected genes of interest. Some of the genes differentially expressed between the two species are already known to be important factors in the maintenance of pluripotency in ESCs, like for example Sox2 or Stat3, or play a role in reprogramming somatic cells to pluripotency like c-Myc, Klf4 and p53 and therefore represent interesting candidates to further analyze in vitro in the rat ESCs. This is the first study investigating the gene expression changes during the transition from morula to blastocyst in the rat preimplantation development. Our data show that in the pluripotent pool of cells of the rat and mouse preimplantation embryo substantial differential regulation of genes is present, which might explain the difficulties observed for the derivation and culture of rat ESCs using mouse conditions.

The peroxisome proliferator-activated receptor γ coactivator 1 (PGC-1) proteins are key regulators of cellular bioenergetics and are accordingly expressed in tissues with a high energetic demand. For example, PGC-1α and PGC-1β control organ function of brown adipose tissue, heart, brain, liver and skeletal muscle. Surprisingly, despite their prominent role in the control of mitochondrial biogenesis and oxidative metabolism, expression and function of the PGC-1 coactivators in the retina, an organ with one of the highest energy demands per tissue weight, are completely unknown. Moreover, the molecular mechanisms that coordinate energy production with repair processes in the damaged retina remain enigmatic. In the present study, we thus investigated the expression and function of the PGC-1 coactivators in the healthy and the damaged retina. We show that PGC-1α and PGC-1β are found at high levels in different structures of the mouse retina, most prominently in the photoreceptors. Furthermore, PGC-1α knockout mice suffer from a striking deterioration in retinal morphology and function upon detrimental light exposure. Gene expression studies revealed dysregulation of all major pathways involved in retinal damage and apoptosis, repair and renewal in the PGC-1α knockouts. The light-induced increase in apoptosis in vivo in the absence of PGC-1α was substantiated in vitro, where overexpression of PGC-1α evoked strong anti-apoptotic effects. Finally, we found that retinal levels of PGC-1 expression are reduced in different mouse models for retinitis pigmentosa. We demonstrate that PGC-1α is a central coordinator of energy production and, importantly, all of the major processes involved in retinal damage and subsequent repair. Together with the observed dysregulation of PGC-1α and PGC-1β in retinitis pigmentosa mouse models, these findings thus imply that PGC-1α might be an attractive target for therapeutic approaches aimed at retinal degeneration diseases.

Background

The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

Results

A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics.

Conclusion

Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.