Open Access Highly Accessed Research article

Gene expression profiling in whole blood identifies distinct biological pathways associated with obesity

Sujoy Ghosh1*, Robert Dent2, Mary-Ellen Harper3, Shelby A Gorman4, Joan S Stuart4 and Ruth McPherson5*

Author Affiliations

1 Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, USA

2 Ottawa Hospital Weight Management Clinic, Ottawa Hospital, Ottawa, Canada

3 Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada

4 GlaxoSmithKline, Research Triangle Park, USA

5 University of Ottawa Heart Institute, Ottawa, Canada

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BMC Medical Genomics 2010, 3:56  doi:10.1186/1755-8794-3-56

Published: 1 December 2010

Additional files

Additional file 1:

Comparison of expression of adipocyte and muscle specific genes in whole blood samples utilized in the current study. Data for adipocyte-specific and muscle-specific gene expression was obtained from microarray data available on 79 different tissues from the Genomics Institute of Novartis Research Foundation http://www.gnf.org webcite. Relative expression in whole blood was also obtained from the same source. The average expression and standard deviation in adipocyte-specific and muscle-specific gene expression observed in whole blood samples used in the current study

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Additional file 2:

PCA model output from multivariate analysis on obese and lean subjects based on whole blood gene expression signals. Analysis of performance of the PCA model separating obese from lean subjects based on blood gene expression signals.

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Additional file 3:

Top 100 differentially expressed genes in whole blood from obese and lean subjects. Using the GenePattern algorithm http://www.broadinstitute.org/cancer/software/genepattern/ webcite a list of the top 50 upregulated and top 50 downregulated genes in obese and lean samples was generated and plotted on a heat-map for visualization. Higher expression levels are indicated in red and lower expression levels are indicated in blue. Genes (rows) are indicated by their Affymetrix probeset identifiers and samples (columns) are indicated by their obese or lean categories.

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Additional file 4:

Differential gene expression analysis between obese and lean subjects from blood transcriptome data. Identification of top 200 differentially expressed genes between Obese and Lean subjects using the Comparative Marker Selection module in GenePattern. Results for top 100 upregulated and top 100 downregulated genes (Obese vs. Lean) are shown.

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Additional file 5:

Description and value ranges of the parameters used in gene-set enrichment analysis (GSEA) in the present study. For detailed explanation of parameters and acceptable value ranges, please see additional documentation at http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html webcite.

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Additional file 6:

Custom pathway database used in the GSEA studies. Column 1 indicates the pathway name, column 2 indicates the source for the pathway information. Subsequent columns represent the gene symbols for the genes constituting the pathway.

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Additional file 7:

List of pathways determined to be upregulated in the Obese subjects compared to the Lean subjects and vice versa. These results were obtained by querying the pathways in the KEGG database (> = 10 and < = 200 gene members) and using the GSEA algorithm.

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Additional file 8:

List of gene members of the 3 pathways identified by GSEA as upregulated in obese subjects. List of genes in the oxidative phosphorylation, ribosome and apoptosis signaling pathways that were expressed in blood from obese and lean subjects. The column 'Core Enrichment' describes whether a gene contributed significantly towards the enrichment of the respective pathway in the gene set enrichment analysis.

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Additional file 9:

List of pathways determined to be upregulated in the Obese subjects compared to the Lean subjects and vice versa. These results were obtained by querying the pathways in a user-defined custom database (> = 10 and < = 200 gene members) and using the GSEA algorithm.

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Additional file 10:

Expression patterns of genes reported to be enriched in erythrocyte/reticulocyte fraction among lean and obese subjects. The average log2 expression signal in obese and lean subjects, differential expression (log ratio) and statistical significance of the differences in expression in the two groups are indicated.

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Additional file 11:

Overexpression of erythrocyte/reticulocyte enriched genes in obese blood samples. A scatter plot of log average expression of genes (x-axis) versus the differences of log expression between the obese and lean cohorts (y-axis) was created (also known as a MA plot). Each gene is indicated by a gray dot with the exception of genes reported to be enriched in erythrocytes/reticulocytes (compared to other blood cell types) which are shown as black pluses. A value of 0 on the y-axis signifies no differences in gene expression between the lean and obese cohorts.

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Additional file 12:

Gene set enrichment analysis between obese and lean subjects considering male only or female only cohorts. Pathways are ranked in descending order of their enrichment for each comparison. Results with males only are shown first, followed by the results with females only.

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Additional file 13:

Gene set enrichment analysis between obese and lean subjects after scaling of gene expression data by expression levels of erythrocyte membrane protein band 2 (EMPB2) and hemoglobin D (HBD) genes respectively. Pathways are ranked in descending order of enrichment. Top part refers to results obtained after scaling with EMPB2; bottom part shows the results following scaling with HBD.

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Additional file 14:

Description of classifiers and classifier performance evaluators used in the study. Brief descriptions of the Naïve Bayes, Logistic Regression, Random Forests and ZeroR classifiers along with the feature selection algorithms used (Information Gain and Cfs Subset evaluator) are given. Mathematical formulas for true and false positive and negative rates (classifier evaluation metrics) are also provided.

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