IGF-1 Induction by Acylated Steryl β-Glucosides Found in a Pre-Germinated Brown Rice Diet Reduces Oxidative Stress in Streptozotocin-Induced Diabetes

BACKGROUND: The pathology of diabetic neuropathy involves oxidative stress on pancreatic β-cells, and is related to decreased levels of Insulin-like growth factor 1 (IGF-1). Acylated steryl β-glucoside (PR-ASG) found in pre-germiated brown rice is a bioactive substance exhibiting properties that enhance activity of homocysteine-thiolactonase (HTase), reducing oxidative stress in diabetic neuropathy. The biological importance of PR-ASG in pancreatic β-cells remains unknown. Here we examined the effects of PR-ASG on IGF-1 and glucose metabolism in β-cells exposed to oxidative stress. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, a pre-germinated brown rice (PR)-diet was tested in streptozotocin (STZ)-induced diabetic rats. Compared with diabetic rats fed control diets, the PR-diet fed rats showed an improvement of serum metabolic and neurophysiological parameters. In addition, IGF-1 levels were found to be increased in the serum, liver, and pancreas of diabetic rats fed the PR-diet. The increased IGF-1 level in the pancreas led us to hypothesize that PR-ASG is protective for islet β-cells against the extensive injury of advanced or severe diabetes. Thus we examined PR-ASG to determine whether it showed anti-apoptotic, pro-proliferative effects on the insulin-secreting β-cells line, INS-1; and additionally, whether PR-ASG stimulated IGF-1 autocrine secretion/IGF-1-dependent glucose metabolism. We have demonstrated for the first time that PR-ASG increases IGF-1 production and secretion from pancreatic β-cells. CONCLUSION/SIGNIFICANCE: These findings suggest that PR-ASG may affect pancreatic β-cells through the activation of an IGF-1-dependent mechanism in the diabetic condition. Thus, intake of pre-germinated brown rice may have a beneficial effect in the treatment of diabetes, in particular diabetic neuropathy

Effect of PR-ASG treatment on cellular ROS level and HTase activity in STZ-exposed cells.

<p>A) Initially, cells were incubated under one of these sets of experimental parameters: STZ-negative, PR-ASG-negative (control); 1 mM STZ (STZ group); or 1 mM STZ supplemented with 4 µg/mL of PR-ASG (STZ+PR-ASG). After 1 hr of incubation, the cells were pre-washed with fresh medium, stained with DCF-DA (1.0 µM), and incubated in darkness for 10 min. DCF-DA green fluorescence was viewed under a fluorescence microscope. B) DCF-DA green fluorescing cells were also quantitated by a Perkin-Elmer Victor V multilabel plate reader with excitation/emission filters set at 490 and 535 nm, and expressed as the percent of the total number of cells that exhibit DCA-DA green fluorescence. C) Cells (2.5×10⁵ in 1 mL of 10% serum- containing RPMI 1640 medium well) were cultured in12-well plates. One day later, the cells were divided into 3 groups fro 1 hour of exposure to one of the following in RPMI medium containing 1.0 µg/mL rat LDL; no STZ with no PR-ASG (control); 1 mM STZ (STZ); or 1 mM STZ with 4 µg/mL of PR-ASG (STZ+PR-ASG). Subsequently, cells were pre-washed; cultured for 24 hours in serum-free RPMI-1640 medium with 0 or 4 µg/mL of PR-ASG; harvested; and eventually used for enzyme preparation. The cellular supernatant produced during enzyme preparation was assessed for activity using an Alfresa auto HTLase assay kit (Alfresa Pharma Corp., Osaka, Japan). One unit of HTase activity is defined as 1 nmol of HT hydrolyzed in 1 min by 1 mg of protein. In B) and C), values were expressed as means ± SEM, n = 6 individual experiments, and analyzed by one-way ANOVA followed by Tukey's multiples comparison test and then Dunnet's test. The single asterisk denotes a statistically significant difference between STZ and STZ+PR-ASG (*p<0.01).</p

Final body weights, blood glucose concentrations, and serum parameters in non-diabetic and diabetic rats (3 weeks).

<p>One-way ANOVA was performed to analyze the variation between 3 groups of non-diabetic or diabetic treatment. The data were then analyzed using Tukey's multiples comparison test (if parametric). Values were expressed as means±SEM. Differences between the PR-treated group and other diet groups were analyzed by Dunnet's multiple comparison test (*p<0.05, **p<0.01, ***p<0.001).</p

Suggested mechanism for PR-ASG's protective action against oxidative stress in INS-1 cells.

<p>Reactive oxgen species (ROS) generated from STZ exposure are metabolized and inactivated by SOD, to produce H₂O₂. It has been shown that an imbalance in the coordinated expression/activity of glutathione peroxidase (GSHPx) and glutathione reductase (GR) can cause excessive generation of ROS, leading to oxidative stress. GSHPx converts H₂O₂ to water using glutathione (GSH). Maintenance of the redox state in cells' redox state is controlled by intracellular regulators such as reduced glutathione (GSH) and NADPH. Both GR and glucose-6-phosphate dehydrogenase (G6PD) are enzymes suspected to have protective activity against conditions of oxidative stress. On the other hand, 2-deoxy-glucose (2-DG, an inhibitor of glycolytic pathway) and 6-aminonicotinamide (6-AN, an inhibitor of pentose phosphate pathway) both decrease the cellular levels of pyruvate and NADPH. This leads to an accumulation of H₂O₂, the building of which acts to induce apoptosis. PR-ASG's defensive action is exerted via an increase in two targets: increasing homocysteine-thiolactonase (HTase) enhances homocysteine (Hcy) metabolism; and increasing pyruvate enhances protection against cell via IGF-1-related glucose metabolism.</p

Distinct patterns of copy number alterations may predict poor outcome in central nervous system germ cell tumors

Abstract We have previously reported that 12p gain may predict the presence of malignant components and poor prognosis for CNS germ cell tumor (GCT). Recently, 3p25.3 gain was identified as an independent predictor of poor prognosis for testicular GCT. Eighty-one CNS GCTs were analyzed. Copy number was calculated using methylation arrays. Five cases (6.2%) showed 3p25.3 gain, but only among the 40 non-germinomatous GCTs (NGGCTs) (5/40, 12.5%; p = 0.03). Among NGGCTs, those with a yolk sac tumor component showed a significantly higher frequency of 3p25.3 gain (18.2%) than those without (1.5%; p = 0.048). NGGCTs with gain showed significantly shorter progression-free survival (PFS) than those without (p = 0.047). The 3p25.3 gain and 12p gain were independent from each other. The combination of 3p25.3 gain and/or 12p gain was more frequent among NGGCTs with malignant components (69%) than among those without (29%; p = 0.02). Germinomas containing a higher number of copy number alterations showed shorter PFS than those with fewer (p = 0.03). Taken together, a finding of 3p25.3 gain may be a copy number alteration specific to NGGCTs and in combination with 12p gain could serve as a marker of negative prognosis or treatment resistance. Germinoma with frequent chromosomal instability may constitute an unfavorable subgroup

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p &lt; 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small