abstract: # OBJECTIVE
  .
  To examine in obese young adults the influence of ethnicity and subcutaneous adipose tissue (SAT) inflammation on hepatic fat fraction (HFF), visceral adipose tissue (VAT) deposition, insulin sensitivity (SI), β-cell function, and SAT gene expression.
  .
  # RESEARCH DESIGN AND METHODS
  .
  SAT biopsies were obtained from 36 obese young adults (20 Hispanics, 16 African Americans) to measure crown-like structures (CLS), reflecting SAT inflammation. SAT, VAT, and HFF were measured by magnetic resonance imaging, and SI and β-cell function (disposition index \[DI\]) were measured by intravenous glucose tolerance test. SAT gene expression was assessed using Illumina microarrays.
  .
  # RESULTS
  .
  Participants with CLS in SAT (*n* = 16) were similar to those without CLS in terms of ethnicity, sex, and total body fat. Individuals with CLS had greater VAT (3.7 ± 1.3 vs. 2.6 ± 1.6 L; *P* = 0.04), HFF (9.9 ± 7.3 vs. 5.8 ± 4.4%; *P* = 0.03), tumor necrosis factor-α (20.8 ± 4.8 vs. 16.2 ± 5.8 pg/mL; *P* = 0.01), fasting insulin (20.9 ± 10.6 vs. 9.7 ± 6.6 mU/mL; *P* \< 0.001) and glucose (94.4 ± 9.3 vs. 86.8 ± 5.3 mg/dL; *P* = 0.005), and lower DI (1,559 ± 984 vs. 2,024 ± 829 ×10^−4^ min^−1^; *P* = 0.03). Individuals with CLS in SAT exhibited upregulation of matrix metalloproteinase-9 and monocyte antigen CD14 genes, as well as several other genes belonging to the nuclear factor-κB (NF-κB) stress pathway.
  .
  # CONCLUSIONS
  .
  Adipose tissue inflammation was equally distributed between sexes and ethnicities. It was associated with partitioning of fat toward VAT and the liver and altered β-cell function, independent of total adiposity. Several genes belonging to the NF-κB stress pathway were upregulated, suggesting stimulation of proinflammatory mediators.
author: Kim-Anne Lê; Swapna Mahurkar; Tanya L. Alderete; Rebecca E. Hasson; Tanja C. Adam; Joon Sung Kim; Elizabeth Beale; Chen Xie; Andrew S. Greenberg; Hooman Allayee; Michael I. GoranCorresponding author: Michael I. Goran, <email@example.com>.
date: 2011-11
references:
title: Subcutaneous Adipose Tissue Macrophage Infiltration Is Associated With Hepatic and Visceral Fat Deposition, Hyperinsulinemia, and Stimulation of NF-κB Stress Pathway

Adipose tissue inflammation is now recognized as an important mediating link that may help explain the relationship between obesity and several metabolic abnormalities, including insulin resistance (1,2), liver fat accumulation (1,2), and vascular dysfunction (3). This association, however, is not consistent across obese individuals. For example, despite a similar degree of obesity, some obese individuals develop insulin resistance, type 2 diabetes, and nonalcoholic fatty liver disease, whereas others remain protected. This has led to the description of metabolically healthy obese patients (4) who display low hepatic fat content and high insulin sensitivity (SI) together with a favorable inflammatory profile (5). One factor that may explain differences in metabolic risk between individuals with the same degree of body fat is adipose tissue inflammation.

Adipose tissue has long been considered as an inactive tissue, in which its presumed primary role was to store energy excess as triglycerides (TG). It is now widely accepted that adipose tissue acts as an endocrine organ as well through secretion of various adipokines and cytokines and plays a role in regulation of metabolic pathways (6). In obese individuals, excessive storage of free fatty acids (FFA) as TGs may lead to subcutaneous adipose tissue (SAT) dysfunction, resulting in impaired TG storage and possibly diversion of FFA to other tissues, such as the liver or the visceral compartment (7). Such a condition has been associated with high adipose tissue inflammation, characterized by higher secretion of proinflammatory cytokines and macrophage recruitment. Previous research has demonstrated that in adipose tissue from obese mice and humans, such macrophages aggregate around dead adipocytes, forming characteristic ring patterns referred to as crown-like structures (CLS) (8). Furthermore, the macrophages within CLS have been shown to be proinflammatory, and their presence is associated with insulin resistance (9,10).

Hispanics are more prone to an ectopic fat pattern, such as visceral and liver fat accumulation (11), when compared with African Americans; this may be driven partly by impaired SAT storage function and associated with adipose macrophage infiltration (12). Conversely, African Americans are similarly prone to obesity but appear protected against visceral and hepatic fat accumulation.

The purpose of this study, therefore, was to investigate the effect of adipose tissue inflammation on visceral and hepatic fat deposition, SI, and adipose tissue gene expression in two different ethnic groups. We hypothesized that individual differences in adipose tissue inflammation, reflected by the presence of CLS, may explain metabolic abnormalities of obesity, such as hepatic fat deposition and insulin resistance. We therefore recruited participants of Hispanic and African American ethnicity, who are obese and at high risk for type 2 diabetes but show very distinct fat repartition patterns, and investigated whether histological and gene expression differences in adipose tissue contribute to poor metabolic outcomes, such as higher liver fat and insulin resistance.

# RESEARCH DESIGN AND METHODS

## Study participants.

This cross-sectional analysis includes 36 obese (BMI ≥30 kg/m^2^) African American (7 men, 9 women) or Hispanic participants (9 men, 11 women) aged 18–25 years. Participants were excluded if they had taken medications known to affect body composition, been diagnosed with any major illness since birth, or had any diagnostic criteria for diabetes. Written informed consent and assent were received from all participants. This study was approved by the Institutional Review Board of the Keck School of Medicine, University of Southern California.

## Fat quantification.

Whole-body fat was measured by dual-energy X-ray absorptiometry using a Hologic QDR 4500 W (Hologic, Bedford, MA). Abdominal magnetic resonance imaging data were obtained by the Dixon method, with a sensitive three-point chemical-shift fat-water separation method using a 1.5 Tesla Siemens Symphony Maestro whole-body scanner (Siemens AG, Erlangen, Germany) with Numaris 4 software. A two-dimensional multislice breath-hold protocol previously reported by Hussain et al. (13) was adopted to obtain 19 axial images across the abdomen from the dome of the liver to the L2-L3 vertebrae. The slice thickness was 10 mm with no interslice gaps. The fat-only dataset was used in the subsequent quantification of SAT volume and visceral adipose tissue (VAT) volume, whereas the fat fraction dataset was used to assess percent hepatic fat content (hepatic fat fraction \[HFF\]). A commercially available image segmentation and quantification software (SliceOmatic; Tomovision, Inc.) was used. SAT and VAT volumes were computed across all 19 image slices in each participant. HFF was computed as the mean fat fraction of all imaging slices within which the liver was present.

## SAT collection.

Abdominal SAT biopsies were performed lateral to the umbilicus in the skin crease below the abdominal pannus in the lower abdomen using standard sterile techniques. The region was locally anesthetized with 2 mL of 2% lidocaine. Through a 0.5-cm skin incision, SAT was collected with a disposable 0.5-cm diameter biopsy punch using three passes. All tissue samples were stored in formalin or promptly frozen in liquid nitrogen (LN2) and stored at −80°C.

## Adipose tissue immunohistochemistry.

Infiltration of macrophage cell populations into adipose tissue was characterized using cell-specific stains against CD68, an established cell surface marker for macrophages (predilute antibodies from DakoCytomation Corporation). In brief, 5-µm-thick adipose tissue sections were fixed and loaded onto a Biogenex I-6000 machine for incubation with primary antibodies. Multilink biotinylated secondary antibody was then allowed to react for 30 min at room temperature. Slides were then washed with PBS and placed in diaminobenzidine solution and microscopically examined for a positive reaction and counterstained with hematoxylin. All samples were evaluated in a blinded fashion by the pathologist for the presence (+) or absence (−) of macrophage CLS, indicating the presence of dead adipocytes (8). Tissue sections were observed with a Nikon Eclipse E800 light microscope using a ×20 objective, and digital images were captured with a DXM 1200 camera. CLS density (CLS per 400 adipocytes) was determined using a drawing tablet and the Nikon Lucia IMAGE version 4.61 morphometric program. Four representative tissue sections in each sample were observed, and participants were dichotomously categorized as being CLS+ if distinct adipose tissue macrophage clusters were present in any examined high-power field or CLS− if clusters were completely absent in all histological fields for a given participant. To further characterize macrophage type, we performed CD11c immunoreactivity in six CLS+ (four Hispanics, two African Americans) and four CLS− participants (three Hispanics, one African American) using a mouse monoclonal antibody against human CD11c, a specific cell surface marker for type 1 macrophages (Novacastra Mouse Monoclonal Antibody CD11c \[Clone 5D11\]).

## Adipose tissue microarray analysis.

Total RNA was isolated from adipose tissue biopsies using RNeasy Mini kits (Qiagen, Valencia, CA). Biotinylated RNA for hybridization with the Illumina arrays was amplified using the TotalPrep RNA Amplification Kit. Global gene expression was determined for each sample using an Illumina (San Diego, CA) HumanHT-12 v3 expression bead chip through a service provided by the Southern California Genotyping Consortium (<http://scgc.genetics.ucla.edu/>). Quality control was performed and fulfilled the criteria for array hybridization suggested by the Tumor Analysis Best Practices Working Group (14). Data analysis was performed using Genome studio and Partek Inc. (St. Louis, MO). Background corrected and quantile normalized signal intensity values were exported to Partek. Array data have been submitted to the Gene Expression Omnibus (accession: GSE23506). A fold change of 1.3 was used as a criterion for inclusion of genes in functional annotation and pathway analysis. Our study was 70% powered to detect a fold change of 1.3 (<http://bioinformatics.mdanderson.org/MicroarraySampleSize/MicroarraySampleSize.aspx>).

## RT-PCR.

Genes of interest were validated using quantitative RT-PCR. Reverse transcription was performed with 0.5 µg of total RNA and random hexamer primers (Applied Biosystems, Foster City, CA). Inventoried and validated TaqMan probes were used. RT-PCR amplification was performed using an ABI HT7900 instrument (Applied Biosystems). All values are expressed as the average relative expression normalized to GUS-B endogenous control subjects.

## SI and intravenous glucose tolerance test.

An insulin-modified frequently sampled intravenous glucose tolerance test (IVGTT) (15) was performed after an overnight fast. Upon arrival, a catheter was inserted into both arms at the antecubital level. At time 0, glucose (25% dextrose, 0.3 g/kg body wt) was administered intravenously. Insulin (0.02 units/kg body wt, Humulin R \[regular insulin for human injection\]; Eli Lilly, Indianapolis, IN) was injected intravenously at 20 min. Blood samples of glucose and insulin were collected at time points −15, −5, 2, 4, 8, 19, 22, 30, 40, 50, 70, 100, and 180 min and of FFA, TG, cholesterol, and cytokines at time −15. Glucose and insulin values obtained from the IVGTT were entered into the MINMOD Millenium 2003 computer program (version 5.16, Bergman, USC) to determine SI, glucose effectiveness, acute insulin response (AIR), and disposition index (DI) (15). Homeostasis model assessment-insulin resistance (HOMA-IR) was calculated according to the following formula: fasting insulin \[mU/mL\] × fasting glucose \[mmol/L\]/22.5, while HOMA–β-cell = 20 × fasting insulin \[mU/mL\]/fasting glucose \[mmol/L\] − 3.5.

## Blood analysis.

Blood samples from all time points taken during the IVGTT were centrifuged immediately for 10 min at 2,500 revolutions per minute at 8–10°C and frozen at −70°C until analysis. Glucose was assayed in duplicate on a Yellow Springs Instrument 2700 Analyzer (Yellow Springs, OH) using the glucose oxidase method. Insulin was assayed in duplicate using a specific human insulin ELISA kit from Linco (St. Charles, MO), and FFA were quantified using a colorimetric kit (NEFA-HR(2); Wako Diagnostics, Richmond, VA). TGs and total, LDL, and HDL cholesterol were measured using the Kodak Ektachem DT slide assay. Circulating inflammatory mediators including plasminogen activator inhibitor 1, monocyte chemoattractant protein 1 (MCP-1), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and hepatocyte growth factor were measured in batch using multiplex Luminex assays (Linco Research). High-sensitivity C-reactive protein was measured chemically using ADVIA 1800 Chemistry System (Siemens Healthcare Diagnostics, Deerfield, IL).

## Statistical methods.

All data are means ± SD, unless otherwise specified. Statistical analyses were performed using STATA 11.0 (Stata Corporation, College Station, TX). *P* values \< 0.05 were considered statistically significant. Values for high-sensitivity C-reactive protein were log-transformed to reach a normal distribution. Unadjusted comparisons for parameters between adipose inflammatory status were done using Student *t* tests. Adjustments of comparisons for sex, ethnicity, total fat, and VAT were performed using ANCOVA when appropriate. Because of the large SDs of HOMA–β-cell, SI, AIR, and DI, these parameters were tested using the nonparametric Wilcoxon signed-rank test. χ^2^ Tests were performed to assess the effect of ethnicity and sex on inflammatory status. Correlation analyses were done using Pearson correlation tests. Differentially expressed genes were determined using one-way ANOVA and exported to Ingenuity pathway analysis 8.6 (Ingenuity Systems, Redwood City, CA).

# RESULTS

## Clinical and histological data.

A total of 36 obese participants with an average BMI of 35.6 ± 3.9 kg/m^2^ completed the study (mean age 21.2 ± 2.3 years; 55% Hispanics, 55% women). We first carried out histological analyses of adipose biopsies to determine the presence of aggregated macrophages as CLS. Mean section area was 14.0 ± 5.1 mm^2^; 16 participants showed presence of CLS (CLS+), whereas there were no signs of CLS in the remaining 20 participants (CLS−). In the CLS+ participants, the mean number of CLS per 400 adipocytes was 12.9 ± 17.9, with a median of 5.4 and an interquartile range of 2.4–14.2. The characteristics of the participants, stratified by CLS status, are shown in Table 1. Of note, CLS+ participants were equally distributed among men and women as well as Hispanics and African Americans (χ^2^ tests: *P* \> 0.05), even after adjusting for total fat and VAT. This suggests that major differences with respect to the presence of CLS are not driven by sex or ethnicity in our study population. In the whole group, CLS+ individuals had increased VAT (3.7 ± 1.3 vs. 2.6 ± 1.6 L; *P* = 0.04), HFF (9.9 ± 7.3 vs. 5.8 ± 4.4%; *P* = 0.03), fasting TNF-α (20.8 ± 4.8 vs. 16.2 ± 5.8 pg/mL; *P* = 0.01), and insulin concentrations (20.8 ± 2.6 vs. 9.7 ± 6.5 mU/mL; *P* = 0.0007), independent of sex, ethnicity, total fat, and visceral fat volume (Fig. 1). Markers of insulin resistance, including fasting glucose, fasting insulin, HOMA-IR, and HOMA-β, were also significantly higher in the CLS+ group, compared with the CLS−, and these comparisons remained significant after adjusting for covariates. DI, reflecting β-cell function, was significantly lower in the CLS+ group (1,559 ± 984 vs. 2,024 ± 829 ×10^−4^ min^−1^; *P* = 0.03), whereas SI, glucose effectiveness, and AIR were not significantly different (Table 1). HDL cholesterol concentrations tended to be lower in CLS+ participants (*P* = 0.09), but there were no differences in fasting TGs and total and LDL cholesterol concentrations. In a subset of participants, we performed further immunohistochemical studies to examine the presence of CD11c+ immunoreactivity, to detect the presence of dendritic cells. Of the six participants with CLS+, four of them showed positive CD11c immunoreactivity staining, whereas it was completely absent in all four CLS− participants (Fig. 2). CD11c+ cells are a subclass of macrophages, called dendritic cells, which have been demonstrated to be proinflammatory and linked to systemic insulin resistance (10,16).

Anthropometric, body composition, SI, and plasma parameters stratified by adipose tissue inflammatory status

|  | CLS− | CLS+ | *P* value |
|----|----|----|----|
| *n* | 20 | 16 |  |
| Number of CLS (per 400 adipocyte) | 0 ± 0 | 12.9 ± 17.9 | 0.002 |
| Age (years) | 21.6 ± 2.2 | 21.1 ± 2.4 | NS |
| Sex (men/women) | 8/12 | 8/8 | NS |
| Ethnicity (Hispanic/African American) | 10/10 | 10/6 | NS |
| Body composition |  |  |  |
|  BMI (kg/m^2^) | 35.1 ± 3.3 | 36.3 ± 4.1 | NS |
|  Total fat (%) | 37.7 ± 7.4 | 37.3 ± 6.3 | NS |
|  SAT (L) | 16.0 ± 4.9 | 17.3 ± 3.9 | NS |
|  VAT (L) | 2.6 ± 1.6 | 3.7 ± 1.3 | 0.04 |
|  HFF (%) | 5.8 ± 4.4 | 9.9 ± 7.3 | 0.03 |
| SI |  |  |  |
|  Fasting glucose (mg/dL) | 86.8 ± 5.3 | 94.4 ± 9.3 | 0.005 |
|  Fasting insulin (mU/mL) | 9.7 ± 6.6 | 20.9 ± 10.6 | \<0.001 |
|  HOMA-IR | 2.0 ± 1.4 | 4.4 ± 2.2 | \<0.001 |
|  HOMA–β-cell | 191.4 ± 125.4 | 399 ± 301 | 0.01 |
|  Acute insulin response (mU/mL × 10 min) | 1,320 ± 912 | 1,116 ± 796 | NS |
|  SI (×10^−4^ min^−1^/mU/mL) | 1.9 ± 0.7 | 1.3 ± 0.9 | NS |
|  DI (×10^−4^ min^−1^) | 2,024 ± 829 | 1,559 ± 984 | 0.03 |
| Lipid metabolism |  |  |  |
|  FFAs (mmol/L) | 0.87 ± 0.19 | 0.83 ± 0.15 | NS |
|  TGs (mg/dL) | 113 ± 59 | 102 ± 48 | NS |
|  Cholesterol (mg/dL) |  |  |  |
|   Total | 155 ± 31 | 158 ± 35 | NS |
|   LDL | 81 ± 26 | 94 ± 39 | NS |
|   HDL | 50 ± 11 | 43 ± 13 | 0.09 |
| Inflammation |  |  |  |
|  MCP-1 (pg/mL) | 349 ± 182 | 379 ± 97 | NS |
|  IL-8 (pg/mL) | 9.7 ± 3.3 | 11.4 ± 3.2 | NS |
|  TNF-α (pg/mL) | 16.2 ± 5.8 | 20.8 ± 4.8 | 0.01 |
|  HGF (ng/mL) | 2.1 ± 0.8 | 2.1 ± 0.8 | NS |
|  PAI-1 (ng/mL) | 104 ± 44 | 121 ± 43 | NS |
|  hs-CRP (mg/L) | 7.7 ± 16.2 | 6.7 ± 8.3 | NS |
|  Leptin (ng/mL) | 45 ± 22 | 51 ± 28 | NS |
|  Adiponectin (mg/mL) | 17 ± 6 | 14 ± 5 | NS |

Data are means ± SD. HGF, hepatocyte growth factor; hs-CRP, high-sensitivity C-reactive protein; NS, not significant; PAI-1, plasminogen activator inhibitor 1.

We then investigated whether presence of CLS translated into the same phenotypes between Hispanics and African Americans. When analyses were stratified by ethnicity, presence of CLS was specifically associated with higher VAT (2.8 ± 0.9 vs. 1.6 ± 0.9 L; *P* = 0.02) and glucose (100.1 ± 8.6 vs. 84.4 ± 5.6 mg/dL; *P* \< 0.001) in African Americans. In Hispanics, CLS was associated with higher TNF-α (22.6 ± 3.8 vs. 16.5 ± 5.8 pg/mL; *P* = 0.01) and a trend for lower SI (1.6 ± 0.4 vs. 2.1 ± 0.2 \[×10^−4^ min^−1^/mU/mL\]; *P* = 0.06).

## Adipose tissue gene expression analysis.

In the same participants, we subsequently assessed gene expression in SAT biopsies. Of the 23,000 known annotated genes analyzed on the Illumina Human HT-12 chip, 375 genes (∼2%) were differentially expressed between the CLS+ and CLS− groups based on a detection *P* value \< 0.05. Table 2 shows the top 15 differentially up- and downregulated genes in CLS+ compared with CLS− individuals. Based on Gene Ontology descriptions, molecules involved in inflammatory disease, such as matrix metalloproteinase-9 (*MMP9*; fold change: +4.8; *P* = 0.0004), interferon γ-inducible protein 30 (fold change: +2.2; *P* = 0.003), and IL-1 receptor antagonist (fold change: +2.0; *P* = 0.03), were among the most upregulated genes in CLS+ subjects compared with CLS− individuals. Genes involved in response to inflammation were also upregulated, including lipopolysaccharide binding protein (fold change: +2.0; *P* = 0.004), TNF receptor superfamily, member 11b (fold change: +1.4; *P* = 0.01), *MCP-1* (fold change: +1.6; *P* = 0.02), and monocyte antigen *CD14* (fold change: +1.5; *P* = 0.01). By comparison, insulin receptor substrate (*IRS*)-*1* (fold change: −1.6; *P* = 0.02) and *IRS-2* (fold change: −1.5; *P* = 0.01), which play central roles in the insulin signaling cascade, were downregulated in CLS+ individuals. Importantly, the differential expression of these inflammatory and insulin signaling genes was observed in both Hispanics and African Americans. On the basis of their relevance to inflammatory pathways we selected the following genes for RT-PCR validation: *CD14*, *MMP9*, suppressor of cytokine signaling 3, and *IRS-1.* In agreement with our hypothesis and the microarrays results, CLS+ individuals showed upregulation of *CD14* (1.7 ± 0.9 vs. 0.9 ± 0.3; *P* = 0.02) and *MMP9* (1.3 ± 1.1 vs. 0.3 ± 0.2; *P* = 0.01) compared with CLS− individuals. The suppressor of cytokine signaling 3 was upregulated (1.5 ± 1.9 vs. 0.8 ± 0.5; *P* = 0.2) and *IRS1* was downregulated in CLS+ individuals (1.0 ± 0.6 vs. 1.4 ± 1.2; *P* = 0.3), but these results did not reach significance. Figure 3 shows a schematic representation of the differentially expressed molecules and their functions.

Top 15 up- and downregulated genes in CLS+ compared with CLS− individuals

| Upregulated in CLS+ individuals |  |  |  | Downregulated in CLS+ individuals |  |  |  |
|----|----|----|----|----|----|----|----|
| Unigene official gene symbol | Gene name | *P* value | Fold change | Unigene official gene symbol | Gene name | *P* value | Fold change |
| *MMP9* | matrix metallopeptidase 9 | 0.0004 | +4.8 | *KRT15* | keratin 15 | 0.04 | −2.2 |
| *SPP1* | secreted phosphoprotein 1 | 0.01 | +3.2 | *CIDEA* | cell death-inducing *DFFA*-like effector a | 0.04 | −1.9 |
| *SLC2A5* | solute carrier family 2 (facilitated glucose/fructose transporter), member 5 | 0.0001 | +2.7 | *COBL* | cordon-bleu homolog (mouse) | 0.01 | −1.9 |
| *PLA2G7* | platelet-activating factor acetylhydrolase, plasma | 0.005 | +2.6 | *COL6A6* | collagen, type VI, α-6 | 0.004 | −1.8 |
| *IFI30* | interferon, γ-inducible protein 30 | 0.003 | +2.2 | *CEACAM6* | carcinoembryonic antigen-related cell adhesion molecule 6 | 0.04 | −1.8 |
| *PLA2G2A* | phospholipase A2, group IIA | 0.003 | +2.1 | *S100P* | S100 calcium binding protein P | 0.02 | −1.6 |
| *LBP* | lipopolysaccharide binding protein | 0.004 | +2.1 | *GSDMB* | gasdermin B | 0.02 | −1.6 |
| *ITGAX* | integrin, α-X | 0.006 | +2.0 | *CISH* | cytokine inducible SH2-containing protein | 0.02 | −1.6 |
| *IL1RN* | IL-1 receptor antagonist | 0.03 | +2.0 | *ALDH3B2* | aldehyde dehydrogenase 3 family, member B2 | 0.04 | −1.6 |
| *CLIC6* | chloride intracellular channel 6 | 0.03 | +2.0 | *ADH1A* | alcohol dehydrogenase 1A (class I), α-polypeptide | 0.006 | −1.6 |
| *CHI3L2* | chitinase 3-like 2 | 0.001 | +2.0 | *IRS1* | insulin receptor substrate 1 | 0.02 | −1.6 |
| *HMOX1* | heme oxygenase 1 | 0.0001 | +2.0 | *FHOD3* | formin homology 2 domain containing 3 | 0.02 | −1.6 |
| *ACP5* | acid phosphatase 5 | 0.007 | +2.0 | *IRS2* | insulin receptor substrate 2 | 0.01 | −1.5 |
| *PRND* | prion protein 2 | 0.005 | +1.9 | *RAP1GAP* | RAP1 GTPase activating protein | 0.03 | −1.5 |
| *HP* | haptoglobin | 0.009 | +1.8 | *ADH1B* | alcohol dehydrogenase 1B (class I), β-polypeptide | 0.02 | −1.5 |

Positive fold change, upregulated in individuals with CLS (CLS+); negative fold change, downregulated in individuals with CLS (CLS−).

To gain further insight into the role of these 375 differentially expressed genes, we performed a pathway analysis using Ingenuity Pathway Analysis Systems and identified 31 significantly differentially regulated pathways (some of which are listed in Table 3). The first few differentially regulated pathways were related to liver disease or injury, including: *1*) liver X receptor/retinoid X receptor activation pathways, which play a major role in hepatic lipid synthesis; *2*) hepatic cholestasis, a metabolic disease resulting from abnormal bile flow; *3*) bile acid biosynthesis; *4*) xenobiotic detoxification by cytochrome P450 enzymes; and *5*) hepatic fibrosis and hepatic stellate cell activation. Of note, molecules found in these various pathways included *MMP9*; lipopolysaccharide binding protein; *CD14*, IL-1-receptor-antagonist; TNF receptor superfamily, member 11b; and *MCP1*, which all belong to the nuclear factor-κB (NF-κB) signaling pathway.

Differentially regulated pathways between CLS+ and CLS− individuals

| Canonical pathway | No. of genes | *P* value | Symbol |
|----|----|----|----|
| Liver X receptor/retinoid X receptor activation | 9 | 4.20 E-05 | CD14, CCL2, TNFRSF11B, APOC1, MSR1, MMP9, NGFR, IL1RN, LBP |
| Hepatic cholestasis | 12 | 6.69 E-05 | CD14, CYP27A1, ABCC3, SLCO3A1, TNFRSF11B, IRAK1, ESR1, NGFR, RARA, IL1RN, LBP, HSD3B7 |
| Bile acid biosynthesis | 7 | 9.26 E-05 | CYP27A1, ALDH1A3, ADHFE1, DHRS9, ADH1A, ADH1B, HSD3B7 |
| Metabolism of xenobiotics by cytochrome P450 | 10 | 1.17 E-04 | AKR1C2, ALDH1A3, CYP2F1, ALDH3B2 (includes EG:222), ADHFE1, CYP2C9, CYP2S1, DHRS9, ADH1A, ADH1B |
| Atherosclerosis signaling | 10 | 1.4 E-04 | PDGFA, CXCR4, CCL2, ITGB2, ALOX5, MSR1, MMP9, PLA2G2A, TNFRSF12A, IL1RN |
| Hepatic fibrosis/hepatic stellate cell activation | 11 | 2.7 E-04 | CD14, PDGFA, TIMP1, CCL2, EDNRA, TNFRSF11B, AGTR1, MMP9, NGFR, EGFR, LBP |
| IL-10 signaling | 7 | 6.6 E-04 | CD14, FCGR2B, CCR1, IL1RN, LBP, FCGR2A, HMOX1 |
| Fatty acid metabolism | 9 | 9.4 E-04 | ALDH1A3, CYP2F1, ADHFE1, AUH, CYP2C9, CYP2S1, DHRS9, ADH1A, ADH1B |
| Complement system | 5 | 1.2 E-03 | C5AR1, C1QC, C2, C3AR1, CFB |
| Fcγ receptor-mediated phagocytosis in macrophages and monocytes | 8 | 1.71 E-03 | LYN, FCGR1A, PLD3, FGR, SYK, FCGR2A, WAS, HMOX1 |
| Lipopolysaccharide/IL-1-mediated inhibition of retinoid X receptor function | 12 | 2.1 E-03 | CD14, ABCC3, IL4I1, TNFRSF11B, ALDH1A3, APOC1, IRAK1, ALDH3B2 (includes EG:222), CYP2C9, NGFR, RARA, LBP |
| Acute phase response signaling | 11 | 2.8 E-03 | TNFRSF11B, IRAK1, SERPINA3, C2, NGFR, HAMP, IL1RN, HP, LBP, HMOX1, CFB |
| Dendritic cell maturation | 10 | 2.9 E-03 | FCGR2B, CD86, TNFRSF11B, TYROBP, FCGR1A, NGFR, IL1RN, TREM2, FCGR2A, FCGR1B |
| IL-9 signaling | 4 | 6.8 E-03 | IRS1, BCL3, IRS2, CISH |
| PPAR signaling | 5 | 1.5 E-02 | PDGFA, TNFRSF11B, CITED2, NGFR, IL1RN, PPARGC1A |
| IL-8 signaling | 9 | 2.01 E-02 | ITGB2, ITGAX, IRAK1, MMP9, PLD3, GNAI1, ANGPT2, EGFR, HMOX1 |
| IL-6 signaling | 6 | 2.07 E-02 | CD14, TNFRSF11B, NGFR, IL1RN, LBP, TNFAIP6 |
| Role of macrophages, fibroblasts, and endothelial cells in rheumatoid arthritis | 13 | 3.6 E-02 | PDGFA, F2RL1, TNFRSF11B, FCGR1A, CFB, C5AR1, CCL2, FZD2, IRAK1, NGFR, HP, IL1RN, TNFSF13B |
| Type 2 diabetes signaling | 1.26E00 | 3.8E-02 | IRS1, TNFRSF11B, IRS2, PKM2, NGFR, SMPD1 |

PPAR, peroxisome proliferator–activated receptor.

# DISCUSSION

Obesity is associated with inflammation, which may play an important role in fatty liver disease and insulin resistance (1,2,17,18). In this study, we showed that in a group of obese Hispanics and African Americans, 44% of the participants had adipose tissue inflammation, which was associated with higher amounts of VAT and liver fat, hyperinsulinemia, and reduced β-cell function. At the molecular level, individuals with CLS showed upregulation of several genes belonging to the NF-κB stress pathway.

In obese individuals, inflammatory processes are thought to originate from the excessive accumulation of fat in the adipose tissue, where it translates into recruitment of macrophages around dead adipocytes, forming ring patterns known as CLS (12). Consistent with a role for CLS in adipose tissue inflammation, we demonstrated the presence of a subclass of proinflammatory macrophages, CD11c+ dendritic cells, which have been linked to systemic insulin resistance, only in adipose tissue of subjects with CLS (10,16). Moreover, we demonstrated that in obese Hispanics and African Americans, VAT, liver fat, and circulating TNF-α were significantly higher in individuals with CLS in fat biopsies, independent of ethnicity, sex, total body fat, and SAT. Such accumulation of fat in the visceral compartment and the liver markedly increases the risk for type 2 diabetes, and these metabolic abnormalities have been recognized as independent features of the metabolic syndrome (19,20). Indeed, inflammation is also closely associated with insulin resistance, both in the liver and the adipose tissue (21–23). This may be because of decreased insulin signaling by inflammatory mediators such as IL-1 and TNF-α (24). Consistent with these previous observations, we show that individuals with CLS (CLS+) had increased fasting glucose and insulin, as well as decreased DI, reflecting altered glucose homeostasis and β-cell function.

We further investigated the effect of inflammation separately in Hispanics and African Americans. The prevalence of individuals with inflammation was equally distributed among ethnicities, suggesting that there is no existing ethnic predisposition to adipose tissue inflammation. Previous reports have shown that Hispanics are more prone to accumulation of lipids in the VAT and the liver (11). Given the tight link between ectopic fat and inflammation, we therefore expected Hispanics to have a higher degree of inflammation. However, our results show that inflammation may occur in Hispanics regardless of VAT amount. Moreover, presence of CLS was associated with lower SI and increased plasma TNF-α concentrations. In contrast, African Americans usually display lower amounts of VAT; in this ethnic group, we found that VAT was significantly increased only by the presence of adipose tissue inflammation. Although we cannot establish any causal relationship at this point, these ethnic discrepancies suggest that in Hispanics, presence of inflammation is not a major player in these adipose tissue depots accumulation, but is instead associated with the development of systemic inflammation and insulin resistance. By contrast, in African Americans, who are usually protected against VAT accumulation, presence of adipose tissue inflammation may reflect a generalized increase in adipocyte depot mass and activation of inflammatory pathways. This suggests that presence of adipose tissue inflammation may be linked to distinct metabolic outcomes, depending on ethnicity.

Based on these clinical observations, we investigated how different gene expression patterns between CLS+ and CLS− individuals could provide a functional link between adipose tissue inflammation, hepatic fat accumulation, and insulin resistance. As a result, several molecules related to the NF-κB pathway were upregulated in CLS+ individuals. NF-κB pathway mediates important stress responses and activates several proinflammatory cascades (25). Classically, its activation requires the binding of proinflammatory stimuli such as TNF-α, IL-1, or bacterial lipopolysaccharide to their appropriate membrane receptors, which subsequently trigger the activation cascade (26). Activation of NF-κB results in enhanced transcription of IL-6, IL-8, and TNF-α, which trigger stress and inflammatory pathways and further stimulate the NF-κB pathway (21). NF-κB also leads to increased MCP-1, which locally recruits macrophages, and MMP9, which plays a role in inflammation-mediated tissue remodeling, such as adipocyte size expansion (27) and fibrosis in the liver (28). Notably, all of these molecules were upregulated in CLS+ individuals in the current study, reflecting a higher degree of inflammation and macrophage activation (Fig. 3). In mononuclear cells from obese individuals, the NF-κB pathway is also activated, contributing to higher blood concentrations of proinflammatory mediators such IL-6, TNF-α, and MMP9 (29). This suggests that CLS-associated macrophages may have similar roles in adipose tissue, thus contributing to local release of proinflammatory cytokines (27). Using pathway analysis, we also observed that the most differentially regulated pathways were related to liver functions/diseases, such as liver X receptor/retinoid X receptor activation, hepatic cholestasis, and hepatic fibrosis pathways, with activation of several genes related to inflammatory processes, and more specifically to the NF-κB pathway. These pathways play pivotal roles in liver injury and may lead to increased hepatic lipid synthesis (30–32).

Our present results show that CLS may be found in some but not all obese individuals, independently of their ethnic background or sex, and is associated with increased VAT, hepatic fat content, and insulin resistance (33). Although our results are based on associations, when evaluated together with existing literature, the findings collectively suggest that with increasing obesity, adipocytes enlarge, until reaching a threshold (33). This may promote adipocyte death, macrophage aggregation, and CLS formation (8). The ensuing inflammatory activity may trigger the NF-κB stress pathway and additional remodeling mechanism, characterized by increased MMP9 protease expression (34,35). Excess TG accumulation in adipose tissue may subsequently spill over to the systemic circulation and in turn accumulate in ectopic tissues, such as VAT and the liver (7,36), which may both contribute to decreased SI. The fact that in Hispanics high amounts of VAT are not necessarily associated with adipose tissue inflammation suggests that such relationships may be more subtle and possibly depend on other environmental and/or genetic factors.

One limitation of our study is the absence of nutritional data. Both high-fat or high-fructose diets induce insulin resistance and inflammation (37). It remains therefore possible that differences in dietary intakes may contribute to the inflammatory status of adipose tissue. Whether the nutritional status may affect adipose tissue inflammation and the presence of CLS over the long run remains to be investigated. Moreover, this study was limited to African Americans and Hispanics, and obesity duration was not collected. It remains therefore to determine how the onset and duration of obesity may affect adipose tissue inflammation in these populations. Finally, we did not separate adipocytes from other cell types. Further studies using cell sorting will be required to discriminate the expression profile between cell types within the adipose tissue.

In conclusion, this study demonstrates that macrophage infiltration in SAT from obese individuals is equally distributed between sexes and ethnicities. Presence of inflammation is associated with higher VAT and hepatic fat content, as well as higher fasting glucose and insulin and reduced β-cell function, independent of total fat. These phenotypes may be attributed to upregulation of major gene pathways involved in proinflammatory cascades, such as the NF-κB stress pathway. Further intervention studies will be required to assess the time course of such metabolic alterations and establish causal relationship between adipose tissue inflammation, hepatic fat accumulation, and development of insulin resistance.

## ACKNOWLEDGMENTS

This work was supported by grants from the American Diabetes Association; National Institutes of Health Grant R01-DK-082574 to A.S.G.; the Robert C. and Veronica Atkins Foundation Grant to A.S.G. and M.I.G.; and the U.S. Department of Agriculture, Agricultural Research Service, under agreement No. 58-1950-7-70 to A.S.G. K.-A.L. is supported by a grant from the Swiss National Science Foundation (PBLA33-122719).

No potential conflicts of interest relevant to this article were reported.

K.-A.L. designed the study, contributed to data and sample collection, analyzed data, wrote the manuscript, and reviewed and edited the manuscript. S.M. analyzed data and reviewed and edited the manuscript. T.L.A., R.E.H., and T.C.A. contributed to data and sample collection and reviewed and edited the manuscript. J.S.K. reviewed and edited the manuscript. E.B., C.X., and A.S.G. contributed to data and sample collection and reviewed and edited the manuscript. H.A. designed the study and reviewed and edited the manuscript. M.I.G. designed the study, analyzed data, and reviewed and edited the manuscript.

# REFERENCES


abstract: β-Cell function improves in patients with type 2 diabetes in response to an oral glucose stimulus after Roux-en-Y gastric bypass (RYGB) surgery. This has been linked to the exaggerated secretion of glucagon-like peptide 1 (GLP-1), but causality has not been established. The aim of this study was to investigate the role of GLP-1 in improving β-cell function and glucose tolerance and regulating glucagon release after RYGB using exendin(9-39) (Ex-9), a GLP-1 receptor (GLP-1R)–specific antagonist. Nine patients with type 2 diabetes were examined before and 1 week and 3 months after surgery. Each visit consisted of two experimental days, allowing a meal test with randomized infusion of saline or Ex-9. After RYGB, glucose tolerance improved, β-cell glucose sensitivity (β-GS) doubled, the GLP-1 response greatly increased, and glucagon secretion was augmented. GLP-1R blockade did not affect β-cell function or meal-induced glucagon release before the operation but did impair glucose tolerance. After RYGB, β-GS decreased to preoperative levels, glucagon secretion increased, and glucose tolerance was impaired by Ex-9 infusion. Thus, the exaggerated effect of GLP-1 after RYGB is of major importance for the improvement in β-cell function, control of glucagon release, and glucose tolerance in patients with type 2 diabetes.
author: Nils B. Jørgensen; Carsten Dirksen; Kirstine N. Bojsen-Møller; Siv H. Jacobsen; Dorte Worm; Dorte L. Hansen; Viggo B. Kristiansen; Lars Naver; Sten Madsbad; Jens J. HolstCorresponding author: Nils B. Jørgensen, <firstname.lastname@example.com>.
date: 2013-09
references:
title: Exaggerated Glucagon-Like Peptide 1 Response Is Important for Improved β-Cell Function and Glucose Tolerance After Roux-en-Y Gastric Bypass in Patients With Type 2 Diabetes

Hyperglycemia in patients with type 2 diabetes is resolved shortly after Roux-en-Y gastric bypass (RYGB), suggesting that mechanisms independent of weight loss contribute to the improvement in glycemic control (1–4).

Within 1 month and as early as 5 days after RYGB, β-cell function in response to a meal improves in subjects with type 2 diabetes, and this is accompanied by an increased postprandial glucagon-like peptide (GLP)-1 response (3,5,6). In contrast, after intravenous infusion of glucose, which does not elicit the incretin effect, an improvement in β-cell function is absent (5,7,8). Therefore, it could be speculated that the early improvements in β-cell function after RYGB are due to the enhanced GLP-1 secretion related to eating a meal, but causality has not been established (9).

In patients with type 2 diabetes, energy restriction per se is known to result in improved hepatic insulin sensitivity and decreased hepatic glucose production and, as a result, lowered fasting plasma glucose concentrations (10–12). Similar metabolic changes are seen after RYGB, when energy intake is limited (13,14), and this has led to the proposal that caloric restriction with a subsequent reduction in glucotoxicity, rather than an increased effect of GLP-1, is responsible for the improved β-cell function (14,15).

The aim of this study was to investigate the role of GLP-1 in the improved β-cell function and glucose tolerance seen after RYGB in subjects with type 2 diabetes. This was accomplished by pharmacologically blocking the GLP-1 receptor (GLP-1R) during a liquid meal tolerance test before and after surgery using exendin(9-39) (Ex-9; Bachem AG, Bubendorf, Switzerland), a specific GLP-1R antagonist (16).

Previous studies have documented increased meal-related glucagon secretion after RYGB despite improvements in insulin secretion and sensitivity and exaggerated GLP-1 release (3,17,18). This observation is surprising given the glucagonostatic properties of GLP-1 and insulin (19,20). Therefore, a further aim of this study was to evaluate the interaction between GLP-1 and glucagon release after RYGB in both the fasting and postprandial states.

# RESEARCH DESIGN AND METHODS

Patients with type 2 diabetes were recruited from the Hvidovre Hospital's bariatric surgery program (Hvidovre, Denmark), met the criteria for bariatric surgery (age \>25 years and BMI \>35 kg/m^2^), and had accomplished a mandatory preoperative, diet-induced loss of 8% of total body wt before inclusion. Patients were excluded if they had uncontrolled hypothyroidism, had been taking antithyroid medication or anorectic agents within 3 months before the experiments, or had a fasting C-peptide level \<700 pmol/L. To confirm the diagnosis of type 2 diabetes, an oral glucose tolerance test (OGTT) was performed ≤1 month before the first experiment.

The study was approved by the Municipal Ethical Committee of Copenhagen (reg. nr. H-A-2008-080-31742), was in accordance with the Declaration of Helsinki II, and was registered with [clinicaltrials.gov](http://clinicaltrials.gov) (NCT01579981) and the Danish Data Protection Agency. Written informed consent was obtained from all patients before entering the study. Incretin-based therapies were put on hold for at least 14 days and all other antidiabetic medications for at least 3 days before the first preoperative experiment. Insulin analogs were replaced with NPH insulin at least 2 weeks before the first experiment. RYBG was performed as previously described (18). Patients were examined at 3 visits: before, 1 week after, and 3 months after RYGB. Visits consisted of 2 days where the patients were examined during a liquid meal tolerance test with a concurrent patient-blinded, primed, continuous infusion of Ex-9 or isotonic saline in random order. On each study day, patients met at 0800 h after a 10-h overnight fast. Patients were weighed (Tanita Corp., Tokyo, Japan), a catheter was inserted into the antecubital vein of each arm (one for blood sampling and one for infusion), and three fasting blood samples were drawn (−40 to 30 min). A primed continuous infusion of either saline or Ex-9 was initiated at time −30 min using a precision infusion pump (P2000; IVAC Medical Systems, Hampshire, U.K.). Saline was infused at a rate corresponding to the Ex-9 infusion volumes. After infusion was started, participants maintained a fasting state for 30 min to allow the drug to reach target tissues and drug concentrations to stabilize before the meal. Three further baseline samples were drawn just before ingestion of the meal (−10 to 0 min). At time 0 min, a liquid meal (Fresubin Energy Drink, 200 mL, 300 kcal, carbohydrate \[50% of energy\], protein \[15% of energy\], fat \[35% of energy\]; Fresenius Kabi Deutschland, Bad Homburg, Germany) was provided. To estimate gastric emptying, 1 g of paracetamol (Pamol; Nycomed Danmark, Roskilde, Denmark) was crushed to a powder and added to the meal. To avoid dumping after surgery and to obtain a comparable stimulus before and after RYGB, meal ingestion was supervised to ensure even distribution of meal intake over a 30-min period.

Blood was sampled before and frequently following the meal for a total of 4 h (−40, −35, −30, −10, −5, 0, 30, 45, 60, 90, 120, 180, and 240 min). During each test, patients were placed sitting in a reclined position in a hospital bed, and no strenuous activity was allowed.

## Ex-9.

Ex-9 was purchased from Bachem AG. Ex-9 from the same lot was used for all experiments. The peptide was dissolved in sterilized water containing 1% human albumin (Plasma Product Unit PSU; Novo Nordisk A/S, Bagsvaerd, Denmark) and subjected to sterile filtration. The dissolved peptide was dispensed in appropriate volumes and stored frozen (−20°C) under sterile conditions until the day of the experiment. The peptide was demonstrated to be 99.5% pure by high-performance liquid chromatography. The Ex-9 infusion rate of 900 pmol/kg body wt/min was chosen because this had previously been reported to block the GLP-1R by 95% (21,22), and the resulting steady-state concentration of Ex-9 was predicted to be 391 nmol/L using previously published pharmacokinetic parameters (23). Likewise, bolus size was calculated to be 43.000 pmol/kg body wt.

## Sample collection and laboratory analyses.

Blood was collected into clot activator tubes for insulin and C-peptide analysis and into prechilled EDTA tubes for analysis of GLP-1, glucose-dependent insulinotropic polypeptide (GIP), glucagon, glucose, paracetamol, and Ex-9.

Clot activator tubes were left to coagulate for 30 min, whereas EDTA tubes were placed on ice until centrifuged at 4°C. Plasma glucose was measured immediately using the glucose oxidase technique (YSI model 2300 STAT Plus; YSI, Yellow Springs, OH). Samples of GLP-1, GIP, glucagon, and Ex-9 were stored at −20°C. All other samples were frozen and stored at −80°C until batch analysis.

Serum insulin and C-peptide concentrations were determined by AutoDELFIA fluoroimmunoassay (Wallac OY, Turku, Finland). Plasma samples were assayed for total GLP-1 immunoreactivity, as described previously (24). Total GIP was measured using a C-terminally directed antiserum 867 with characteristics similar to the previously employed R65 (25,26). Glucagon was measured with the LINCO assay (Millipore, Billerica, MA) because it does not cross-react with Ex-9 (27). Ex-9 was measured using antibody 3145 raised in rabbits immunized with exendin-4, which shows 100% cross-reactivity with Ex-9 but \<0.01% cross-reactivity with GLP-1, glucagon, or GIP (27). Paracetamol was measured using a calorimetric assay (Roche Diagnostics GmbH, Mannheim, Germany), and HbA~1c~ was measured using high-performance liquid chromatography with a cation exchange column (Tosoh Bioscience, Tokyo, Japan).

## Calculations and statistical analyses.

Fasting glucose and hormone values were calculated as the mean of the time points from the interval −40 to −30 min. Total area under the curve (AUC) was calculated using the trapezoidal model. Baseline values were the mean of the time points from the interval −10 to 0 min. Incremental AUC (I-AUC) was calculated as AUC above baseline.

Insulin resistance was calculated using homeostasis model assessment of insulin resistance (HOMA-IR) using the following equation: Insulin~fasting~ \[pmol/L\] × Glu~fasting~ \[mmol/L\] / (22.5 × 6.945), where Glu is glucose.

Prehepatic insulin secretion rates (ISRs) were calculated by deconvolution of peripheral C-peptide concentrations and application of population-based parameters for C-peptide kinetics using the ISEC software (28,29). ISR is expressed as picomoles × kilograms^−1^ × minutes^−1^. The relationship between glucose concentrations and ISR during the meal, the β-cell glucose sensitivity (β-GS), was characterized as previously described (3). The time to peak paracetamol concentration was used as a marker of gastric emptying.

Data are expressed as means ± SE. Statistical analyses were carried out using Wilcoxon matched pairs signed rank test or Kruskal-Wallis rank sum test, as appropriate. *P* values \<0.05 were considered significant. The analyses were carried out using the R 2.11.1 statistical software package.

# RESULTS

## Patient characteristics.

Eleven patients with type 2 diabetes were recruited for the study. Two patients were excluded from data analysis: one because intravenous cannulation could not be performed on the second experimental day and the other because RYGB was abandoned because of massive intestinal adherences on the basis of earlier gastrointestinal surgery and peritonitis.

Thus, a total of nine patients with type 2 diabetes (age 50 ± 3 years; three females and six males; BMI 39.2 ± 2.4 kg/m^2^; diabetes duration 5.7 ± 1.3 years; HbA~1c~ 6.5 ± 0.3% \[48 ± 4 mmol/mol\]; 2-h OGTT plasma glucose 14.4 ± 1.0 mmol/L) were examined 9 ± 2 days before and 7 ± 1 days (1 week) and 96 ± 2 days (3 months) after RYGB. Preoperatively, one patient was treated with diet alone and eight were treated with metformin either as monotherapy (*n* = 4) or in combination with glimepiride (1), vildagliptin (1), liraglutide (1), or liraglutide and insulin glargine (1). None of the patients received any antidiabetic medication after RYGB. Further comorbidities were hypertension (6), hypercholesterolemia (2), hypothyroidism (*n* = 1, well controlled, with thyroid-stimulating hormone within the normal range), and myocardial infarction 4 years earlier (1). None had records or symptoms of gastroparesis, neuropathy, retinopathy, or nephropathy.

The patients entered the bariatric surgery program 337 ± 40 days before RYGB with a BMI of 42.9 ± 2.8 kg/m^2^. On the day of the OGTT (30 ± 9 days before RYGB), BMI had decreased to 39.5 ± 2.5 kg/m^2^ (92 ± 0.9% of initial BMI), which was not different from that measured on the first experimental day. One week after RYGB, patients had lost 3.9 ± 0.4% (*P* \< 0.01) of preoperative BMI, and after 3 months they had lost 13.5 ± 1.1% (*P* \< 0.01). HbA~1c~ decreased to 5.7 ± 0.2% (39 ± 2 mmol/mol; *P* \< 0.01) 3 months after surgery.

## Ex-9 concentrations.

Mean Ex-9 concentrations from time 0 to 240 min were similar before (404 ± 52 nmol/L) and after RYGB (1 week: 444 ± 65 nmol/L; 3 months: 405 ± 51 nmol/L; *P* = 0.7) and were stable throughout the experiment (Fig. 1).

## Gastric emptying.

Paracetamol profiles are shown in Fig. 1. Before the operation, peak paracetamol concentrations were reached 70 ± 10 min after the start of the meal, but after RYGB they peaked at the first postprandial sample point (30 min) (1 week: 33 ± 2 min \[*P* \< 0.01\]; 3 months: 32 ± 2 min \[*P* \< 0.01\]). GLP-1R blockade had a minor insignificant effect on time to peak paracetamol concentration before the operation (before Ex-9: 60 ± 10 min \[*P* = 0.49\]) but no detectable effect after RYGB (Ex-9 at 1 week: 30 ± 0 min; at 3 months: 30 ± 0 min).

## Fasting glucose and hormone concentrations.

Data regarding fasting glucose and hormone concentrations are listed in Table 1. Following RYGB, fasting glucose, insulin, and C-peptide concentrations decreased, whereas fasting GLP-1, GIP, and glucagon concentrations did not substantially change.

Fasting glucose and hormone concentrations (−40, −35, and −30 min) before infusion of saline or Ex-9

![](3044tbl1)

Changes in glucose and hormone concentrations 30 min after start of infusion, that is, before the meal, are reported in Table 2. Ex-9 infusion caused glucose concentrations to increase significantly at all study times, whereas ISR decreased most before and at least 3 months after the operation. Fasting GLP-1 and GIP concentrations were not affected by Ex-9 infusion, but glucagon concentrations increased significantly compared with the changes observed during saline infusion before and 3 months after RYGB.

Changes in fasting glucose and hormone concentrations after a primed infusion of saline or Ex-9 but before the meal

![](3044tbl2)

## Insulin resistance.

Insulin resistance decreased by ∼50% after RYGB (HOMA-IR before RYGB: 5.72 ± 0.74; 1 week after: 2.71 ± 0.41 \[*P* = 0.004\]; 3 months after: 2.18 ± 0.30 \[*P* = 0.004\]). HOMA-IR did not differ between the days of saline and Ex-9 infusion at any of the three visits (data not shown).

## Postprandial glucose concentrations and insulin secretion.

Data for postprandial glucose concentrations and insulin secretion are reported in Table 3, and glucose and ISR profiles are shown in Fig. 2. Following RYGB, 2-h postprandial glucose concentrations were significantly decreased but peak glucose concentrations were unchanged. Blocking the GLP-1R with Ex-9 caused 2-h and peak glucose concentrations to significantly increase compared with saline at all visits.

Postprandial glucose and hormone responses with saline or Ex-9 infusion

![](3044tbl3)

After the operation, insulin secretion was significantly increased in response to the meal as evidenced by both increased peak ISR and total meal-induced insulin secretion (I-AUC ISR). Ex-9 infusion resulted in significant decreases in peak ISR and I-AUC ISR 1 week and 3 months after RYGB but not before the operation.

## β-Cell glucose sensitivity.

Data for β-GS are shown in Fig. 3. In response to the liquid meal, β-GS increased as a consequence of RYGB. One week after the operation, β-GS had almost doubled (*P* = 0.008), and it remained elevated at 3 months (*P* = 0.027). When the GLP-1R was blocked with Ex-9 before the operation, an insignificant decrease in β-GS (*P* = 0.07) was observed. After RYGB, Ex-9 infusion reduced β-GS to preoperative levels both at 1 week and 3 months after the operation, and these decreases were highly significant (both *P* = 0.004 vs. saline).

## Postprandial GLP-1, GIP, and glucagon concentrations.

After RYGB, I-AUC GLP-1 was increased eightfold, reflecting a greatly elevated meal-induced GLP-1 response (Fig. 4A). Ex-9 infusion resulted in a further augmentation of GLP-1 release both before and after RYGB. The GIP response (Fig. 4B) was not affected by RYGB or Ex-9 infusion.

Meal-induced glucagon secretion (I-AUC glucagon) (Fig. 4C and Table 3) increased after RYGB. Before the operation, I-AUC glucagon was not affected by GLP-1R blockade (*P* = 0.3), but after RYGB, Ex-9 infusion resulted in a marked and significant increase in glucagon secretion upon stimulation by the meal compared with the corresponding day of saline infusion.

# DISCUSSION

One week and 3 months after RYGB, glucose tolerance and β-GS are improved and the meal-related GLP-1 response and glucagon release are increased in subjects with type 2 diabetes, as we have previously demonstrated (3). The key finding in the current study is that pharmacological blockade of the GLP-1R after RYGB reverses the improvements in β-GS and glucose tolerance and increases postprandial glucagon release. These results strongly suggest that the increased endogenous GLP-1 secretion is of great importance for the acute effects of RYGB on β-cell function and for glucose tolerance in patients with type 2 diabetes. Furthermore, we demonstrate that glucagon secretion is inhibited by GLP-1 after RYGB.

Before RYGB, GLP-1R inhibition impaired glucose tolerance because of an increment in fasting and postprandial glucose concentrations. Fasting insulin secretion decreased but meal-induced secretion was unaltered, and β-GS was not significantly reduced when Ex-9 was infused. Fasting glucagon concentrations increased during GLP-1R blockade, but the meal-induced glucagon response was unaltered. The effects of Ex-9 infusion on insulin and glucagon secretion during fasting were similar to those reported in two recent studies of patients with type 2 diabetes (30,31), but contrary to our results, these studies found a pronounced effect of GLP-1R inhibition on insulin and glucagon secretion when endogenous GLP-1 secretion was stimulated. However, both studies used the hyperglycemic clamp technique to keep plasma glucose concentrations fixed, whereas our study allowed plasma glucose concentrations to vary; as a consequence, they were higher on the day of Ex-9 infusion. This greater glucose stimulus is likely to counteract the effects of GLP-1R antagonism on postprandial insulin and glucagon secretion in our study and may explain these seemingly contradictory results.

One week after RYGB, plasma glucose concentrations had improved in both the fasting and postprandial states. After the operation, insulin secretion increased, β-GS doubled, the GLP-1 response was greatly increased, and meal-induced glucagon release was elevated. These results confirm our previous report and demonstrate that RYGB improves β-cell function soon after surgery (3). GLP-1R blockade caused glucose tolerance to deteriorate and β-GS to decrease to preoperative levels. At the same time, postprandial GLP-1 and glucagon responses increased.

Three months after the operation, glucose tolerance was further improved with enhanced insulin and GLP-1 secretion compared with before surgery, and overall glucose control was improved, as evidenced by a decreased HbA~1c~, despite patients not receiving antidiabetic medication. GLP-1R antagonism produced results similar to those observed 1 week after the operation, again returning β-GS to preoperative levels and augmenting the meal-related glucagon response, resulting in an impairment of glucose tolerance.

Before the operation, the weight of patients was stable, but after RYGB they experienced rapid weight loss, amounting to ∼4% within a week. It has been suggested that the acute effects of RYGB on β-cell function in patients with type 2 diabetes are strictly related to reduced energy intake (14,15), although reports on the effects of such acute energy restriction are conflicting (12,32). However, while energy restriction has little or no effect on GLP-1 release, postprandial GLP-1 secretion is greatly increased after RYGB (3,5,14,33–35). Previous reports have shown that the incretin effect after RYGB is normalized in patients with type 2 diabetes and that this cannot be explained simply by weight loss (36,37). Furthermore, in patients with type 2 diabetes, β-cell function in response to a meal or an oral glucose load has consistently been reported to improve within the first month after RYGB but not after gastric restrictive surgery where patients undergo the same postoperative dietary regimen or when β-cell function is evaluated by intravenous glucose infusion (3,5–7,33,38,39). The present data illustrate that the exaggerated GLP-1 response in patients with type 2 diabetes seems to be the main explanation for the improvement in β-cell function after RYGB in response to a meal. Reduced glucotoxicity may play a role in the improved β-cell function after surgery, but it seems to be of minor importance compared with the actions of GLP-1 (40,41). Of note, HOMA-IR was not different between saline and Ex-9 infusion at any visit. Therefore, the changes we observed with GLP-1R blockade are unlikely to be a consequence of adaptation of β-cells to changes in insulin resistance (42). It has been suggested that the large GLP-1 response after RYGB is triggered by the accelerated delivery of nutrients to the enteroendocrine cells of the distal ileum (43,44). We recently reported the gastric emptying of liquids to be greatly accelerated in patients who have received RYGB as measured using a scintigraphic method, and this was associated with an increased release of GLP-1 (45). In agreement with this finding, paracetamol absorption in this study was accelerated after RYGB, supporting such a mechanism of action. GLP-1R blockade increased postprandial GLP-1 release by 60–100% before and after RYGB, a finding that may be related to inhibition of GLP-1R on the enteroendocrine cells that work to feed negatively back on GLP-1 secretion (46). Although GLP-1 inhibits gastric emptying (19), paracetamol absorption kinetics were not significantly changed with Ex-9 infusion, making accelerated gastric emptying an unlikely mechanism by which GLP-1R blockade increases GLP-1 secretion in this study. It must be emphasized that estimation of gastric emptying using this method is limited by the blood sampling frequency, especially after RYGB. Previous studies have indicated that GLP-1 tonically suppresses fasting glucagon concentrations in patients with type 2 diabetes, and we were able to reproduce that finding here (31). The glucagonostatic properties of GLP-1 and the exaggerated response after RYGB would predict a reduced release of glucagon as induced by a meal (19); however, the glucagon response increases after the operation. Eliminating the effect of endogenous GLP-1 with Ex-9 further increased glucagon secretion, implying that meal-induced glucagon release continues to be under the inhibitory control of GLP-1. The mechanism underlying the increased meal-induced glucagon response remains to be elucidated.

Salehi et al. (47) reported that Ex-9 infusion resulted in a more pronounced decrease of insulin secretion when a meal was ingested during a hyperglycemic clamp in nondiabetic patients who received RYGB compared with normal controls. Strengths of the current study include the prospective design, the inclusion of patients with type 2 diabetes, and the use of a physiological meal test, allowing us to demonstrate the effects of endogenous GLP-1 signaling before and after RYGB and to evaluate both β-cell function and glucose tolerance. Therefore, our results expand the observations of Salehi et al.

We used a primed infusion of Ex-9 at a rate of 900 pmol × kg^−1^ × min^−1^. This dose of Ex-9, which resulted in stable plasma concentrations of ∼400 nmol/L on all experimental days, has previously been reported to result in 95% inhibition of the GLP-1R even in the presence of very high GLP-1 concentrations, such as those expected in the portal vascular bed after RYGB upon stimulation by a meal (21,22). GLP-1R blockade did not influence GIP secretion in this study, and it has previously been shown that Ex-9 infusion does not interfere with GIP signaling on the β-cells (48). In our own studies of the effect of GIP in COS-7 cells transfected with the human GIP receptor, there was no effect of Ex-9 in nanomolar to micromolar concentrations (M. Rosenkilde, unpublished observations). Collectively, this shows that Ex-9 specifically inhibits the actions of GLP-1 with respect to the incretin effect.

A limitation of this study is the small sample size of nine patients, which makes the detection of more subtle changes less likely. Despite this, we were able to demonstrate a highly significant effect of GLP-1R inhibition on insulin secretion and β-cell function after RYGB, underscoring the strength of the association.

In light of the marked inhibition of β-cell function, glucose tolerance was only moderately affected during Ex-9 infusion after the operation. This could reflect an increase in the relative importance of non-insulin-mediated glucose uptake for glucose tolerance (49–51), but the study was not designed to evaluate this.

In addition, we did not account for hepatic glucose production in this study, which could already be decreased 1 week after the operation, perhaps as a result of caloric restriction (9). Further studies are needed to investigate this early aspect of glucose metabolism after RYGB.

In vitro studies of the murine GLP-1R suggest that it is constitutively active and inhibited by Ex-9 even in the absence of GLP-1; i.e., Ex-9 is an inverse agonist (52). It is questionable whether this is a property of the human GLP-1R in vivo (53), but if so our results could be influenced by an increased GLP-1R sensitivity to the inverse agonism of Ex-9 after surgery. However, the effects of Ex-9 on basal insulin and glucagon secretion were greatest before the operation and decreased with time from surgery, which makes it highly unlikely that Ex-9 should more potently inhibit GLP-1R signaling after RYGB.

We conclude that an increased effect of GLP-1 as a consequence of hypersecretion is the main explanation for the improved β-cell function after RYGB in response to a meal in patients with type 2 diabetes. Furthermore, the effects of GLP-1 are important in controlling glucagon release and glucose tolerance in these patients after the operation.

## ACKNOWLEDGMENTS

This work was carried out as a part of the program of the UNIK: Food, Fitness & Pharma for Health and Disease (see [www.foodfitnesspharma.ku.dk](http://www.foodfitnesspharma.ku.dk)). The UNIK project is supported by the Danish Ministry of Science, Technology and Innovation. Further support was received from the Danish Diabetes Association, The Novo Nordisk Foundation, and The Strategic Research Council for the Capital Area and the Danish Research Agency (Ministry of Science, Technology and Innovation). The purchase of Ex-9 was made possible through a donation from Desirée & Niels Ydes Foundation.

No potential conflicts of interest relevant to this article were reported.

N.B.J. planned and conducted experiments, researched data, and wrote the manuscript. C.D. conducted experiments, contributed to discussion, and reviewed the manuscript. K.N.B.-M., S.H.J., S.M., and J.J.H. contributed to discussion and reviewed the manuscript. D.W., D.L.H., V.B.K., and L.N. reviewed the manuscript. S.M. and J.J.H. planned experiments. N.B.J. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Parts of this study were presented in oral form at the 48th Annual Meeting of the European Association for the Study of Diabetes, Berlin, Germany, 30 September–1 October 2012, and in poster form at the 72nd Scientific Sessions of the American Diabetes Association, Philadelphia, Pennsylvania, 8–12 June 2012.

This work would not have been possible without the technical assistance of Alis Sloth Andersen and Dorthe Baunbjerg Nielsen (Department of Endocrinology, Hvidovre Hospital) and Lene Brus Albæk (Department of Biomedical Sciences, Faculty of Health Sciences, University of Copenhagen).

# REFERENCES


author: Nigel Irwin; Victor Alan GaultCorresponding author: Victor Alan Gault, <email@example.com>.
date: 2013-09
references:
title: Unraveling the Mechanisms Underlying Olanzapine-Induced Insulin Resistance

Atypical antipsychotics (AAPs) are widely prescribed agents for treatment of schizophrenia and other related psychiatric disorders. Although AAPs were a major development in psychopharmacology, so-called second-generation agents such as olanzapine have exhibited unexpected and unfavorable metabolic side effects. These side effects include weight gain, glucose intolerance, and insulin resistance, all of which increase the likelihood of developing diabetes and cardiovascular disease (1). Nonetheless, how these adverse metabolic effects arise following AAP treatment remains unclear. A key question is whether AAP-associated metabolic impairments are because of the psychiatric illness itself or if they are merely secondary to weight gain.

In this issue, Teff et al. (2) examine the direct effects of second-generation AAPs on insulin resistance and postprandial gut hormone profiles following a mixed meal. The antipsychotic drugs used in these experiments—olanzapine and aripiprazole—were administered to healthy volunteers for 9 consecutive days to exclude potential confounding issues associated with psychiatric disease or potential weight gain. In the clinic, olanzapine therapy tends to result in weight gain and metabolic dysregulation (3), whereas aripiprazole is considered metabolically neutral (4). Thus, Teff et al. logically hypothesized that detrimental effects on meal-related metabolism would be limited to olanzapine. Consistent with this hypothesis, the authors showed that aripiprazole had no effect on body weight, blood pressure, and circulating levels of key blood parameters over the 9-day treatment period. Aripiprazole also did not result in significant changes in postprandial metabolism following a mixed meal. In contrast, olanzapine increased fasting plasma insulin by day 9. This corresponded with postprandial hyperinsulinemia, suggesting that olanzapine had detrimental effects on insulin sensitivity. Indeed, hyperinsulinemic-euglycemic clamps supported this theory. However, aripiprazole also induced insulin resistance despite it generally being considered lacking in metabolic effects.

A unique aspect to the study by Teff et al. (2) was participant ingestion of a mixed meal that elicited both incretin responses (5) and neurally mediated insulin release (6), both of which could be critical for the metabolic effects of centrally acting drugs. Surprisingly, olanzapine administration increased postprandial glucagon-like peptide-1 (GLP-1) secretion and circulating glucagon levels. These responses were unexpected for two reasons. First, GLP-1 is believed to improve insulin sensitivity and reduce weight gain. Second, it directly inhibits glucagon release (7). The authors speculate that other unknown factors, such as glucose-dependent insulinotropic polypeptide (GIP) secretion or cholinergic vagally mediated actions, may mediate the altered postprandial metabolic profile following olanzapine administration. In this regard, it has recently been shown that biologic action of GIP can be potentiated by xenin-25, a peptide cosecreted with GIP from intestinal K cells, which is thought to indirectly activate muscarinic receptors (8). Olanzapine is a well-characterized muscarinic receptor antagonist (9). Thus, interactions between GIP and other vagal inputs could be of importance for the postprandial metabolic effects of AAPs (Fig. 1).

The new data are interesting but need to be corroborated in studies with larger sample sizes to increase power and also to be verified in psychiatric patients. A previous study has shown no effect of olanzapine on gut hormone secretion (10). The direct effect of AAPs on GIP secretion and action certainly needs to be considered. Although weight gain did not affect data interpretation, Teff et al. (2) clearly acknowledge that adiposity could be a confounding factor. In addition, the nutrient composition of the mixed meal could have important implications on GIP and GLP-1 secretion. Both incretin hormones are strongly released in response to carbohydrates, but GIP is released much more powerfully than GLP-1 in response to fat (11).

Taken together, the data illustrate that olanzapine can induce insulin resistance and postprandial hormonal dysregulation independently of weight gain. Although the regulatory mechanisms involved remain to be fully elucidated, the well-characterized weight gain following prolonged olanzapine administration would likely exacerbate these effects. Thus, interventions to inhibit weight gain in patients receiving AAP therapy may only be partially effective in preventing metabolic disease. The report by Teff et al. (2) should stimulate continued efforts aimed at resolving the direct detrimental metabolic effects of AAPs that may ultimately lead to improved treatment options for these patients.

## ACKNOWLEDGMENTS

No potential conflicts of interest relevant to this article were reported.

# REFERENCES


date: 2017-03
title: EARLY CANCER DIAGNOSIS SAVES LIVES, CUTS TREATMENT COSTS

**3 FEBRUARY 2017 \| GENEVA -** New guidance from WHO, launched ahead of World Cancer Day (4 February), aims to improve the chances of survival for people living with cancer by ensuring that health services can focus on diagnosing and treating the disease earlier.

New WHO figures released this week indicate that each year 8.8 million people die from cancer, mostly in low- and middle-income countries. One problem is that many cancer cases are diagnosed too late. Even in countries with optimal health systems and services, many cancer cases are diagnosed at an advanced stage, when they are harder to treat successfully.

"Diagnosing cancer in late stages, and the inability to provide treatment, condemns many people to unnecessary suffering and early death," says Dr Etienne Krug, Director of WHO's Department for the Management of Noncommunicable Diseases, Disability, Violence and Injury Prevention.

"By taking the steps to implement WHO's new guidance, healthcare planners can improve early diagnosis of cancer and ensure prompt treatment, especially for breast, cervical, and colorectal cancers. This will result in more people surviving cancer. It will also be less expensive to treat and cure cancer patients."

All countries can take steps to improve early diagnosis of cancer, according to WHO's new Guide to cancer early diagnosis.

**The three steps to early diagnosis are:**

Improve public awareness of different cancer symptoms and encourage people to seek care when these arise.

Invest in strengthening and equipping health services and training health workers so they can conduct accurate and timely diagnostics.

Ensure people living with cancer can access safe and effective treatment, including pain relief, without incurring prohibitive personal or financial hardship.

Challenges are clearly greater in low- and middle-income countries, which have lower abilities to provide access to effective diagnostic services, including imaging, laboratory tests, and pathology – all key to helping detect cancers and plan treatment. Countries also currently have different capacities to refer cancer patients to the appropriate level of care.

WHO encourages these countries to prioritize basic, high-impact and low-cost cancer diagnosis and treatment services. The Organization also recommends reducing the need for people to pay for care out of their own pockets, which prevents many from seeking help in the first place.

Detecting cancer early also greatly reduces cancer's financial impact: not only is the cost of treatment much less in cancer's early stages, but people can also continue to work and support their families if they can access effective treatment in time. In 2010, the total annual economic cost of cancer through healthcare expenditure and loss of productivity was estimated at US\$ 1.16 trillion.

Strategies to improve early diagnosis can be readily built into health systems at a low cost. In turn, effective early diagnosis can help detect cancer in patients at an earlier stage, enabling treatment that is generally more effective, less complex, and less expensive. For example, studies in high-income countries have shown that treatment for cancer patients who have been diagnosed early are 2 to 4 times less expensive compared to treating people diagnosed with cancer at more advanced stages.

Dr Oleg Chestnov, WHO Assistant Director-General for Noncommunicable Diseases and Mental Health, notes: "Accelerated government action to strengthen cancer early diagnosis is key to meet global health and development goals, including the Sustainable Development Goals (SDGs)."

SDG 3 aims to ensure healthy lives and promote well-being for all at all ages. Countries agreed to a target of reducing premature deaths from cancers and other noncommunicable diseases (NCDs) by one third by 2030. They also agreed to achieve universal health coverage, including financial risk protection, access to quality essential health-care services, and access to safe, effective, quality and affordable essential medicines and vaccines for all. At the same time, efforts to meet other SDG targets, such as improving environmental health and reducing social inequalities can also help reduce the cancer burden.

Cancer is now responsible for almost 1 in 6 deaths globally. More than 14 million people develop cancer every year, and this figure is projected to rise to over 21 million by 2030. Progress on strengthening early cancer diagnosis and providing basic treatment for all can help countries meet national targets tied to the SDGs.

Available from: <http://www.who.int/mediacentre/news/releases/2017/early-cancer-costs/en/>


abstract: A report on the 44th Annual *Drosophila* Research Conference, Chicago, USA, 5-9 March, 2003.
author: Stephen Richards
date: 2003
institute: 1Human Genome Sequencing Center, Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77006, USA. E-mail: email@example.com
title: How is the *Drosophila* research community making use of the genome sequence?

*Drosophila* researchers have capitalized on the genome sequence in the three years since it was released. From identifying paralogs and orthologs to microarray analysis of specific cell types, it is clear that the genome sequence is changing approaches to *Drosophila* research. In this review of the 2003 *Drosophila* research conference, I have focused on talks illustrating the various ways in which the genome sequence is being used.

The Larry Sandler Award is presented annually to the graduate student with the best thesis that uses *Drosophila* as a model system. This year's recipient, Sinisa Urban (University of Cambridge, UK), gave perhaps the best presentation of the meeting. His thesis focused on how the cell controls the release of the Spitz epidermal growth factor (EGF) signal. In *Drosophila*, a single EGF receptor is used repeatedly in over 60 different contexts; control of how the signal is released is therefore critical. Spitz is a transmembrane protein, which must be cleaved by a protease to release the signaling portion for secretion. Firstly, Urban presented biochemical data showing that cleavage of the Spitz protein by Rhomboid occurs in the Golgi apparatus and is followed by glycosylation and secretion of the Spitz signaling portion. Secondly, using biochemical and mutagenic approaches, he demonstrated that Rhomboid is a serine protease: it contains the residues necessary for serine protease catalysis, and is inhibited by known serine protease inhibitors. He also identified a family of seven Rhomboid-like proteins in *Drosophila* and additional Rhomboid-like proteins in species as diverse as the Gram-negative bacteria *Pseudomonas aeruginosa* and *Providencia stuartii.* Finally, he discussed the cleavage site in the Spitz protein: a seven-amino-acid sequence, ASIASGA in the single-letter amino-acid code, in a transmembrane region of the Spitz protein. This motif is also present in the TGFα and Delta signaling proteins, suggesting they may also be substrates for Rhomboid cleavage. The ASIASGA motif seems to have two functions that allow it to be cleaved by Rhomboid: it produces a kink in the transmembrane α helix and it forms a hydrophilic pocket at the top of the helix allowing water, which is necessary for protease activity, to enter the cleavage site.

Michelle Markstein (University of California, Berkeley, USA) used a computational method (Fly Enhancer <http://flyenhancer.org>) to search the genome sequence for clusters of enhancers that are targets of the Dorsal transcription factor. Looking for clusters of enhancer sequences appears to improve the sensitivity of such methods and has allowed the identification of approximately one third of the genes estimated to be directly affected by Dorsal. Besides known targets such as *zen, sog* and *brinker*, she found novel targets, including *Phm*, *Ady* and CG12443; these were confirmed by embryonic *in situ* hybridization and expression of *lacZ* under the control of the putative enhancer. Interestingly, it seems that clusters of different enhancer binding sequences may be more diagnostic for the identification of *cis*-control regions than clusters of a single binding site.

A number of groups described research using microarray analysis. Ulrike Gaul (Rockefeller University, New York, USA) presented an analysis of glial cell transcription. Glial cells labeled with green fluorescent protein under the control of the *repo* promoter were chemically dissociated from embryos and sorted by fluorescence-activated cell sorting (FACS). Gene expression in glial and non-glial cell fractions was assessed using an Affymetrix gene array, and 255 strongly expressed genes were identified. CG11Q10 is expressed only in midline and longitudinal glia; reduction of the transcript level by RNA interference (RNAi) prevents midline glial cells from separating axon tracks in embryonic commissures. Other examples of new genes found in this screen include molecules affecting axon guidance, cell migration and shape, and axon wrapping. The combination of microarray analysis and RNAi provides a new paradigm for rapid screening.

Amir Orian (Fred Hutchinson Cancer Center, Seattle, USA) has investigated the binding sites of the Myc-Max-Mad (MMM) transcription factor complex. Fusions of *Dam* methylase to these proteins were introduced into transgenic flies, then genomic DNA was digested with a methylation-sensitive restriction enzyme and the fragments were analyzed on a microarray. Interestingly, methylation of genes encoding synaptic-vesicle and mitochondrial proteins was observed, suggesting that the MMM complex may exert previously unknown influences on these processes.

Greg Gibson (North Carolina State University, Raleigh, USA) used long-oligonucleotide arrays to study the inheritance of gene expression. Gene expression was measured in seven strains of *D. melanogaster* and all F1 progeny of crosses between those strains. His data show that approximately 10% of genes are differentially expressed between any two of the strains studied, and that 20% of genes are expressed differently in the F1 compared to the parental strains. It is possible to divide these differences into several classes: some are expected, such as additive, dominant and recessive patterns of inheritance of the expression level; in other cases, the level of gene expression in the F1 is significantly greater or less than can be explained by additive expression of both parental strains.

Many researchers are making use of the expanding *Drosophila* gene collection. Mark Stapleton (Lawrence Berkeley National Laboratory, Berkeley, USA) identified RNA-editing substrates by comparing the high-quality cDNA and genomic sequences. He found 27 adenosine deaminase substrates, the majority of which are ion-channel transcripts. Pavel Tomancak (University of California, Berkeley, USA) presented a comparison of *D. melanogaster* and *D. pseudoobscura* embryonic expression patterns for a number of genes. The vast majority of the 176 genes investigated showed identical expression patterns in the two species. But two genes with different expression patterns were identified. The expression of the midline fasciclin transcript is moved from the neuroectoderm in *D. melanogaster* to the mesoderm in *D. pseudoobscura. Ecdysone-inducible gene E2* (described in a poster presented by Amy Beaton, University of California, Berkeley, USA) is expressed in the anterior of early embryos and in the developing foregut by stage 11 in *D. melanogaster*, but in *D. pseudoobscura* it is expressed in the posterior of early embryos and in the developing hindgut by stage 11.

Laura Lee (Massachusetts Institute of Technology, Cambridge, USA) identified seven novel substrates for Pan gu, a protein kinase required early in the cell cycle during embryogenesis. Her biochemical screen made use of coupled transcription-translation of cDNA clones from the *Drosophila* gene collection to produce \[^35^S\]-labeled proteins in a 384-well format. Pools of 24 proteins were then screened in a variety of binding, degradation and enzymatic assays. Examples include screens for Disheveled-binding proteins, microtubule-binding proteins and the Pan gu kinase assay based on band shirts on electrophoretic gels.

One talk highlighted the imprecise art of gene prediction. Marc Hild (University of Heidelberg, Germany) presented a microarray constructed using a less stringent gene-prediction program and a possible 21,396 putative ORFs. Expression data aquired using this array suggests that there are 3,000 more *Drosophila* genes than were predicted in the Release 3 version of the genome. Some of these sequences produce phenotypes in S2 tissue culture cells when inhibited by RNAi. Once the data are made public and analyzed in detail, many of these 'novel' genes will no doubt be found to have exons that overlap those of previous predictions. Other differences may be 'philosophical': for example, should a gene prediction be considered if it has an open reading frame of less than 100 amino acids? It is clear that biological evidence is required to positively identify a gene.

The *D. pseudoobscura* sequence, available from the Drosophila Genome Project <http://hgsc.bcm.tmc.edu/projects/drosophila>, may be the surest way to identify the meaningful sequences of the *D. melanogaster* genome. Richard Gibbs (Baylor College of Medicine, Houston, USA) presented the initial release of the *D. pseudoobscura* sequence. A tBLASTn comparison of the two *Drosophila* genomes identified putative orthologs in *D. pseudoobscura* for 95% of *D. melanogaster* genes. Alignment of the two genomic sequences identified both large features, such as chromosomal inversions, and small ones, such as conserved non-coding regions. A comparative genomic approach using both sequences will improve gene prediction and allow the identification of *cis*-regulatory sequences for the majority of *Drosophila* genes. We can hope that the sequence of *D. pseudoobscura* will be as informative to *Drosophila* research as that of *D. melanogaster*, and many presentations on 'the other *Drosophila*' can be expected at future annual conferences.


abstract: A report on the 13th International Conference on Systems Biology, held in Toronto, Ontario, Canada, 19-23 August 2012.
author: Adam P Rosebrock; Amy A Caudy
date: 2012
institute: 1Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St., Toronto, Ontario, Canada, M5S3E1; 2Department of Molecular Genetics, University of Toronto, 160 College St., Toronto, Ontario, Canada, M5S3E1
title: The future of deciphering personal genomes? The flies (and yeast and worms) still have it

# Introduction

Sydney Brenner said in his 2002 Nobel lecture that we are 'drowning in a sea of data, starving for knowledge'. Drafts of the human genome sequence were barely a year old at the time, and even optimistic projections placed the \$1,000 genome beyond 2040. Just a decade after Brenner's lecture, technological revolutions in sequencing and protein analysis have changed the pace of genome biology. The reference genome has given way to individual patient sequences. Sydney's sea of data is becoming ever deeper.

New tools are necessary to extract biological meaning from these data. The diverse group of scientists gathered in systems biology is at the forefront of devising new ways to net knowledge out of a data sea that widens and deepens every day. The flood of data produced by modern 'omics technologies demands new methods that connect genotype to phenotypic consequence. With the \$1,000 genome now predicted by the end of 2012, we are faced with the challenge of interpreting raw sequence into health-relevant information.

Here, we cover some of the highlights of an inherently diverse meeting, focusing on the role of systems biology as relevant to genomic medicine (abstracts are freely available online: <http://www.icsb2012toronto.com/schedule.php>).

# Biological systems are dynamic; systems biologists are catching up

Twenty-three pairs of chromosomes within a single cell give rise to hundreds of tissues with diverse functions, morphologies and biochemical activities. The humble budding yeast is born, has a fruitful replicative life on a variety of foods, and eventually succumbs to senescence. Life consists of a series of changing processes that cannot be explained by a snapshot view; models that accurately represent biological processes must be built from data that capture these dynamics.

Advances in mass spectrometry have enabled examination of rare and modified proteins; modified proteins are turning out to be not rare at all. Kirti Sharma in Matthias Mann's lab (Max Planck Institute of Biochemistry, Martinsreid, Germany) claims that 75% of the proteins in cancer cells are present as phosphorylated forms, and that this is likely to be a vast underestimate, with current methods capturing only one-tenth of the phosphopeptides in the cell.

A major focus of systems biology is understanding the interaction between biomolecules. Genetic approaches, including systematic identification of synthetic lethal gene deletions (championed by Charley Boone at the University of Toronto, ON, Canada), are being supplemented by measurement of physical interactions. These efforts are focused on reducing the massive disconnect between genome sequence and physical function.

Many key proteins in the cell are interaction hubs and form complexes with many different partners. Interactions are frequently mutually exclusive or separated in space, time, or by cellular state. Anne-Claude Gingras (Samuel Lunenfeld Research Institute, Toronto, ON, Canada) and Andrew Emili (University of Toronto, Toronto, ON, Canada) are working to move from a simple static representation of protein complexes to an understanding of these dynamic interactions. Gingras is interrogating protein interactions by affinity purification of different components of a single complex. Emili has taken a global approach by analyzing the entire proteome following multiple orthogonal separation methods and identifying complexes by common separation patterns. Michael Washburn (Stowers Institute, Kansas City, MO, USA) has been using mass spectrometry to address questions once reserved for structural biology. Washburn has used affinity purification and mass spectrometry to measure protein interactions in partially disassociated complexes. Interactions that survive are likely to occur between directly contacting molecules.

Weak interactions are common and essential in biological systems. Mike Tyers (IRIC, Université de Montréal, QC, Canada) reminded listeners that without weak interactions, we would be stones. Tyers' work demonstrates that multiple weak sites provide a mechanism for buffering, redundancy and cooperative kinetic behavior. Disruption of individual weak-functioning residues can frequently be tolerated, confounding current mutation-counting sequencing efforts.

# Model organisms drive understanding of genotype to phenotype

Studies in model organisms can answer questions that have proved to be difficult to study in man. Missing heritability is one of the biggest problems in human genome-wide association studies. Joshua Bloom (Princeton University, Princeton, NJ, USA) mapped dozens of quantitative trait loci in yeast. They were able to account for more than 90% of the heritability of these traits, suggesting that the failure to map quantitative traits in human populations results from the diversity of alleles and the influence of environment rather than a problem with the approach of mapping.

Enrolling more patients is a common way to increase the power of genome-wide association studies. Two talks at the meeting presented a systems-biology alternative. Chad Myers (University of Minnesota, Minneapolis, MN, USA) and Manuel Mattheisen (Brigham and Williams Hospital, Boston, MA, USA) demonstrated approaches that group genes prior to testing for significant variants. Both researchers used functional groupings of genes based largely on data from model systems. Model organisms continue to be a critical scaffold for understanding biology.

Ben Lehner (ICREA, Barcelona, Spain) highlighted how chance controls life. *Caenorhabditis elegans* harboring null alleles of Tbx-9, a transcription factor regulating development, have a fifty-fifty chance of death or completely normal life. Lehner's group has identified fluctuations in gene expression within this isogenic population that modulate this life and death coin-toss. In our world of Brownian motion and stochastic gene expression, deterministic sequence to phenotype analysis will remain the stuff of science fiction.

# 'Personal genomes' are of more value to the community than the patient

Geneticist Sewall Wright once said that natural variation is the worst kind of experiment to study. Wright, the father of genetic drift and the fitness landscape, had a prescient understanding of our predicament. Mike Snyder (Stanford University, Stanford, CA, USA) gave an update on the Snyderome, a longitudinal study of his transcriptome, proteome, metabolome and other physiological parameters in what may well be the most expensive case study in history. Next-generation sequencing has become Snyder's 'Ouiji board'. His risk allele for diabetes may have manifested itself in an episode of elevated blood glucose, which Snyder (possibly) corrected by a wholesale elimination of sweets. Fortunately for Snyder, none of his other disease risk alleles have been borne out or have required such radical life changes. Personal genome sequencing is here, but the interpretation is difficult, and as Snyder advises, not for worriers.

According to Chris Saunder (Memorial Sloan Kettering Cancer Center, New York, NY, USA), genome medicine is sequence-rich and phenotype-poor. Saunder remarked that the erudite molecular profiling of the Cancer Genome Atlas and related efforts frequently lacks accompanying data on phenotype, cautioning that careful and deep phenotyping is as important as sequencing itself. George Church (Harvard University, Cambridge, MA, USA) highlighted efforts coordinated through the Personal Genomics Project to provide such rich phenotypic information. This dataset of genomes coupled with phenotype will be useful in understanding the scope of variation, but Church cautioned that the diagnostic value of genomes is limited. Like a stethoscope, sequencing is just one source of health information.

Identification of causative alleles is particularly critical in cancer; cells that appear in relapse are often from a different or earlier lineage than cells in the original tumor. John Dick (Princess Margaret Hospital, Toronto, ON, Canada) described work to identify causative mutations in genomes using xenografts. As in relapsing patients, many of the tumors that grew in mice are genetically different from the primary tumor. Dick's work questions the value of a 'tumor genome' derived from bulk cells, emphasizing the need to identify rare cells that can form new tumors. Personalized medicine may well become cell-, not patient-, specific.

Dave Hill (CCSB, Boston, MA, USA) reminded attendees that many proteins are multifunctional. Patient-specific mutations will affect some or many of a diverse array of protein interaction partners. To move from sequence to diagnosis and prognosis, one needs to understand these complex interactions. Hill cautioned that early successes with Mendelian traits will not translate into rapid understanding of complex genetic interactions.

The most remarkable personal genome, though, was that of only half a human. Thijn Brummelkamp (Netherlands Cancer Institute, Amsterdam, The Netherlands) presented a series of functional genetic screens using a haploid somatic human cell line. Brummelkamp used traditional insertional mutagenesis to identify essential human genes, bringing classic tools of model organism research to human genetic analysis.

# In addition to new tools, systems biology requires new ways of training tomorrow's scientists

Radical advances in technology have driven systems biology in the past decade. George Church highlighted the phenomenal growth of sequencing technology, noting that 44 commercial sequencing technologies are now available or soon will be. New sequencing technologies will be cheaper, faster and more accurate, and will permit new types of analyses.

The diverse skills required by systems biology require development of a new kind of scientist. David Botstein (Princeton University, Princeton, NJ, USA) accepted an award for excellence in quantitative education for his creation of a new teaching paradigm. Botstein's view is that students languish in prerequisite general courses while thirsting for access to current research and educational specialization. Contrary to the status quo, Botstein envisions research and teaching as organically connected tasks. This integrated training approach is necessary to bridge the significant cultural gap between biologists, mathematicians and computer scientists. Students should be taught current scientific practice, not scientific history. If the acceleration in systems biology continues, merely keeping up with the present will be a monumental task.