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

Abstract

Background: Obesity is reaching epidemic proportions and represents a significant risk factor for cardiovascular disease, diabetes, and cancer.

Methods: To explore the relationship between increased body mass and gene expression in blood, we conducted whole-genome expression profiling of whole blood from seventeen obese and seventeen well matched lean subjects. Gene expression data was analyzed at the individual gene and pathway level and a preliminary assessment of the predictive value of blood gene expression profiles in obesity was carried out.

Results: Principal components analysis of whole-blood gene expression data from obese and lean subjects led to efficient separation of the two cohorts. Pathway analysis by gene-set enrichment demonstrated increased transcript levels for genes belonging to the "ribosome", "apoptosis" and "oxidative phosphorylation" pathways in the obese cohort, consistent with an altered metabolic state including increased protein synthesis, enhanced cell death from proinflammatory or lipotoxic stimuli, and increased energy demands. A subset of pathway-specific genes acted as efficient predictors of obese or lean class membership when used in Naive Bayes or logistic regression based classifiers.

Conclusion: This study provides a comprehensive characterization of the whole blood transcriptome in obesity and demonstrates that the investigation of gene expression profiles from whole blood can inform and illustrate the biological processes related to regulation of body mass. Additionally, the ability of pathway-related gene expression to predict class membership suggests the feasibility of a similar approach for identifying clinically useful blood-based predictors of weight loss success following dietary or surgical interventions.

Figure 1

Comparison of gene expression signals generated by Affymetrix microarrays and quantitative real-time PCR. Gene expression signals were generated by real-time, quantitative PCR (Taqman, black bars) and oligonucleotide microarrays (Affymetrix, white bars). Overexpression or underexpression of a gene in the obese and lean cohorts is expressed as a log ratio, to the base 2. Affymetrix and Taqman based results for each gene are shown as a stacked bar. For each gene, agreement between the results from the two platforms is indicated when both white and black bars lie on the same side of the zero (0) value on the log ratio axis; conversely, disagreement is indicated when the gray and black bars lie on opposite sides. The overall agreement between the two platforms was 85% (45/53 genes showed agreement in the direction of differential expression).

Figure 2

Multivariate analysis of obese and lean subjects based on gene expression signals. Principal component analysis (PCA) was performed on lean and obese subjects based on 12128 Affymetrix probe-set signals. A scatterplot of the first two principal components demonstrate a general separation of the obese and lean phenotypes along the first principal component (PC1). Model parameters are as follows: Further details on the PCA model parameters are included in Supplemental Table 2.

Figure 3

Gene-set enrichment analysis. Gene-set enrichment analysis against the KEGG database for differentially enriched pathways in whole blood between obese and lean subjects. Enrichment plots for the 3 pathways upregulated in the obese cohort are shown on the left side with the relative gene positions indicated by the straight lines (line plot) under each graph. Lines clustered to the left represent higher ranked genes in the ranked list. Expression profiles for a subset of genes (shaded in yellow in the line plots) contributing to core enrichment for each pathway are shown to the right as a heatmap. The heatmap compares subject-level gene expression in both obese and lean subjects. Gene expression is normalized for each row. Lower levels of expression are represented in shades of blue and higher expression in red.