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. 2008 Nov;93(11):4532-41.
doi: 10.1210/jc.2008-1001. Epub 2008 Aug 26.

Endoplasmic reticulum stress markers are associated with obesity in nondiabetic subjects

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Endoplasmic reticulum stress markers are associated with obesity in nondiabetic subjects

Neeraj K Sharma et al. J Clin Endocrinol Metab. 2008 Nov.

Abstract

Objective: Adipocyte and hepatocyte endoplasmic reticulum (ER) stress response is activated in dietary and genetic models of obesity in mice. We hypothesized that ER stress was also activated and associated with reduced insulin sensitivity (SI) in human obesity.

Research design and methods: We recruited 78 healthy, nondiabetic individuals over a spectrum of body mass index (BMI) who underwent oral and iv glucose tolerance tests, and fasting sc adipose and muscle biopsies. We tested expression of 18 genes and levels of total and phosphorylated eukaryotic initiation factor 2alpha, c-jun, and c-Jun N-terminal kinase 1 in adipose tissue. We compared gene expression in stromal vascular and adipocyte fractions in paired samples from 22 individuals, and tested clustering on gene and protein markers.

Results: Adipocyte expression of most markers of ER stress, including chaperones downstream of activating transcription factor 6, were significantly correlated with BMI and percent fat (r>0.5; P<0.00001). Phosphorylation of eukaryotic initiation factor 2alpha but not of c-Jun N-terminal kinase 1 or c-jun was increased with obesity. ER stress response (as elsewhere) was also increased with obesity in a second set of 86 individuals, and in the combined sample (n=161). The increase was only partially attributable to the stromal vascular fraction and macrophage infiltration. ER stress markers were only modestly correlated with S(I). Clustering algorithms supported ER stress activation with high BMI but not low SI.

Conclusions: Multiple markers of ER stress are activated in human adipose with obesity, particularly for protective chaperones downstream of activating transcription factor 6alpha.

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Figures

Figure 1
Figure 1
Gene expression vs. BMI by ethnicity. The figure shows regression plots for HSPA5 (A), TRAF2 (B), DNAJC3 (C), and HYOU1 (D). For all figures, both gene expression measures (ratio to 18S RNA) and BMI were logarithmically transformed, but BMI is shown as kg/m2. Solid lines represent means for European American participants (n = 54), and dotted lines represent means for African-American participants (n = 24). Bars represent 95% CIs.
Figure 2
Figure 2
Gene expression of ERSR genes by BMI group. Gene expression, calculated as real-time values normalized to 18S RNA, is shown by BMI divided into lean (BMI < 25 kg/m2; n = 21, black bars), overweight (25–30 kg/m2; n = 29, white bars), or obese (BMI > 30 kg/m2, n = 28, hatched bars). Significant differences based on ANOVA of logarithmically-transformed values are marked by asterisks; no asterisks represent no significant difference. Other levels of significance are as follows: *, <0.01; **, P < 0.001; ***, P < 0.0001; ****, P < 0.00001. The graph is shown on a linear scale.
Figure 3
Figure 3
Western blot of phosphoproteins. Representative Western blots showing samples with high and low BMI. Antibodies are either total protein as noted, or phosphoproteins. A, Total and phosphorylated eIF2α, with β-actin for normalization. Control is palmitate-treated HepG2 cells, which show acute ERSR. Numbers below lane numbers 1–8 give BMI of individuals represented. B, Phosphorylated and unphosphorylated forms of JNK1 and JNK2, and phosphorylated c-jun. Control is from palmitate-treated HepG2 cells. The gel is representative of Western blots on 34 individuals, as described in the Results, with each individual measured at least twice. C, Scatterplot for 34 samples for p-eIF2α and p-JNK1. Significance (P values) is based on the Mann-Whitney U test. HBMI, high BMI group (n = 19); LBMI, low BMI group (n = 15).
Figure 4
Figure 4
Hierarchical clustering of gene expression for ER stress transcripts phosphorylated EIF2a and JNK1. These are data on 15 individuals selected for lowest BMI and 19 individuals selected for highest BMI from the full sample of 76 individuals, as shown in Fig. 3. Euclidian distance matrix between each individual was generated using log-normalized values of all expression measures and was used for seriation under multiple fragment heuristics.

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