Significance Insufficient sleep and circadian rhythm disruption are associated with negative health outcomes, but the mechanisms involved remain largely unexplored. We show that one wk of insufficient sleep alters gene expression in human blood cells, reduces the amplitude of circadian rhythms in gene expression, and intensifies the effects of subsequent acute total sleep loss on gene expression. The affected genes are involved in chromatin remodeling, regulation of gene expression, and immune and stress responses. The data imply molecular mechanisms mediating the effects of sleep loss on health and highlight the interrelationships between sleep homeostasis, circadian rhythmicity, and metabolism.

Circadian organization of the mammalian transcriptome is achieved by rhythmic recruitment of key modifiers of chromatin structure and transcriptional and translational processes. These rhythmic processes, together with posttranslational modification, constitute circadian oscillators in the brain and peripheral tissues, which drive rhythms in physiology and behavior, including the sleep-wake cycle. In humans, sleep is normally timed to occur during the biological night, when body temperature is low and melatonin is synthesized. Desynchrony of sleep-wake timing and other circadian rhythms, such as occurs in shift work and jet lag, is associated with disruption of rhythmicity in physiology and endocrinology. However, to what extent mistimed sleep affects the molecular regulators of circadian rhythmicity remains to be established. Here, we show that mistimed sleep leads to a reduction of rhythmic transcripts in the human blood transcriptome from 6.4% at baseline to 1.0% during forced desynchrony of sleep and centrally driven circadian rhythms. Transcripts affected are key regulators of gene expression, including those associated with chromatin modification (methylases and acetylases), transcription (RNA polymerase II), translation (ribosomal proteins, initiation, and elongation factors), temperature-regulated transcription (cold inducible RNA-binding proteins), and core clock genes including CLOCK and ARNTL (BMAL1). We also estimated the separate contribution of sleep and circadian rhythmicity and found that the sleep-wake cycle coordinates the timing of transcription and translation in particular. The data show that mistimed sleep affects molecular processes at the core of circadian rhythm generation and imply that appropriate timing of sleep contributes significantly to the overall temporal organization of the human transcriptome.bloodomics | chronobiology | microarray | genomics | biological rhythms T wenty-four-hour rhythms in physiology and behavior are generated through interaction between environmental cycles and endogenous self-sustained circadian oscillators (1). In mammals, circadian oscillators are present in the brain and most peripheral tissues (2). Circadian rhythmicity within cells in these tissues is generated by a molecular oscillator, which is composed of core transcriptional-translational feedback loops that can also interact with metabolic oscillators (3).The circadian regulation of the mammalian transcriptome includes circadian transcriptional and translational regulation by proteins such as the positive transcription factors CLOCK and ARNTL (BMAL1), and the repressors PERIOD (PER) and CRYPTOCHROME (CRY) (4), chromatin modification by factors such as E1A binding protein P300 (EP300) and the methyltransferase MLL3 (4-6), RNA polymerase activity (4, 7), and posttranscriptional events such as the regulation of ribosome biogenesis and translation (8, 9). Furthermore, physiological factors such as body temperature (10) and endocrine rhythms such as cortisol (11) can modify and reinforce these regulat...

Background: The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger.

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