Dataset Information

Whole blood gene expression profiles in insulin resistant Latinos with the metabolic syndrome.

ABSTRACT: Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral whole blood cells. All subjects (n=184) were Latino descent from the Arizona Insulin Resistance registry. Subjects were classified based on the metabolic syndrome phenotype according to the National Cholesterol Education Program's Adult Treatment Panel III. Of the 184 Latino subjects in the study, 74 were classified with the metabolic syndrome and 110 were without. Whole blood gene expression profiling was performed using the Agilent 4x44K Whole Human Genome Microarray. Whole blood microarray analysis identified 1,432 probes that were altered in expression ? 1.2 fold and P<0.05 after Benjamini-Hochberg in the metabolic syndrome subjects. KEGG pathway analysis revealed significant enrichment for pathways including ribosome, oxidative phosphorylation and MAPK signaling (all Benjamini-Hochberg P<0.05). Whole blood mRNA expression changes observed in the microarray data were confirmed by quantitative RT-PCR. Transcription factor binding motif enrichment analysis revealed E2F1, ELK1, NF-kappaB, STAT1 and STAT3 significantly enriched after Bonferroni correction (all P<0.05). The results of the present study demonstrate that whole blood is a useful tissue for studying the metabolic syndrome and its underlying insulin resistance although the relationship between blood and skeletal muscle differs.

SUBMITTER: Tangen SE

PROVIDER: S-EPMC3866261 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Whole blood gene expression profiles in insulin resistant Latinos with the metabolic syndrome.

Tangen Samantha E SE Tsinajinnie Darwin D Nuñez Martha M Shaibi Gabriel Q GQ Mandarino Lawrence J LJ Coletta Dawn K DK

PloS one 20131217 12

Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral ...[more]

PMID: 24358323

Similar Datasets

Project description:Metabolic syndrome (MetS) is highly prevalent worldwide. In the United States, estimates show that more than 30% of the adult population has MetS. MetS consists of multiple phenotypes, including obesity, dyslipidemia, and impaired glucose tolerance. Therefore, identifying the molecular mechanisms to explain this complex disease is critical for diagnosing and treating MetS. We previously showed 70 increased genes and 20 decreased genes in whole blood in MetS participants. The present study aimed to identify blood-based DNA methylation biomarkers in non-MetS versus MetS participants. The present study analyzed whole blood DNA samples from 184 adult participants of Latino descent from the Arizona Insulin Resistance (AIR) registry. We used the National Cholesterol Education Program Adult Treatment Panel III (NCEP: ATP III) criteria to identify non-MetS (n = 110) and MetS (n = 74) participants. We performed whole blood methylation analysis on select genes: ATP Synthase, H+ Transporting mitochondrial F1 Complex, Epsilon Subunit (ATP5E), Cytochrome C Oxidase Subunit VIc (COX6C), and Ribosomal Protein L9 (RPL9). The pyrosequencing analysis was a targeted approach focusing on the promoter region of each gene that specifically captured CpG methylation sites. In MetS participants, we showed decreased methylation in two CpG sites in COX6C and three CpG sites in RPL9, all p < 0.05 using the Mann-Whitney U test. There were no ATP5E CpG sites differently methylated in the MetS participants. Furthermore, while adjusting for age, gender, and smoking status, logistic regression analysis reaffirmed the associations between MetS and mean methylation within COX6C and RPL9 (both p < 0.05). In addition, Spearman's correlation revealed a significant inverse relationship between the previously published gene expression data and methylation data for RPL9 (p < 0.05). In summary, these results highlight potential blood DNA methylation biomarkers for the MetS phenotype. However, future validation studies are warranted to strengthen our findings.

Project description:The association of lung cancer with changes in microRNAs in plasma shown in multiple studies suggests a utility for circulating microRNA biomarkers in non-invasive detection of the disease. We examined if presence of lung cancer is reflected in whole blood microRNA expression as well, possibly because of a systemic response. Locked nucleic acid microarrays were used to quantify the global expression of microRNAs in whole blood of 22 patients with lung adenocarcinoma and 23 controls, ten of whom had a radiographically detected non-cancerous lung nodule and the other 13 were at high risk for developing lung cancer because of a smoking history of >20 pack-years. Cases and controls differed significantly for age with a mean difference of 10.7 years, but not for gender, race, smoking history, blood hemoglobin, platelet count, or white blood cell count. Of 1282 quantified human microRNAs, 395 (31%) were identified as expressed in the study's subjects, with 96 (24%) differentially expressed between cases and controls. Classification analyses of microRNA expression data were performed using linear kernel support vector machines (SVM) and top-scoring pairs (TSP) methods, and classifiers to identify presence of lung adenocarcinoma were internally cross-validated. In leave-one-out cross-validation, the TSP classifiers had sensitivity and specificity of 91% and 100%, respectively. The values with SVM were both 91%. In a Monte Carlo cross-validation, average sensitivity and specificity values were 86% and 97%, respectively, with TSP, and 88% and 89%, respectively, with SVM. MicroRNAs miR-190b, miR-630, miR-942, and miR-1284 were the most frequent constituents of the classifiers generated during the analyses. These results suggest that whole blood microRNA expression profiles can be used to distinguish lung cancer cases from clinically relevant controls. Further studies are needed to validate this observation, including in non-adenocarcinomatous lung cancers, and to clarify upon the confounding effect of age.

Project description:Gene expression profiling using blood samples is a valuable tool for biomarker discovery in clinical studies. Different whole blood RNA collection and processing methods are highly variable and might confound comparisons of results across studies. The main aim of the current study is to compare how blood storage, extraction methodologies, and the blood components themselves may influence gene expression profiling. Whole blood and peripheral blood mononuclear cell (PBMC) samples were collected in triplicate from five healthy donors. Whole blood was collected in RNAgard® and PAXgene® Blood RNA Tubes, as well as in collection tubes with anticoagulants such as dipotassium ethylenediaminetetraacetic acid (K2EDTA) and Acid Citrate Dextrose Solution A (ACD-A). PBMCs were separated using sodium citrate Cell Preparation Tubes (CPT™), FICOLL™, magnetic separation, and the LeukoLOCK™ methods. After blood collection, the LeukoLOCK™, K2EDTA and ACD-A blood tubes were shipped overnight using cold conditions and samples from the rest of the collection were immediately frozen with or without pre-processing. The RNA was isolated from whole blood and PBMCs using a total of 10 different experimental conditions employing several widely utilized RNA isolation methods. The RNA quality was assessed by RNA Integrity Number (RIN), which showed that all PBMC procedures had the highest RIN values when blood was stabilized in TRIzol® Reagent before RNA extraction. Initial data analysis showed that human blood stored and shipped at 4°C overnight performed equally well when checked for quality using RNA integrity number when compared to frozen stabilized blood. Comparisons within and across donor/method replicates showed signal-to-noise patterns which were not captured by RIN value alone. Pathway analysis using the top 1000 false discovery rate (FDR) corrected differentially expressed genes (DEGs) showed frozen vs. cold shipping conditions greatly impacted gene expression patterns in whole blood. However, the top 1000 FDR corrected DEGs from PBMCs preserved after frozen vs. cold shipping conditions (LeukoLOCK™ preserved in RNAlater®) revealed no significantly affected pathways. Our results provide novel insight into how RNA isolation, various storage, handling, and processing methodologies can influence RNA quality and apparent gene expression using blood samples. Careful consideration is necessary to avoid bias resulting from downstream processing. Better characterization of the effects of collection method idiosyncrasies will facilitate further research in understanding the effect of gene expression variability in human sample types.

Dataset Information

Whole blood gene expression profiles in insulin resistant Latinos with the metabolic syndrome.

Publications

Whole blood gene expression profiles in insulin resistant Latinos with the metabolic syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets