Glucose homeostasis is tightly regulated to meet the energy requirements of the vital organs and maintain an individual's health. The liver has a major role in the control of glucose homeostasis by controlling various pathways of glucose metabolism, including glycogenesis, glycogenolysis, glycolysis and gluconeogenesis. Both the acute and chronic regulation of the enzymes involved in the pathways are required for the proper functioning of these complex interwoven systems. Allosteric control by various metabolic intermediates, as well as post-translational modifications of these metabolic enzymes constitute the acute control of these pathways, and the controlled expression of the genes encoding these enzymes is critical in mediating the longer-term regulation of these metabolic pathways. Notably, several key transcription factors are shown to be involved in the control of glucose metabolism including glycolysis and gluconeogenesis in the liver. In this review, we would like to illustrate the current understanding of glucose metabolism, with an emphasis on the transcription factors and their regulators that are involved in the chronic control of glucose homeostasis.

Liver plays a crucial role in controlling energy homeostasis in mammals, although the exact mechanism by which it influences other peripheral tissues has yet to be addressed. Here we show that Creb regulates transcriptional co-activator (Crtc) 2 is a major regulator of whole-body energy metabolism. Crtc2 liver-specific knockout lowers blood glucose levels with improved glucose and insulin tolerance. Liver-specific knockout mice display increased energy expenditure with smaller lipid droplets in adipose depots. Both plasma and hepatic Fgf21 levels are increased in Crtc2 liver-specific knockout mice, as a result of the reduced miR-34a expression regulated by Creb/Crtc2 and the induction of Sirt1 and Pparα. Ectopic expression of miR-34a reverses the metabolic changes in knockout liver. We suggest that Creb/Crtc2 negatively regulates the Sirt1/Pparα/Fgf21 axis via the induction of miR-34a under diet-induced obesity and insulin-resistant conditions.

Fasting glucose homeostasis is maintained in part through cAMP (adenosine 3',5'-monophosphate)-dependent transcriptional control of hepatic gluconeogenesis by the transcription factor CREB (cAMP response element-binding protein) and its coactivator CRTC2 (CREB-regulated transcriptional coactivator 2). We showed that PRMT6 (protein arginine methyltransferase 6) promotes fasting-induced transcriptional activation of the gluconeogenic program involving CRTC2. Mass spectrometric analysis indicated that PRMT6 associated with CRTC2. In cells, PRMT6 mediated asymmetric dimethylation of multiple arginine residues of CRTC2, which enhanced the association of CRTC2 with CREB on the promoters of gluconeogenic enzyme-encoding genes. In mice, ectopic expression of PRMT6 promoted higher blood glucose concentrations, which were associated with increased expression of genes encoding gluconeogenic factors, whereas knockdown of hepatic PRMT6 decreased fasting glycemia and improved pyruvate tolerance. The abundance of hepatic PRMT6 was increased in mouse models of obesity and insulin resistance, and adenovirus-mediated depletion of PRMT6 restored euglycemia in these mice. We propose that PRMT6 is involved in the regulation of hepatic glucose metabolism in a CRTC2-dependent manner.

Purpose Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. Materials and Methods The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland–Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. Results The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. Conclusions PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.