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Abstract Chronic hepatitis B virus (HBV) infection is a major medical concern worldwide. Current treatments for HBV infection effectively inhibit virus replication; however, these treatments cannot cure HBV and novel treatment-strategies should be necessary. In this study, we identified tripartite motif-containing protein 26 (TRIM26) could be a supportive factor for HBV replication. Small interfering RNA-mediated TRIM26 knockdown (KD) modestly attenuated HBV replication in human hepatocytes. Endogenous TRIM26 physically interacted with HBV core protein (HBc), but not polymerase and HBx, through the TRIM26 SPRY domain. Unexpectedly, TRIM26 inhibited HBc ubiquitination even though TRIM26 is an E3 ligase. HBc was degraded by TRIM26 KD in Huh-7 cells, whereas the reduction was restored by a proteasome inhibitor. RING domain-deleted TRIM26 mutant (TRIM26ΔR), a dominant negative form of TRIM26, sequestered TRIM26 from HBc, resulting in promoting HBc degradation. Taking together, this study demonstrated that HBV utilizes TRIM26 to avoid the proteasome-dependent HBc degradation. The interaction between TRIM26 and HBc might be a novel therapeutic target against HBV infection.

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

Abstract Background Homeostatic regulation of sleep is reflected in the maintenance of a daily balance between sleep and wakefulness. Although numerous internal and external factors can influence sleep, it is unclear whether and to what extent the process that keeps track of time spent awake is determined by the content of the waking experience. We hypothesised that alterations in environmental conditions may elicit different types of wakefulness, which will in turn influence both the capacity to sustain continuous wakefulness as well as the rates of accumulating sleep pressure. To address this, we compared the effects of repetitive behaviours such as voluntary wheel running or performing a simple touchscreen task, with wakefulness dominated by novel object exploration, on sleep timing and EEG slow-wave activity (SWA) during subsequent NREM sleep. Results We find that voluntary wheel running is associated with higher wake EEG theta-frequency activity and results in longer wake episodes, as compared with exploratory behaviour; yet, it does not lead to higher levels of EEG SWA during subsequent NREM sleep in either the frontal or occipital derivation. Furthermore, engagement in a touchscreen task, motivated by food reward, results in lower SWA during subsequent NREM sleep in both derivations, as compared to exploratory wakefulness, even though the total duration of wakefulness is similar. Conclusion Overall, our study suggests that sleep-wake behaviour is highly flexible within an individual and that the homeostatic processes that keep track of time spent awake are sensitive to the nature of the waking experience. We therefore conclude that sleep dynamics are determined, to a large degree, by the interaction between the organism and the environment.