Impact of visceral fat on gene expression profile in peripheral blood cells in obese Japanese subjects.

BACKGROUND: Visceral fat plays a central role in the development of metabolic syndrome and atherosclerotic cardiovascular diseases. The association of visceral fat accumulation with cardio-metabolic diseases has been reported, but the impact of visceral fat on the gene expression profile in peripheral blood cells remains to be determined. The aim of this study was to determine the effects of visceral fat area (VFA) and subcutaneous fat area (SFA) on the gene expression profile in peripheral blood cells of obese subjects. METHODS: All 17 enrolled subjects were hospitalized to receive diet therapy for obesity (defined as body mass index, BMI, greater than 25 kg/m2). VFA and SFA were measured at the umbilical level by computed tomography (CT). Blood samples were subjected to gene expression profile analysis by using SurePrint G3 Human GE Microarray 8 × 60 k ver. 2.0. The correlation between various clinical parameters, including VFA and SFA, and peripheral blood gene expression levels was analyzed. RESULTS: Among the 17 subjects, 12 had normal glucose tolerance or borderline diabetes, and 5 were diagnosed with type 2 diabetes without medications [glycated hemoglobin (HbA1c); 6.3 ± 1.3%]. The mean BMI, VFA, and SFA were 30.0 ± 5.5 kg/m2, 177 ± 67 and 245 ± 131 cm2, respectively. Interestingly, VFA altered the expression of 1354 genes, including up-regulation of 307 and down-regulation of 1047, under the statistical environment that the parametric false discovery rate (FDR) was less than 0.1. However, no significant effects were noted for SFA or BMI. Gene ontology analysis showed higher prevalence of VFA-associated genes than that of SFA-associated genes, among the genes associated with inflammation, oxidative stress, immune response, lipid metabolism, and glucose metabolism. CONCLUSIONS: Accumulation of visceral fat, but not subcutaneous fat, has a significant impact on the gene expression profile in peripheral blood cells in obese Japanese subjects.

Gene expression levels of S100 protein family in blood cells are associated with insulin resistance and inflammation (Peripheral blood S100 mRNAs and metabolic syndrome).

OBJECTIVE: Visceral fat obesity is located upstream of metabolic syndrome and atherosclerotic diseases. Accumulating evidences indicate that several immunocytes including macrophages infiltrate into adipose tissue and induce chronic low-grade inflammation. We recently analyzed the association between visceral fat adiposity and the gene expression profile in peripheral blood cells in human subjects and demonstrated the close relationship of visceral fat adiposity and disturbance of circadian rhythm in peripheral blood cells. In a series of studies, we herein investigated the association of visceral fat adiposity and mRNA levels relating to inflammatory genes in peripheral blood cells. APPROACH AND RESULTS: Microarray analysis was performed in peripheral blood cells from 28 obese subjects. Reverse transcription-polymerase chain reaction (RT-PCR) was conducted by using blood cells from 57 obese subjects. Obesity was defined as body mass index (BMI) greater than 25 kg/m2 according to the Japanese criteria. Gene expression profile analysis was carried out with Agilent whole human genome 4×44K oligo-DNA microarray. Gene ontology (GO) analysis showed that 14 genes were significantly associated with visceral fat adiposity among 239 genes relating to inflammation. Among 14 genes, RT-PCR demonstrated that S100A8, S100A9, and S100A12 positively correlated with visceral fat adiposity in 57 subjects. Stepwise multiple regression analysis showed that S100A8 and S100A12 mRNA levels were closely associated with HOMA-IR and S100A9 mRNA was significantly related to adiponectin and CRP. CONCLUSIONS: Peripheral blood mRNA levels of S100 family were closely associated with insulin resistance and inflammation.

A pilot investigation of visceral fat adiposity and gene expression profile in peripheral blood cells.

Evidence suggests that visceral fat accumulation plays a central role in the development of metabolic syndrome. Excess visceral fat causes local chronic low-grade inflammation and dysregulation of adipocytokines, which contribute in the pathogenesis of the metabolic syndrome. These changes may affect the gene expression in peripheral blood cells. This study for the first time examined the association between visceral fat adiposity and gene expression profile in peripheral blood cells. The gene expression profile was analyzed in peripheral blood cells from 28 obese subjects by microarray analysis. Reverse transcription-polymerase chain reaction (RT-PCR) was performed using peripheral blood cells from 57 obese subjects. Obesity was defined as body mass index (BMI) greater than 25 kg/m(2) according to the Japanese criteria, and the estimated visceral fat area (eVFA) was measured by abdominal bioelectrical impedance. Analysis of gene expression profile was carried out with Agilent whole human genome 4 × 44 K oligo-DNA microarray. The expression of several genes related to circadian rhythm, inflammation, and oxidative stress correlated significantly with visceral fat accumulation. Period homolog 1 (PER1) mRNA level in blood cells correlated negatively with visceral fat adiposity. Stepwise multiple regression analysis identified eVFA as a significant determinant of PER1 expression. In conclusion, visceral fat adiposity correlated with the expression of genes related to circadian rhythm and inflammation in peripheral blood cells.

Gene organization in rice revealed by full-length cDNA mapping and gene expression analysis through microarray.

Rice (Oryza sativa L.) is a model organism for the functional genomics of monocotyledonous plants since the genome size is considerably smaller than those of other monocotyledonous plants. Although highly accurate genome sequences of indica and japonica rice are available, additional resources such as full-length complementary DNA (FL-cDNA) sequences are also indispensable for comprehensive analyses of gene structure and function. We cross-referenced 28.5K individual loci in the rice genome defined by mapping of 578K FL-cDNA clones with the 56K loci predicted in the TIGR genome assembly. Based on the annotation status and the presence of corresponding cDNA clones, genes were classified into 23K annotated expressed (AE) genes, 33K annotated non-expressed (ANE) genes, and 5.5K non-annotated expressed (NAE) genes. We developed a 60mer oligo-array for analysis of gene expression from each locus. Analysis of gene structures and expression levels revealed that the general features of gene structure and expression of NAE and ANE genes were considerably different from those of AE genes. The results also suggested that the cloning efficiency of rice FL-cDNA is associated with the transcription activity of the corresponding genetic locus, although other factors may also have an effect. Comparison of the coverage of FL-cDNA among gene families suggested that FL-cDNA from genes encoding rice- or eukaryote-specific domains, and those involved in regulatory functions were difficult to produce in bacterial cells. Collectively, these results indicate that rice genes can be divided into distinct groups based on transcription activity and gene structure, and that the coverage bias of FL-cDNA clones exists due to the incompatibility of certain eukaryotic genes in bacteria.

Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetes.

We hypothesized that systemically circulating peripheral blood mononuclear cells (PBMCs) reflect the pathophysiology of type 2 diabetes. PBMCs were obtained from 18 patients with type 2 diabetes and 16 non-diabetic subjects. The expression of genes in the PBMCs was analyzed by using a DNA chip followed by statistical analysis for specific gene sets for biological categories. The only gene set coordinately up-regulated by the existence of diabetes and down-regulated by glycemic control consisted of 48 genes involved in the c-Jun N-terminal kinase (JNK) pathway. In contrast, the only gene set coordinately down-regulated by the existence of diabetes, but not altered by glycemic control consisted of 92 genes involved in the mitochondrial oxidative phosphorylation (OXPHOS) pathway. Our findings suggest that genes involved in the JNK and OXPHOS pathways of PBMCs may be surrogate transcriptional markers for hyperglycemia-induced oxidative stress and morbidity of type 2 diabetes, respectively.

A Bayesian missing value estimation method for gene expression profile data.

MOTIVATION: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. RESULTS: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. AVAILABILITY: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/.