OBJECTIVES: This study aimed to identify the glucose sensing pathway related genes of C. glabrata and to analyze the regulation pattern of these genes in response to different surrounding glucose concentrations through the quantitative real time polymerase chain reaction (qRT-PCR).
MATERIALS AND METHODS: Phylogenetic analysis was carried out on predicted amino acid sequences of C. glabrata and S. cerevisiae to compare their degree of similarity. In addition, the growth of C. glabrata in response to different amounts of glucose (0%, 0.01%, 0.1%, 1% and 2%) was evaluated via the spot dilution assay on prepared agar medium. Besides, the SNF3 and RGT2, which act as putative glucose sensors, and the RGT1 and MIG1, which act as putative transcriptional regulators and selected downstream hexose transporters (HXTs), were analysed through qRT-PCR analysis for the gene expression level under different glucose concentrations.
RESULTS: Comparative analysis of predicted amino acids in the phylogenetic tree showed high similarity between C. glabrata and S cerevisiae. Besides, C. glabrata demonstrated the capability to grow in glucose levels as low as 0.01% in the spot dilution assay. In qRT-PCR analysis, differential expressions were observed in selected genes when C. glabrata was subjected to different glucose concentrations.
CONCLUSIONS: The constructed phylogenetic tree suggests the close evolutionary relationship between C. glabrata and S. cerevisiae. The capability of C. glabrata to grow in extremely low glucose environments and the differential expression of selected glucose-sensing related genes suggested the possible role of these genes in modulating the growth of C. glabrata in response to different glucose concentrations. This study helps deepen our understanding of the glucose sensing mechanism in C. glabrata and serves to provide fundamental data that may assist in unveiling this mechanism as a potential drug target.
Methods: This is a pre- and post-measurement intervention study conducted in low-income community housing projects in Kuala Lumpur, Malaysia. A total of 90 participants aged 18 years and above with hypertension received intervention. The participants were divided into small groups and received instructions on the use of home blood pressure measurement. They also attended a series of talks on dietary intake modification and exercise demonstration for the first six months (active phase). In another 6 months (maintenance phase), they received only pamphlet and SMS reminders. Their anthropometry, blood pressure, dietary, and biochemical parameter changes were measured at baseline, 6 months, and 12 months of intervention.
Results: Macronutrients and micronutrients showed a significant improvement at the end of 12-month dietary intervention. The energy, carbohydrate, protein, total fat, sodium, and potassium are showing significant reduction from baseline to end of the 12-month intervention. There is no significant reduction in blood pressure. Fasting blood glucose, renal sodium, triglyceride, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol showed a significant improvement, after controlling for age and reported physical activity.
Conclusion: The intervention improved the nutritional intake and biochemical profiles of the low-income urban population with hypertension. This promising result should be replicated in a larger scale study.
Methods: A total of 413 individuals (163 men and 250 women) aged 30-60 years were selected by stratified random sampling. The participants had safe alcohol consumption habits (<2 drinks/day) and no symptoms of hepatitis B and C. NAFLD was diagnosed through ultrasound. Blood pressure, anthropometric, and body composition measurements were made and liver function tests were conducted. Biochemical assessments, including the measurement of fasting blood sugar (FBS) and ferritin levels, as well as lipid profile tests were also performed. Metabolic syndrome was evaluated according to the International Diabetes Federation (IDF) criteria.
Results: The overall prevalence of ultrasound-diagnosed NAFLD was 39.3%. The results indicated a significantly higher prevalence of NAFLD in men than in women (42.3% vs 30.4%; P < 0.05). Binary logistic regression analysis was performed to determine the significant variables as NAFLD predictors. Overall, male gender, high body mass index (BMI), high alanine aminotransferase (ALT), high FBS, and high ferritin were identified as the predictors of NAFLD. The only significant predictors of NAFLD among men were high BMI and high FBS. These predictors were high BMI, high FBS, and high ferritin in women (P < 0.05 for all variables).
Conclusions: The metabolic profile can be used for predicting NAFLD among men and women. BMI, FBS, ALT, and ferritin are the efficient predictors of NAFLD and can be used for NAFLD screening before liver biopsy.