Attainment of a brown adipocyte cell phenotype in white adipocytes, with their abundant mitochondria and increased energy expenditure potential, is a legitimate strategy for combating obesity. The unique transcriptional regulators of the primary brown adipocyte phenotype are unknown, limiting our ability to promote brown adipogenesis over white. In the present work, we used microarray analysis strategies to study primary preadipocytes, and we made the striking discovery that brown preadipocytes demonstrate a myogenic transcriptional signature, whereas both brown and white primary preadipocytes demonstrate signatures distinct from those found in immortalized adipogenic models. We found a plausible SIRT1-related transcriptional signature during brown adipocyte differentiation that may contribute to silencing the myogenic signature. In contrast to brown preadipocytes or skeletal muscle cells, white preadipocytes express Tcf21, a transcription factor that has been shown to suppress myogenesis and nuclear receptor activity. In addition, we identified a number of developmental genes that are differentially expressed between brown and white preadipocytes and that have recently been implicated in human obesity. The interlinkage between the myocyte and the brown preadipocyte confirms the distinct origin for brown versus white adipose tissue and also represents a plausible explanation as to why brown adipocytes ultimately specialize in lipid catabolism rather than storage, much like oxidative skeletal muscle tissue. microarray ͉ myocyte ͉ principal component analysis ͉ differentiation ͉ transcriptome

Mutations in PINK1 cause the mitochondrial-related neurodegenerative disease Parkinson's. Here we investigate whether obesity, type 2 diabetes, or inactivity alters transcription from the PINK1 locus. We utilized a cDNA-array and quantitative real-time PCR for gene expression analysis of muscle from healthy volunteers following physical inactivity, and muscle and adipose tissue from nonobese or obese subjects with normal glucose tolerance or type 2 diabetes. Functional studies of PINK1 were performed utilizing RNA interference in cell culture models. Following inactivity, the PINK1 locus had an opposing regulation pattern (PINK1 was down-regulated while natural antisense PINK1 was up-regulated). In type 2 diabetes skeletal muscle, all transcripts from the PINK1 locus were suppressed and gene expression correlated with diabetes status. RNA interference of PINK1 in human neuronal cell lines impaired basal glucose uptake. In adipose tissue, mitochondrial gene expression correlated with PINK1 expression although remained unaltered following siRNA knockdown of Pink1 in primary cultures of brown preadipocytes. In conclusion, regulation of the PINK1 locus, previously linked to neurodegenerative disease, is altered in obesity, type 2 diabetes and inactivity, while the combination of RNAi experiments and clinical data suggests a role for PINK1 in cell energetics rather than in mitochondrial biogenesis.

Proliferation-related gene signatures have been proposed to aid breast cancer management by providing reproducible prognostic and predictive information on a patient-by-patient basis. It is unclear however, whether a less demanding assessment of cell division rate (as determined in clinical setting by expression of Ki67) can function in place of gene profiling.We investigated agreement between literature-, distribution-based, as well as signaturederived values for Ki67, relative to the genomic grade index (GGI), 70-gene signature, p53 signature, recurrence score (RS), and the molecular subtype models of Sorlie, Hu, and Parker in representative sets of 253 and 159 breast cancers with a median follow-up of 13 and 14.5 years, respectively. The relevance for breast cancer specific survival was also addressed in uni-and bivariate Cox models.Taking both cohorts into account, our broad approach identified ROC optimized Ki67 cutoffs in the range of 8e28%. With optimum signature-reproducing cutoffs, similarity in classification of individual tumors was higher for binary signatures (72e85%), than multi-level signatures (67e73%). Consistent with strong agreement, no prognostic superiority was noted for either Ki67 or the binary GGI, 70-gene and p53 signatures in the Uppsala dataset by bivariate analyses. In contrast, Ki67-independent prognostic capacity could be demonstrated for RS and molecular subtypes according to Sorlie, Hu and Parker in both datasets.Our results show that the added prognostic value of binary proliferation-related gene signatures is limited for Ki67-assessed breast cancers. More complex, multi-level descriptions have a greater potential in short-and long-term prognostication for biologically relevant breast cancer subgroups.ª 2014 Federation of European Biochemical Societies.Published by Elsevier B.V. All rights reserved.

Microarrays enable high-throughput parallel gene expression analysis, and their use has grown exponentially during the past decade. We are now in a position where individual experiments could benefit from using the swelling public data repositories to allow microarrays to progress from being a hypothesis-generating tool to a powerful resource that can be used to test hypothesis about biology. Comparative microarray analysis could better distinguish phenotypes from associated phenotypes; identify valid differentially expressed genes by combining many studies; test new hypothesis; and discover fundamental patterns of gene regulation. This review aims to describe the additional methodology needed for such comparative microarray analysis, and we identify and discuss a number of problems such as loss of published data, lack of annotations, and variable array quality, which need to be solved before comparative microarray analysis can be used in a more systematic and powerful manner.