箸の文化と割箸の歴史地理：奈良吉野下市の割箸を主として

Lipid droplets (LDs) hypertrophy in adipocytes is the main cause of energy metabolic system dysfunction, obesity and its afflictions such as T2D. However, the role of adipocytes in linking energy metabolic disorders with insulin regulation is unknown in humans. Human adipocytes constitutively synthesize and secrete insulin, which is biologically functional. Insulin concentrations and release are fat mass-and LDs-dependent respectively. Fat reduction mediated by bariatric surgery repairs obesity-associated T2D the expression of genes, like PCSK1 (proinsulin conversion enzyme), GCG (Glucagon), GPLD1, CD38 and NNAT, involved in insulin regulation/release were differentially expressed in pancreas and adipose tissue (AT). INS (insulin) and GCG expression reduced in human AT-T2D as compared to AT-control, but remained unchanged in pancreas in either state. Insulin levels (mRNA/protein) were higher in AT derived from prediabetes BB rats with destructed pancreatic 2-cells and controls than pancreas derived from the same rats respectively. Insulin expression in 10 human primary cell types including adipocytes and macrophages is an evidence for extrapancreatic insulin-producing cells the data suggest a crosstalk between AT and pancreas to fine-tune energy metabolic system or may minimize the metabolic damage during diabetes. This study opens new avenues towards T2D therapy with a great impact on public health

Co-expressed immune and metabolic genes in visceral and subcutaneous adipose tissue from severely obese individuals are associated with plasma HDL and glucose levels: a microarray study

<p>Abstract</p> <p>Background</p> <p>Excessive accumulation of body fat, in particular in the visceral fat depot, is a major risk factor to develop a variety of diseases such as type 2 diabetes. The mechanisms underlying the increased risk of obese individuals to develop co-morbid diseases are largely unclear.</p> <p>We aimed to identify genes expressed in subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) that are related to blood parameters involved in obesity co-morbidity, such as plasma lipid and glucose levels, and to compare gene expression between the fat depots.</p> <p>Methods</p> <p>Whole-transcriptome SAT and VAT gene expression levels were determined in 75 individuals with a BMI >35 kg/m². Modules of co-expressed genes likely to be functionally related were identified and correlated with BMI, plasma levels of glucose, insulin, HbA_1c, triglycerides, non-esterified fatty acids, ALAT, ASAT, C-reactive protein, and LDL- and HDL cholesterol.</p> <p>Results</p> <p>Of the approximately 70 modules identified in SAT and VAT, three SAT modules were inversely associated with plasma HDL-cholesterol levels, and a fourth module was inversely associated with both plasma glucose and plasma triglyceride levels (p < 5.33 × 10^-5). These modules were markedly enriched in immune and metabolic genes. In VAT, one module was associated with both BMI and insulin, and another with plasma glucose (p < 4.64 × 10^-5). This module was also enriched in inflammatory genes and showed a marked overlap in gene content with the SAT modules related to HDL. Several genes differentially expressed in SAT and VAT were identified.</p> <p>Conclusions</p> <p>In obese subjects, groups of co-expressed genes were identified that correlated with lipid and glucose metabolism parameters; they were enriched with immune genes. A number of genes were identified of which the expression in SAT correlated with plasma HDL cholesterol, while their expression in VAT correlated with plasma glucose. This underlines both the singular importance of these genes for lipid and glucose metabolism and the specific roles of these two fat depots in this respect.</p

Analysis of the differences in whole-genome expression related to asthma and obesity

Introduction Concomitant obesity significantly impairs asthma control. Obese asthmatics show more severe symptoms and an increased use of medications. Objectives The primary aim of the study was to identify genes that are differentially expressed in the peripheral blood of asthmatic patients with obesity, asthmatic patients with normal body mass, and obese patients without asthma. Secondly, we investigated whether the analysis of gene expression in peripheral blood may be helpful in the differential diagnosis of obese patients who present with symptoms similar to asthma. Patients and methods The study group included 15 patients with asthma (9 obese and 6 normal-weight patients), while the control group-13 obese patients in whom asthma was excluded. The analysis of whole-genome expression was performed on RNA samples isolated from peripheral blood. Results The comparison of gene expression profiles between asthmatic patients with obesity and those with normal body mass revealed a significant difference in 6 genes. The comparison of the expression between controls and normal-weight patients with asthma showed a significant difference in 23 genes. The analysis of genes with a different expression revealed a group of transcripts that may be related to an increased body mass (PI3, LOC100008589, RPS6KA3, LOC441763, IFIT1, and LOC100133565). Based on gene expression results, a prediction model was constructed, which allowed to correctly classify 92% of obese controls and 89% of obese asthmatic patients, resulting in the overall accuracy of the model of 90.9%. Conclusions The results of our study showed significant differences in gene expression between obese asthmatic patients compared with asthmatic patients with normal body mass as well as in obese patients without asthma compared with asthmatic patients with normal body mass

Whole blood gene expression profiles of patients with a past aneurysmal subarachnoid hemorrhage

Background The pathogenesis of development and rupture of intracranial aneurysms (IA) is largely unknown. Also, screening for IA to prevent aneurysmal subarachnoid hemorrhage (aSAH) is inefficient, as disease markers are lacking. We investigated gene expression profiles in blood of previous aSAH patients, who are still at risk for future IA, aiming to gain insight into the pathogenesis of IA and aSAH, and to make a first step towards improvement of aSAH risk prediction. Methods and Results We collected peripheral blood of 119 patients with aSAH at least two years prior, and 118 controls. We determined gene expression profiles using Illumina HumanHT-12v4 Bead-Chips. After quality control, we divided the dataset in a discovery (2/3) and replication set (1/3), identified differentially expressed genes, and applied (co-) differential co-expression to identify disease-related gene networks. No genes with a significant (false-discovery rate <5%) differential expression were observed. We detected one gene network with significant differential co-expression, but did not find biologically meaningful gene networks related to a history of aSAH. Next, we applied prediction analysis of microarrays to find a gene set that optimally predicts absence or presence of a history of aSAH. We found no gene sets with a correct disease state prediction higher than 40%. Conclusions No gene expression differences were present in blood of previous aSAH patients compared to controls, besides one differentially co-expressed gene network without a clear relevant biological function. Our findings suggest that gene expression profiles, as detected in blood of previous aSAH patients, do not reveal the pathogenesis of IA and aSAH, and cannot be used for aSAH risk prediction

Differences in gene expression related to the outcomes of obesity treatment, peak oxygen uptake, and fatty acid metabolism measured in a cardiopulmonary exercise test

INTRODUCTION The obesity pandemic requires development of methods that could be used on a large scale, such as the cardiopulmonary exercise test (CPET). Gene expression may explain CPET results on the molecular level. OBJECTIVES The aim of this study was to compare gene expression in obesity, depending on CPET results. PATIENTS AND METHODS The study group consisted of 9 obese patients and 7 controls. The treatment encompassed diet, rehabilitation, and behavioral therapy. Diet was based on the body composition analyzed by bioelectrical impedance, resting metabolic rate, and subjective patient preferences. The rehabilitation depended on the CPET results: maximal oxygen uptake and fatty acid metabolism. Behavioral intervention focused on the diagnosis of health problems leading to obesity, lifestyle modification, training in self-assessment, and development of healthy habits. The intensive treatment lasted for 12 weeks and consisted of consultations with a physician, dietitian, and medical rehabilitation specialist. RNA was isolated from the whole blood. A total of 47 323 transcripts were analyzed, of which 32 379 entities were confirmed to have high quality of RNA. RESULTS We observed differences in gene expression related to the CPET results indicating abnormalities in fat oxidation and maximal oxygen uptake. The genes with major differences in expression were: CLEC12A, HLA-DRB1, HLA-DRB4, HLA-A29.1, IFIT1, and LOC100133662. CONCLUSIONS The differences in gene expression may account for the outcomes of treatment related to inflammation caused by obesity, which affects the muscles, fat tissue, and fatty acid metabolism

Differences in gene expression related to the outcomes of obesity treatment, peak oxygen uptake, and fatty acid metabolism measured in a cardiopulmonary exercise test

INTRODUCTION The obesity pandemic requires development of methods that could be used on a large scale, such as the cardiopulmonary exercise test (CPET). Gene expression may explain CPET results on the molecular level. OBJECTIVES The aim of this study was to compare gene expression in obesity, depending on CPET results. PATIENTS AND METHODS The study group consisted of 9 obese patients and 7 controls. The treatment encompassed diet, rehabilitation, and behavioral therapy. Diet was based on the body composition analyzed by bioelectrical impedance, resting metabolic rate, and subjective patient preferences. The rehabilitation depended on the CPET results: maximal oxygen uptake and fatty acid metabolism. Behavioral intervention focused on the diagnosis of health problems leading to obesity, lifestyle modification, training in self-assessment, and development of healthy habits. The intensive treatment lasted for 12 weeks and consisted of consultations with a physician, dietitian, and medical rehabilitation specialist. RNA was isolated from the whole blood. A total of 47 323 transcripts were analyzed, of which 32 379 entities were confirmed to have high quality of RNA. RESULTS We observed differences in gene expression related to the CPET results indicating abnormalities in fat oxidation and maximal oxygen uptake. The genes with major differences in expression were: CLEC12A, HLA-DRB1, HLA-DRB4, HLA-A29.1, IFIT1, and LOC100133662. CONCLUSIONS The differences in gene expression may account for the outcomes of treatment related to inflammation caused by obesity, which affects the muscles, fat tissue, and fatty acid metabolism

Modules of differentially co-expressed genes: permutation and preservation results.

<p>p_Bonf indicates Bonferroni-corrected p-value, NA: not applicable (module not tested for preservation). Modules were randomly color-labeled.</p><p>Modules of differentially co-expressed genes: permutation and preservation results.</p

Baseline characteristics of the study population.

<p>FIA indicates familial intracranial aneurysm, N: number, SAH: subarachnoid hemorrhage, NA: not applicable.</p><p>Baseline characteristics of the study population.</p

Top 10 hub genes in yellow module.

<p>Top 10 hub genes in yellow module.</p

Predicted SAH probability in subjects from replication set, using 2388 probes selected with prediction analysis of microarrays.

<p>This figure shows the probability of being a SAH case for each subject in the replication set, based on prediction analysis of microarrays (PAM). We used PAM to define a group of probes in the discovery set with the highest predictive value to identify cases and controls in the replication set. As a result, a group of 2388 probes was selected, with a relatively high misclassification rate of 40%. The figure shows that this group of probes does not divide cases and controls in two separate groups.</p