SummaryAs the premier model organism in biomedical research, the laboratory mouse shares the majority of protein-coding genes with humans, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications, and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of other sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases.

Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.

Recent studies have demonstrated the important enzymatic, structural and regulatory roles of RNA in the cell. Here we present a post-transcriptional regulation system in Escherichia coli that uses RNA to both silence and activate gene expression. We inserted a complementary cis sequence directly upstream of the ribosome binding site in a target gene. Upon transcription, this cis-repressive sequence causes a stem-loop structure to form at the 5'-untranslated region of the mRNA. The stem-loop structure interferes with ribosome binding, silencing gene expression. A small noncoding RNA that is expressed in trans targets the cis-repressed RNA with high specificity, causing an alteration in the stem-loop structure that activates expression. Such engineered riboregulators may lend insight into mechanistic actions of endogenous RNA-based processes and could serve as scalable components of biological networks, able to function with any promoter or gene to directly control gene expression.

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Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

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The level of arousal in mammals is correlated with metabolic state and specific patterns of cortical neuronal responsivity. In particular, rhythmic transitions between periods of high activity (up phases) and low activity (down phases) vary between wakefulness and deep sleep͞anesthesia. Current opinion about changes in cortical response state between sleep and wakefulness is split between neuronal network-mediated mechanisms and neuronal metabolism-related mechanisms. Here, we demonstrate that slow oscillations in network state are a consequence of interactions between both mechanisms. Specifically, recurrent networks of excitatory neurons, whose membrane potential is partly governed by ATPmodulated potassium (KATP) channels, mediate response-state oscillations via the interaction between excitatory network activity involving slow, kainate receptor-mediated events and the resulting activation of ATP-dependent homeostatic mechanisms. These findings suggest that K ATP channels function as an interface between neuronal metabolic state and network responsivity in mammalian cortex.glutamate ͉ slow-wave oscillation ͉ potassium channel ͉ rhythm S low-wave oscillations (SWO) occur in the cerebral cortex and associated areas (1). They are particularly manifest during periods of behavioral quiescence and are an ubiquitous feature of deep sleep (2-5). Two hypotheses dominate the possible functional significance of such activity (6). SWO have been shown to be critical for learning and plasticity (2, 7). Additionally it has been proposed that they allow for, or are generated by, reduced neuronal metabolism, whereby restorative cellular processes take place to reset the deficit induced by a period of wakefulness (8-10). At the cellular level, slow-wave electroencephalogram activity correlates with fluctuations in the membrane potential of cortical neurones (3, 11), where periods of hyperpolarization (down phase) alternate between periods of depolarization (up phase). Such bistable behavior is thought to depend on a balance of recurrent excitation and local inhibition (12, 13) in addition to slow-wave input from the thalamus (14). However, persistent depolarized states might also occur through synaptic excitation alone, with kinetically slow excitatory synaptic potentials (EPSPs) such as those generated by kainate receptors (15) or NMDA receptors (but see below), temporally summating with sufficient background activity (16). In addition, such a maintained depolarization can be generated purely by intrinsic ionic currents in bistable neurons (17).The nature of the relationship between neuronal metabolic state and SWO is also unclear. From a network perspective, a number of synaptic conductances, most notably those mediated by NMDA receptors (18), are modulated by metabolism. However, some neurons have intrinsic conductances specifically designed to sense aspects of metabolic state. During wakefulness, constant neuronal activity is metabolically demanding (8,19). Measurements of cerebral metabolism during slow-wave sleep have de...

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users.