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The new type 2 diabetes drug, dapagliflozin, reduces blood glucose levels and body weight by inhibiting sodium glucose transporter 2 (SGLT2) in proximal tubular cells. SGLT2 inhibitors might modulate glucose influx into renal tubular cells, thereby regulating the metabolic conditions that cause endoplasmic reticulum (ER) stress in the cells. In this study, we examined the effect of dapagliflozin on ER stress in the HK-2 proximal tubular cell line and in the kidney of db/db mice to characterise its function in diabetic nephropathy (DN). We found that dapagliflozin regulated ER stress-mediated apoptosis in vitro and in vivo . Only the elf2α-ATF4-CHOP pathway was regulated under these conditions. Notably, the drug rescued C2 ceramide-induced ER stress-mediated apoptosis and ER stress-mediated apoptosis, which might occur in DN, in db/db mice. Our study shows a novel role for dapagliflozin as an inhibitor of ER stress and suggests that dapagliflozin might be useful for the prevention of DN.

[This corrects the article DOI: 10.3389/fncel.2018.00490.].

Dapagliflozin, empagliflozin, tofogliflozin, selective inhibitors of sodium-glucose cotransporter 2 (SGLT2), is used clinically to reduce circulation glucose levels in patients with type 2 diabetes mellitus by blocking the reabsorption of glucose by the kidneys. Dapagliflozin is metabolized and inactivated by UGT1A9. Empagliflozin is metabolized and inactivated by UGT1A9 and by other related isoforms UGT2B7, UGT1A3, and UGT1A8. Tofogliflozin is metabolized and inactivated by five different enzymes CYP2C18, CYP3A4, CYP3A5, CYP4A11, and CYP4F3. Dapagliflozin treatment of HCT116 cells, which express SGLT2 but not UGT1A9, results in the loss of cell adhesion, whereas HepG2 cells, which express both SGLT2 and UGT1A9, are resistant to the adhesion-related effects of dapagliflozin. PANC-1 and H1792 cells, which do not express either SGLT2 or UGT1A9, are also resistant to adhesion related effects of dapagliflozin. On the other hand, either empagliflozin or tofogliflozin treatment of HCT116, HepG2, PANC-1, and H1792 cells are resistant to the adhesion-related effects as observed in dapagliflozin treated HCT116 cells. Knockdown of UGT1A9 by shRNA in HepG2 cells increased dapagliflozin sensitivity, whereas the overexpression of UGT1A9 in HCT116 cells protected against dapagliflozin-dependent loos of cell adhesion. Dapagliflozin treatment had no effect on cellular interactions with fibronectin, vitronectin, or laminin, but it induced a loss of interaction with collagen I and IV. In parallel, dapagliflozin treatment reduced protein levels of the full-length discoidin domain receptor I (DDR1), concomitant with appearance of DDR1 cleavage products and ectodomain shedding of DDR1. In line with these observations, unmetabolized dapagliflozin increased ADAM10 activity. Dapagliflozin treatment also significantly reduced Y792 tyrosine phosphorylation of DDR1 leading to decrement of DDR1 function and detachment of cancer cells. Concomitant with these lines of results, we experienced that CEA in patients with colon cancer, which express SGLT2 but not UGT1A9, and type 2 diabetes mellitus treated by dapagliflozin in addition to chemotherapy was decreased (case 1). CEA in patients with colon cancer, which express SGLT2 but not UGT1A9, and type 2 diabetes mellitus was treated by dapagliflozin alone after radiation therapy was decreased but started to rise after cessation of dapagliflozin (case 2). CA19-9 in two of patients with pancreatic cancer and type 2 diabetes mellitus was resistant to the combination therapy of dapagliflozin and chemotherapy (case 3 and 4 respectively). PIVKAII in patients with liver cancer and type 2 diabetes mellitus, and CYFRA in patients with squamous lung cancer and type 2 diabetes mellitus was also resistant the combination therapy of dapagliflozin and chemotherapy (case 5 and 6 respectively). Taken together, these data suggest a potential role for dapagliflozin anticancer therapy against colon cancer cells that express SGLT2, but not UGT1A9.

Brief refeeding times (~60 min) enhanced hepatic Angptl8 expression in fasted mice. We cloned the mouse Angptl8 promoter region to characterise this rapid refeeding-induced increase in hepatic Angptl8 expression. Deletion of the −309/−60 promoter region significantly attenuated basal promoter activity in hepatocytes. A computational motif search revealed a potential binding motif for hepatocyte nuclear factor 1α/1β (HNF-1α/β) at −84/−68 bp of the promoter. Mutation of the HNF-1 binding site significantly decreased the promoter activity in hepatocytes, and the promoter carrying the mutated HNF-1 site was not transactivated by co-transfection of HNF-1 in a non-hepatic cell line. Silencing Hnf-1 in hepatoma cells and mouse primary hepatocytes reduced Angptl8 protein levels. Electrophoretic mobility-shift assays confirmed direct binding of Hnf-1 to its Angptl8 promoter binding motif. Hnf-1α expression levels increased after short-term refeeding, paralleling the enhanced in vivo expression of the Angptl8 protein. Chromatin immunoprecipitation (ChIP) confirmed the recruitment of endogenous Hnf-1 to the Angptl8 promoter region. Insulin-treated primary hepatocytes showed increased expression of Angptl8 protein, but knockdown of Hnf-1 completely abolished this enhancement. HNF-1 appears to play essential roles in the rapid refeeding-induced increases in Angptl8 expression. HNF-1α may therefore represent a primary medical target for ANGPTL8-related metabolic abnormalities. The study revealed the transcriptional regulation of the mouse hepatic Angptl8 gene by HNF-1.

Background Approximately 2.4 million patients in Japan would benefit from treatment for thyroid disease, including Graves’ disease and Hashimoto’s disease. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to develop and conduct preliminary testing on a machine learning method for screening patients with hyperthyroidism and hypothyroidism who would benefit from prompt medical treatment. Methods We collected electronic medical records and medical checkup data from four hospitals in Japan. We applied four machine learning algorithms to construct classification models to distinguish patients with hyperthyroidism and hypothyroidism from control subjects using routine laboratory tests. Performance evaluation metrics such as sensitivity, specificity, and the area under receiver operating characteristic (AUROC) were obtained. Techniques such as feature importance were further applied to understand the contribution of each feature to the machine learning output. Results The results of cross-validation and external evaluation indicated that we achieved high classification accuracies (AUROC = 93.8% for hyperthyroidism model and AUROC = 90.9% for hypothyroidism model). Serum creatinine (S-Cr), mean corpuscular volume (MCV), and total cholesterol were the three features that were most strongly correlated with the hyperthyroidism model, and S-Cr, lactic acid dehydrogenase (LDH), and total cholesterol were correlated with the hypothyroidism model. Conclusions We demonstrated the potential of machine learning approaches for diagnosing the presence of thyroid dysfunction from routine laboratory tests. Further validation, including prospective clinical studies, is necessary prior to application of our method in the clinic. Plain language summary Thyroid dysfunction, including over-activity of the thyroid (hyperthyroidism) and under-activity (hypothyroidism), is a leading hormonal disorder with major health implications, and many patients with thyroid dysfunction are not formally diagnosed. We aimed to develop a computer-based approach for screening patients for hyperthyroidism and hypothyroidism who might benefit from medical treatment. We used routine laboratory data and specialised computer software to detect thyroid dysfunction in patients. We show that we can do this accurately, and so might be able to use our system to screen patients based on data from routine medical tests. Further testing in clinical studies is needed, however, before our approach can be used in the clinic. Hu et al. develop a machine learning approach for diagnosis of thyroid disorders based on routine clinical laboratory measurements. The approach is tested on data from multiple institutions and can distinguish patients with hyper- or hypothyroidism from controls with high accuracy.

Gene regulation systems are mimicked by simple quantitative detection of non-nucleic acid molecular targets such as protein and metabolite. Here, we describe a one-tube, one-step real-time quantitative detection methodology for isothermal signal amplification of those targets. Using this system, real-time quantitative detection of thrombin and streptomycin, which were used as examples for protein and metabolite targets, was successfully demonstrated with detection limits of at most 50 pM and 75 nM, respectively. Notably, the dynamic range of target concentrations could be obtained for over four orders of magnitude. Thus, our method is expected to serve as a point-of-care or on-site test for medical diagnosis and food and environmental hygiene.

Thyrotropin-releasing hormone (TRH) regulates various physiological activities through activation of receptors expressed in a broad range of cells in the central nervous system. The cerebellum expresses TRH receptors in granule cells and molecular layer interneurons. However, the function of TRH in the cerebellum remains to be clarified. Here, using TRH knockout (KO) mice we studied the role of TRH in the cerebellum. Immunohistochemistry showed no gross morphological differences between KO mice and wild-type (WT) littermates in the cerebellum. In the rotarod test, the initial performance of KO mice was comparable to that of WT littermates, but the learning speed of KO mice was significantly lower than that of WT littermates, suggesting impaired motor learning. The motor learning deficit in KO mice was rescued by intraperitoneal injection of TRH. Electrophysiology revealed absence of long-term depression (LTD) at parallel fiber-Purkinje cell synapses in KO mice, which was rescued by bath-application of TRH. TRH was shown to increase cyclic guanosine monophosphate (cGMP) content in the cerebellum. Since nitric oxide (NO) stimulates cGMP synthesis in the cerebellum, we examined whether NO-cGMP pathway was involved in TRH-mediated LTD rescue in KO mice. Pharmacological blockade of NO synthase and subsequent cGMP production prevented TRH-induced LTD expression in KO mice, whereas increase in cGMP signal in Purkinje cells by 8-bromoguanosine cyclic 3’,5’-monophosphate, a membrane-permeable cGMP analog, restored LTD without TRH application. These results suggest that TRH is involved in cerebellar LTD presumably by upregulating the basal cGMP level in Purkinje cells, and, consequently, in motor learning.

The family with sequence similarity 83 (FAM83) protein family G (FAM83G) possesses a predicted consensus phosphorylation motif for serine/threonine-protein kinase D1/protein kinase C mu (PKD1/PKCμ) at serine residue 356 (S356). In this study, overexpressed wild-type FAM83G coimmunoprecipitated with PKD1/PKCμ in Chinese hamster ovary (CHO) cells inhibited heat shock protein 27 (HSP27) phosphorylation at S82 and reduced the living cell number. The expression of a FAM83G phosphorylation-resistant mutant (S356A-FAM83G) had no effect on the living cell number or the induction of spontaneous apoptosis. By contrast, the introduction of a synthetic peptide encompassing FAM83G S356 into HCT116 and HepG2 cells decreased HSP27 S15 and S82 phosphorylation and induced spontaneous apoptosis. On the other hand, the introduction of FAM83G phosphorylation-resistant mutant synthesized peptides (S356A-AF-956 and S356A-AG-066) did not reduce the living cell number or induce spontaneous apoptosis. The endogenous expression of HSP27 and FAM83G was apparently greater in HCT116 and HepG2 cells compared with in CHO cells. In various types of lung cancer cell lines, the FAM83G messenger RNA (mRNA) level in non-small lung cancer cells was at a similar level to that in non-cancerous cells. However, the FAM83G mRNA level in the small cell lung cancer cell lines was variable, and the HSP27 mRNA level in FAM83G mRNA-rich types was greater than that in FAM83G mRNA-normal range types. Taken together, these data demonstrate that FAM83G S356 phosphorylation modulates HSP27 phosphorylation and apoptosis regulation and that HSP27 is a counterpart of FAM83G.

摘要 背景 对2型糖尿病患者残余β细胞功能的评估是必不可少的, 因为它不仅指导诊断, 而且指导适当的治疗方式。然而, 尚缺乏方便评估残余β细胞功能的生物标志物。因此, 我们评估了正在接受胰岛素治疗的门诊患者和在4年后接受胰岛素治疗的患者内源性胰岛素水平。 方法 回顾174例门诊2型糖尿病患者(男性占50%), 血糖得到适度控制(HbA1c:7.3%, 5.2%~14.8%)。20例肾小球滤过率低于30mL/min/1.73m2的患者被排除在评估内源性胰岛素水平之外, 评估根据随机C肽指数(C‐CPI)和尿C肽/肌酐比值(UCPCR, 随时测定, 一般在早餐后1~2h)。根据胰岛素治疗的情况对患者进行分层。 结果 胰岛素治疗组的C‐CPI和UCPCR显著低于非胰岛素治疗组(分别为0.9比2.2, p < 0.0001, 24.7和75.5, p = 0.0003)。此外, 4年来需要胰岛素的患者的C‐CPI显著低于不需要胰岛素的患者(1.0比1.7, p= 0.0184)。多变量逻辑回归分析显示, 胰岛素分泌的两个指标都会影响胰岛素治疗的需求, 但 C‐CPI 可以作为最方便和有用的生物标志物, 不仅可以反映当前的胰岛素治疗需求(p = 0.0002), 还可以指导后续胰岛素治疗的需求(p = 0.0008)。 结论 C‐CPI易于测定, 是门诊患者较为实用的指标, 因此, 我们的发现对初级保健具有重要意义。

Autophagy is an evolutionarily conserved catabolic process by which cells degrade intracellular proteins and organelles in the lysosomes and recycle their metabolites. We have recently demonstrated the crucial role for the basal level of autophagic activity in thyrocyte survival and homeostasis using the thyroid-specific autophagy knockout mice. Here, we first studied hormonal regulation of autophagy in thyrocytes in vitro using a rat thyroid cell line PCCl3 and in vivo with mice. In cultured PCCl3 cells, thyroxine decreased microtubule-associated protein 1 light chain 3 (LC3) puncta (a component of autophagosome) and increased p62 (an autophagy substrate) levels, showing thyroxine-suppression of autophagy. In contrast, TSH increased both LC3 puncta and p62 levels, but at the same time stabilized p62 protein by inhibiting p62 degradation, indicating TSH induction of autophagy. Our experiments with various inhibitors identified that both the cAMP-protein kinase (PK) A-cAMP response element binding protein/ERK and PKC signaling pathways regulates positively autophagic activity. The in vivo results obtained with wild-type mice treated with methimazole and perchlorate or thyroxine were consistent with in vitro results. Next, in thyroid-specific autophagy knockout mice treated with methimazole and perchlorate (that is, mice were placed under a stressed condition where enhanced autophagy was required) for 2 months, lower follicle sizes and lower thyroglobulin contents in thyrocytes were observed, suggesting impaired thyroglobulin production presumably from insufficient nutrient supply. We therefore conclude that TSH positively regulates autophagic activity through the cAMP-PKA-cAMP response element binding protein/ERK and PKC signaling pathways, whereas thyroid hormones inhibit its activity in thyrocytes. Metabolites produced by autophagy appear to be necessary for protein synthesis stimulated by TSH.