Next Generation Sequencing Methylation Profiling in Skeletal Muscle from Lean and Obese Subjects [Bisulfite-Seq]
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE73303 | GEO | 2015/11/01
SECONDARY ACCESSION(S): PRJNA296565
REPOSITORIES: GEO
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Project description:Obesity is a metabolic disease caused by environmental, genetic, and epigenetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to identify skeletal muscle DNA methylation patterns in obesity. Muscle biopsies were obtained basally from lean (n=11) and obese (n=9) participants in combination with euglycemic hyperinsulinemic clamps to assess insulin sensitivity. We performed reduced representation bisulfite sequencing next generation methylation analysis on DNA isolated from vastus lateralis muscle biopsies.
2015-11-01 | E-GEOD-73303 | biostudies-arrayexpress
Project description:Obesity is a metabolic disease caused by environmental, genetic, and epigenetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to identify skeletal muscle DNA methylation patterns in combination with transcriptomic changes in obesity. Muscle biopsies were obtained basally from lean (n=10) and obese (n=10) participants in combination with euglycemic hyperinsulinemic clamps to assess insulin sensitivity. We performed microarray (SurePrint G3 Human Gene Expression 8x60K v2) analysis on RNA isolated from vastus lateralis muscle biopsies. This represents the expression profiling component only.
2015-11-01 | E-GEOD-73078 | biostudies-arrayexpress
Project description:Obesity is a metabolic disease caused by environmental, genetic, and epigenetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to identify skeletal muscle DNA methylation patterns in combination with transcriptomic changes in obesity.
2015-11-01 | GSE73078 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-11-01 | E-GEOD-73304 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2015-11-01 | GSE73304 | GEO
Project description:Obtaining adipose tissue samples are paramount to the understanding of human obesity. We have examined the impact of needle-aspirated and surgical biopsy techniques on the study of subcutaneous adipose tissue (scAT) gene expression in both obese and lean subjects. Biopsy sampling methods have a significant impact on data interpretation and revealed that gene expression profiles derived from surgical tissue biopsies better capture the significant changes in molecular pathways associated with obesity. We hypothesize that this is because needle biopsies do not aspirate the fibrotic fraction of scAT; which subsequently results in an under-representation of the inflammatory and metabolic changes that coincide with obesity. This analysis revealed that the biopsy technique influences the gene expression underlying the biological themes commonly discussed in obesity (e.g. inflammation, extracellular matrix, metabolism, etc), and is therefore a caveat to consider when designing microarray experiments. These results have crucial implications for the clinical and physiopathological understanding of human obesity and therapeutic approaches. Keywords: subject and tissue biopsy technique comparison
2008-12-22 | GSE12050 | GEO
Project description:Obtaining adipose tissue samples are paramount to the understanding of human obesity. We have examined the impact of needle-aspirated and surgical biopsy techniques on the study of subcutaneous adipose tissue (scAT) gene expression in both obese and lean subjects. Biopsy sampling methods have a significant impact on data interpretation and revealed that gene expression profiles derived from surgical tissue biopsies better capture the significant changes in molecular pathways associated with obesity. We hypothesize that this is because needle biopsies do not aspirate the fibrotic fraction of scAT; which subsequently results in an under-representation of the inflammatory and metabolic changes that coincide with obesity. This analysis revealed that the biopsy technique influences the gene expression underlying the biological themes commonly discussed in obesity (e.g. inflammation, extracellular matrix, metabolism, etc), and is therefore a caveat to consider when designing microarray experiments. These results have crucial implications for the clinical and physiopathological understanding of human obesity and therapeutic approaches. Keywords: subject and tissue biopsy technique comparison Tissue samples from lean and obese subjects were analyzed: total of 36 hybridizations. The goal was to compare the effect of biopsy sampling methods on global subcutaneous adipose tissue gene expression analyses. The following subject groups were used for the analysis: 9 lean subjects: needle biopsy 9 lean subjects: surgical biopsy 9 obese subjects: needle biopsy 9 obese subjects: surgical biopsy
2008-12-22 | E-GEOD-12050 | biostudies-arrayexpress
Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)
2015-12-15 | E-GEOD-73034 | biostudies-arrayexpress
Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.
2015-12-15 | GSE73034 | GEO
Project description:Next Generation Sequencing Methylation Profiling in Skeletal Muscle from Lean and Obese Subjects
| PRJNA296560 | ENA