Activation of the mesolimbic dopamine reward pathway by acute ethanol produces reinforcement and changes in gene expression that appear to be crucial to the molecular basis for adaptive behaviors and addiction. The inbred mouse strains DBA/2J and C57BL/6J exhibit contrasting acute behavioral responses to ethanol. We used oligonucleotide microarrays and bioinformatics methods to characterize patterns of gene expression in three brain regions of the mesolimbic reward pathway of these strains. Expression profiling included examination of both differences in gene expression 4 h after saline injection or acute ethanol (2 g/kg). Using a rigorous stepwise method for microarray analysis, we identified 788 genes differentially expressed in control DBA/2J versus C57BL/6J mice and 307 ethanolregulated genes in the nucleus accumbens, prefrontal cortex, and ventral tegmental area. There were strikingly divergent patterns of ethanol-responsive gene expression in the two strains. Ethanol-responsive genes also showed clustering at discrete chromosomal regions, suggesting local chromatin effects in regulation. Ethanol-regulated genes were generally related to neuroplasticity, but regulation of discrete functional groups and pathways was brain region specific: glucocorticoid signaling, neurogenesis, and myelination in the prefrontal cortex; neuropeptide signaling and developmental genes, including factor Bdnf, in the nucleus accumbens; and retinoic acid signaling in the ventral tegmental area. Bioinformatics analysis identified several potential candidate genes for quantitative trait loci linked to ethanol behaviors, further supporting a role for expression profiling in identifying genes for complex traits. Brain regionspecific changes in signaling and neuronal plasticity may be critical components in development of lasting ethanol behavioral phenotypes such as dependence, sensitization, and craving.

Heart muscle is metabolically versatile, converting energy stored in fatty acids, glucose, lactate, amino acids, and ketone bodies. Here, we use mouse models in ketotic nutritional states (24 h of fasting and a very low carbohydrate ketogenic diet) to demonstrate that heart muscle engages a metabolic response that limits ketone body utilization. Pathway reconstruction from microarray data sets, gene expression analysis, protein immunoblotting, and immunohistochemical analysis of myocardial tissue from nutritionally modified mouse models reveal that ketotic states promote transcriptional suppression of the key ketolytic enzyme, succinyl-CoA:3-oxoacid CoA transferase (SCOT; encoded by Oxct1), as well as peroxisome proliferatoractivated receptor ␣-dependent induction of the key ketogenic enzyme HMGCS2. Consistent with reduction of SCOT, NMR profiling demonstrates that maintenance on a ketogenic diet causes a 25% reduction of myocardial 13 C enrichment of glutamate when 13 C-labeled ketone bodies are delivered in vivo or ex vivo, indicating reduced procession of ketones through oxidative metabolism. Accordingly, unmetabolized substrate concentrations are higher within the hearts of ketogenic diet-fed mice challenged with ketones compared with those of chow-fed controls. Furthermore, reduced ketone body oxidation correlates with failure of ketone bodies to inhibit fatty acid oxidation. These results indicate that ketotic nutrient environments engage mechanisms that curtail ketolytic capacity, controlling the utilization of ketone bodies in ketotic states.The mammalian heart must maintain constant levels of ATP to perform its mechanical and electrical functions. A variety of experimental approaches have established that cardiomyopathy is associated with changes in cardiac energy metabolism and that altered energy metabolism can cause cardiomyopathy (1-5). In the normal adult heart, mitochondrial oxidative phosphorylation provides more than 95% of the ATP generated. Substrate utilization is dynamic, and metabolic flexibility under differing physiological conditions is an important adaptive property of myocardium (6 -8). Adaptations over time are

Gene expression array technology has reached the stage of being routinely used to study clinical samples in search of diagnostic and prognostic biomarkers. Due to the nature of array experiments, which examine the expression of tens of thousands of genes simultaneously, the number of null hypotheses is large. Hence, multiple testing correction is often necessary to control the number of false positives. However, multiple testing correction can lead to low statistical power in detecting genes that are truly differentially expressed. Filtering out non-informative genes allows for reduction in the number of null hypotheses. While several filtering methods have been suggested, the appropriate way to perform filtering is still debatable. We propose a new filtering strategy for Affymetrix GeneChips®, based on principal component analysis of probe-level gene expression data. Using a wholly defined spike-in data set and one from a diabetes study, we show that filtering by the proportion of variation accounted for by the first principal component (PVAC) provides increased sensitivity in detecting truly differentially expressed genes while controlling false discoveries. We demonstrate that PVAC exhibits equal or better performance than several widely used filtering methods. Furthermore, a data-driven approach that guides the selection of the filtering threshold value is also proposed.

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We report here the transcriptional responses in Saccharomyces cerevisiae to deletion of the RNH201 gene encoding the catalytic subunit of RNase H2. Deleting RNH201 alters RNA expression of 349 genes by ≥1.5-fold (q-value <0.01), of which 123 are upregulated and 226 are downregulated. Differentially expressed genes (DEGs) include those involved in stress responses and genome maintenance, consistent with a role for RNase H2 in removing ribonucleotides incorporated into DNA during replication. Upregulated genes include several that encode subunits of RNA polymerases I and III, and genes involved in ribosomal RNA processing, ribosomal biogenesis and tRNA modification and processing, supporting a role for RNase H2 in resolving R-loops formed during transcription of rRNA and tRNA genes. A role in R-loop resolution is further suggested by a higher average GC-content proximal to the transcription start site of downregulated as compared to upregulated genes. Several DEGs are involved in telomere maintenance, supporting a role for RNase H2 in resolving RNA-DNA hybrids formed at telomeres. A large number of DEGs encode nucleases, helicases and genes involved in response to dsRNA viruses, observations that could be relevant to the nucleic acid species that elicit an innate immune response in RNase H2-defective humans.