In acute cold stress in mammals, JMJD1A, a histone H3 lysine 9 (H3K9) demethylase, upregulates thermogenic gene expressions through β-adrenergic signaling in brown adipose tissue (BAT). Aside BAT-driven thermogenesis, mammals have another mechanism to cope with long-term cold stress by inducing the browning of the subcutaneous white adipose tissue (scWAT). Here, we show that this occurs through a two-step process that requires both β-adrenergic-dependent phosphorylation of S265 and demethylation of H3K9me2 by JMJD1A. The histone demethylation-independent acute Ucp1 induction in BAT and demethylation-dependent chronic Ucp1 expression in beige scWAT provides complementary molecular mechanisms to ensure an ordered transition between acute and chronic adaptation to cold stress. JMJD1A mediates two major signaling pathways, namely, β-adrenergic receptor and peroxisome proliferator-activated receptor-γ (PPARγ) activation, via PRDM16-PPARγ-P-JMJD1A complex for beige adipogenesis. S265 phosphorylation of JMJD1A, and the following demethylation of H3K9me2 might prove to be a novel molecular target for the treatment of metabolic disorders, via promoting beige adipogenesis.

Recent epidemiological studies showed that coffee consumption is associated with a lower risk of type 2 diabetes, presumably due to suppression of excess fat accumulation in adipocytes. However, the mechanism underlying the effect of coffee on adipocyte differentiation has not been well documented. To elucidate the mechanism, we investigated the effect of coffee on the differentiation of mouse preadipocyte 3T3-L1 cells. Coffee reduced the accumulation of lipids during adipocytic differentiation of 3T3-L1 cells. At 5% coffee, the accumulation of lipids decreased to half that of the control. Coffee also inhibited the expression of the peroxisome proliferator-activated receptor γ (PPARγ), a transcription factor controlling the differentiation of adipocytes. Furthermore, coffee reduced the expression of other differentiation marker genes, aP2, adiponectin, CCAAT-enhancer-binding protein α (C/EBPα), glucose transporter 4 (GLUT4), and lipoprotein lipase (LPL), during adipocyte differentiation. Major bioactive constituents in coffee extracts, such as caffeine, trigonelline, chlorogenic acid, and caffeic acid, showed no effect on PPARγ gene expression. The inhibitory activity was produced by the roasting of the coffee beans.

In eukaryotes, the carboxy-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) is composed of tandem repeats of the heptapeptide YSPTSPS, which is subjected to reversible phosphorylation at Ser2, Ser5, and Ser7 during the transcription cycle. Dynamic changes in CTD phosphorylation patterns, established by the activities of multiple kinases and phosphatases, are responsible for stage-specific recruitment of various factors involved in RNA processing, histone modification, and transcription elongation/termination. Yeast Ssu72, a CTD phosphatase specific for Ser5 and Ser7, functions in 3′-end processing of pre-mRNAs and in transcription termination of small non-coding RNAs such as snoRNAs and snRNAs. Vertebrate Ssu72 exhibits Ser5- and Ser7-specific CTD phosphatase activity in vitro, but its roles in gene expression and CTD dephosphorylation in vivo remain to be elucidated. To investigate the functions of vertebrate Ssu72 in gene expression, we established chicken DT40 B-cell lines in which Ssu72 expression was conditionally inactivated. Ssu72 depletion in DT40 cells caused defects in 3′-end formation of U2 and U4 snRNAs and GAPDH mRNA. Surprisingly, however, Ssu72 inactivation increased the efficiency of 3′-end formation of non-polyadenylated replication-dependent histone mRNA. Chromatin immunoprecipitation analyses revealed that Ssu72 depletion caused a significant increase in both Ser5 and Ser7 phosphorylation of the Pol II CTD on all genes in which 3′-end formation was affected. These results suggest that vertebrate Ssu72 plays positive roles in 3′-end formation of snRNAs and polyadenylated mRNAs, but negative roles in 3′-end formation of histone mRNAs, through dephosphorylation of both Ser5 and Ser7 of the CTD.

Concomitant with the development of deep learning, brain–computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested via within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model via subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.

Objectives To reveal that nonprimary motor-related areas located in the upper stream of the sensorimotor area are associated with self-regulated local electroencephalogram changes in the sensorimotor area during motor tasks. Methods Among healthy participants, we measured the gating of somatosensory-evoked potentials (SEPs) in nonprimary motor-related areas and the sensorimotor area, and event-related desynchronisation, which reflects the excitability changes of the neurons localised in the sensorimotor area during motor execution and imagery. Results We confirmed significant correlations between beta-band event-related desynchronisation and the somatosensory-evoked potential gating of frontal N30 during motor imagery and execution (motor imagery: r = 0.723; P < 0.05; motor execution: r = 0.873; P < 0.05), and nonsignificant correlations between beta-band event-related desynchronisation and the somatosensory-evoked potential gating of N20 (motor imagery: r = 0.079; P > 0.05; motor execution: r = 0.449; P > 0.05). Conclusions The N30 gating of SEPs, with which the beta-band event-related desynchronisation was associated, implies that they modulate sensory input to the supplementary motor area/premotor cortex during motor tasks, the beta-band self-regulated local electroencephalogram changes in the sensorimotor area related sensory input to the supplementary motor area/premotor cortex, and not to primary sensory area derived from N20 gating. This study suggests that some motor commands are derived from sensory gating in the supplementary motor area/premotor cortex.