RESEARCH ARTICLE

Attenuation of inflammation and cellular stress-related

pathways maintains insulin sensitivity in obese type I

interleukin-1 receptor knockout mice on a high-fat diet

Baukje de Roos1�, Vanessa Rungapamestry2�, Karen Ross1, Garry Rucklidge1, Martin Reid1,Gary Duncan1, Graham Horgan3, Sinead Toomey2, John Browne2, Christine E. Loscher2,Kingston H. G. Mills4 and Helen M. Roche2

1 Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK2 Nutrigenomics Research Group, UCD Conway Institute, School of Public Health & Population Science,

University College Dublin, Dublin, Ireland3 Biomathematics and Statistics Scotland, Rowett Research Institute, Aberdeen, UK4 Immune Regulation Research Group, School of Biochemistry and Immunology, Trinity College Dublin, Dublin,

Ireland

Received: September 25, 2008

Revised: February 27, 2009

Accepted: March 6, 2009

The development of insulin resistance in the obese is associated with chronic, low-grade

inflammation. We aimed to identify novel links between obesity, insulin resistance and the

inflammatory response by comparing C57BL/6 with type I interleukin-1 receptor knockout

(IL-1RI�/�) mice, which are protected against diet-induced insulin resistance. Mice were fed a

high-fat diet for 16 wk. Insulin sensitivity was measured and proteomic analysis was performed

on adipose, hepatic and skeletal muscle tissues. Despite an equal weight gain, IL-1RI�/� mice

had lower plasma glucose, insulin and triacylglycerol concentrations, compared with controls,

following dietary treatment. The higher insulin sensitivity in IL-1RI�/�mice was associated with

down-regulation of antioxidant proteins and proteasomes in adipose tissue and hepatic soluble

epoxide hydrolase, consistent with a compromised inflammatory response as well as increased

glycolysis and decreased fatty acid b-oxidation in their muscle. Their lower hepatic triacylglycerol

concentrations may reflect decreased flux of free fatty acids to the liver, decreased hepatic fatty

acid-binding protein expression and decreased lipogenesis. Correlation analysis revealed down-

regulation of classical biomarkers of ER stress in their adipose tissue, suggesting that disruption

of the IL-1RI-mediated inflammatory response may attenuate cellular stress, which was asso-

ciated with significant protection from diet-induced insulin resistance, independent of obesity.

Keywords:

Cellular stress / Inflammation / Insulin resistance / Inter-organ proteomics / Type I

interleukin-1 receptor

1 Introduction

Obesity is associated with chronic, low-grade inflammation

that has been implicated in the development of insulin

resistance and type II diabetes mellitus (T2DM). The

inflammatory response may be triggered, and reside

Abbreviations: A.U., arbitrary units; EET, epoxyeicosatrieonic

acid; FABP, fatty acid-binding protein; HOMA, homeostasis

model assessment for insulin resistance; IL-1RI�/�, type I

interleukin-1 receptor knockout; MCP-1, chemokine monocyte

chemotactic protein-1; NFjB p65, nuclear factor-kappa B p65;

PDK, pyruvate dehydrogenase kinase; PLS-DA, partial least

squares-discriminant analysis; QUICKI, quantitative insulin

sensitivity check index; sEH, soluble epoxide hydrolase;

SMP30, senescence marker protein 30; T2DM, type II diabetes

mellitus; TAG, triacylglycerol; TNF, tumour necrosis factor; WAT,

white adipose tissue

�These authors contributed equally to this work.

Correspondence: Dr. Baukje de Roos, University of Aberdeen,

Rowett Institute of Nutrition & Health, Greenburn Road, Bucks-

burn, Aberdeen AB21 9SB, UK

E-mail: b.deroos@abdn.ac.uk

Fax: 144-1224-716629

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

3244 Proteomics 2009, 9, 3244–3256DOI 10.1002/pmic.200800761

predominantly, in adipose tissue, although other metaboli-

cally critical sites may also be involved as the disease

progresses [1]. However, much remains to be discovered

about the role of adipocytes in inflammation [2]. The

macrophage-inflammatory response in white adipose tissue

(WAT) may be a general phenomenon associated with

morbid fat-mass expansion, as demonstrated by significant

regulation of macrophage and inflammatory genes in the

WAT of obese mice [3]. A key question is what proportion of

these inflammatory genes are causally linked to particular

metabolic outcomes [2].

Growing evidence indicates a correlation, and possibly a

causative relationship, between inflammation and insulin

resistance in T2DM. Mice lacking the tumour necrosis

factor-a (TNF-a) ligand, the p55 TNF receptor, or monocyte

chemotactic protein-1 (MCP-1), are partially protected from

obesity-induced insulin resistance [4, 5], although the

contributory role of MCP-1 in dysregulation of metabolic

function has been disputed [6]. Furthermore, macrophage

and inflammatory genes were up-regulated before an

increase in circulating insulin levels, indicating that

macrophage activities occur after the increase in adiposity,

but before insulin resistance [7]. It is currently unknown

how the inflammatory response initiated in WAT ultimately

causes systemic insulin resistance, but it may involve lipo-

lysis. TNF-a directly decreases insulin sensitivity and

increases lipolysis in murine and human adipocytes [8, 9].

Moreover, free fatty acids activate immune cells that may

infiltrate hypertrophic adipose tissue via an up-regulation

of Toll-like receptors 2 and 4, and c-Jun-N-terminal

kinase-dependant stress and pro-inflammatory signalling

pathways [10].

In this study, we explored novel links between obesity,

plasma lipids, insulin resistance, hepatic weight and fat

accumulation, focusing on the adipose tissue, liver and

muscle proteome in type I interleukin-1 receptor I (IL-1RI)

knockout mice. IL-1 is a potent mediator of innate inflam-

mation [11], which exerts its biological functions viathe IL-1RI. The expression of the IL-1R and IL-1b is

elevated in visceral adipose tissue of obese subjects [12].

Therefore, IL-1RI represents a potential target that

mediates the pro-inflammatory, insulin resistant state

associated with diet-induced obesity. Thus the IL-1RI�/�

mouse model presents the ideal context to explore the

interaction between the innate inflammatory process and

insulin sensitivity within three organs that play a key role in

insulin resistance.

2 Materials and methods

2.1 Animals and diets

IL-1RI�/� (C57BL/6 background sourced from Jackson

Laboratories), and C57BL/6 control (purchased from

Harlan) 6-wk old male mice were held at the BioResources

Unit of Trinity College Dublin (Ireland) under specific

pathogen-free conditions. Ethical approval was obtained

from the Trinity College Dublin Ethics Committee and mice

were maintained according to the regulations of the

European Union and the Irish Department of Health that

govern the use of animals in research. Seven IL-1RI�/� and

eight C57BL/6 control mice were each fed a high-fat diet

(60% Kcal from fat; Research Diets, New Brunswick, NJ,

USA) for 16 wk. Body weight was recorded at the start and

end of dietary intervention. Following dietary treatment, a

fasting blood sample was taken and animals were culled by

CO2 inhalation. Liver, skeletal muscle from the hind leg and

epididymal WAT were harvested, frozen in liquid nitrogen,

and stored at �801C.

2.2 Plasma analysis

Plasma glucose, insulin, NEFA and triacylglycerol (TAG)

concentrations were measured using commercially available

kits, as for serum [13]. Fasting plasma insulin and glucose

concentrations were used to calculate the biomarker insulin

resistance from the homoeostasis model assessment for

insulin resistance (HOMA) ((glucose0� insulin0)/22.5) [14].

The biomarker insulin sensitivity was calculated from

the revised quantitative insulin sensitivity check index

(QUICKI) (1/(log insulin0 1 log glucose0 1 log NEFA0))

[15]. Plasma adiponectin and IL-6 concentrations were

determined using Mouse Quantikine Elisa kits (R & D

Systems, MN, USA).

2.3 Tissue weight, and lipid and adipophilin analysis

Total hepatic lipids were extracted using the method of

Folch as described by us previously [16]. For TAG analysis,

the lipid fraction was re-suspended in chloroform:methanol

(2:1) and 10 mg of lipid was dried under a stream of

nitrogen in a separate tube and re-suspended in 5 mL

ethanol. TAG levels were analysed in quadruplicate

by a commercial kit according to the manufacturer’s

instructions (Thermoelectron triglyceride kit, Lab medics,

Manchester, UK), using a glycerol standard (Sigma, UK).

Hepatic adipophilin in cytosolic protein homogenates was

measured by an in-house ELISA using a specific polyclonal

antibody (Fitzgerald Industries International, Concord,

MA, USA) [16]. The weight of the adipose tissue was also

recorded.

2.4 Separation and identification of proteins

Adipose tissue protein homogenates were prepared from

epididymal fat depots (300–500 mg per animal) by homo-

genisation in 1 mL of lysis buffer (7 M urea, 2 M thiourea,

4% CHAPS, 2% Ampholite, pH 3–10) and centrifugation at

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200� g for 5 min. The supernatant was carefully transferred

to a Beckman microfuge tube without disrupting the over-

lying layer of lipid and then centrifuged at 226 000� g and

201C for 30 min (Beckman TL-100). Protein was determined

from the resulting supernatant using the RC/DC protein

assay (Bio-Rad, Hemel Hempstead, UK) and 100mg of the

protein was loaded per 2-D gel. Cytosolic hepatic protein

homogenates were prepared from each animal [17, 18] and

300 mg of hepatic protein was loaded per 2-D gel. Skeletal

muscle protein homogenates were prepared from the hind-

leg muscles (approximately 200 mg per animal), as described

for hepatic tissue [17, 18], after grinding the frozen tissue to

a powder in liquid nitrogen and 300 mg of protein was loaded

per 2-D gel.

Proteins were separated by 2-D gel electrophoresis using

one 2-D gel per tissue sample per animal, as described

previously [17, 18] with the following modifications. Bio-Rad

IPG strips (pH 5–8 for liver and adipose tissue and pH 3–10

for muscle) were used for the separation of proteins in the

first dimension. Gels were stained using the flamingo

fluorescent stain (Bio-Rad) and analysed using PDQuest 2-DAnalysis Software, version 7.1.1 (Bio-Rad, Hercules, CA,

USA). Spot densities were normalised using the local

regression model within the PDQuest software. Spots with

densities that significantly contributed to the treatment

effects were excised from the SDS-PAGE gels using the

EXQuestTM Spot Cutter (Bio-Rad). These proteins were

trypsinised using a protocol of the MassPrep Station

(Micromass) and analysed by MALDI-TOF and by electro-

spray LC-MS/MS as described previously [17–19]. The

MS/MS spectra of peptide fragments were forwarded the

MASCOT search engine version 2.1 (http://www.ma-

trixscience.com) for database searching, using the following

search criteria for Mus musculus: allowance of 0 or 1 missed

cleavages, trypsin as digestion enzyme, carbamidomethyl

modification of cystein, methione oxidation as partial

modification and charged state as MH1, as described

previously [19].

2.5 Western blot analysis of adipose tissue nuclear

factor-kappa B p65 (NF-jB P65) and hepatic

soluble epoxide hydrolase (sEH)

As indicators of an altered tissue-specific inflammatory

response to support our proteomics data, adipose tissue

NF-kB P65 (in four IL-1RI�/� samples and three C57BL/6

control samples) and hepatic sEH protein levels (in five

IL-1RI�/� samples and six C57BL/6 control samples) were

analysed by Western blot as described previously [17], with

the following modifications. Cytosolic protein (8 mg) was

loaded for 1-D gel electrophoresis. Following transfer of

protein onto nitrocellulose membranes, the latter were

blocked using 5% BSA in TBS with 0.05% Tween and

incubated overnight at 41C with anti-NF-kB P65 (Santa

Cruz, CA, USA; 1:500) or anti-sEH (Santa Cruz; 1:1000)

antibodies diluted in 3% BSA. Membranes were also probed

with anti-actin antibody (1:1000) to ascertain equal loading

of protein. Membranes were washed with TBS with 0.05%

Tween and incubated with peroxidase conjugate anti-rabbit

antibody (Sigma; 1:5000) for 2 h at room temperature.

After further washing, immunoreactive protein complexes

were visualised using a chemiluminescent reagent

(Supersignals West Pico Chemiluminescent Substrate,

Pierce, IL, USA). Membranes were exposed to film for

1–10 min and processed using an Agfa X-ray processor.

Protein bands were quantified by densitometry, using the

TINA software version 2.09c (Raytest, Straubenhardt,

Germany), and protein levels were expressed as arbitrary

units (A.U.).

2.6 Gene expression analysis of TNF-a and MCP-1 in

epididymal fat

Total RNA was extracted from epididymal fat using Trizol

Reagent (Invitrogen, Paisley, UK). The concentration was

adjusted to 50 ng/mL and the RNA samples were stored at

�801C. Single stranded cDNA was prepared using the High

Capacity cDNA Archive kit (Applied Biosystems, Warring-

ton, UK). Labelled probes and primers for TNF-a and

MCP-1 were obtained from Applied Biosystems. The relative

concentration of both genes was determined using the

standard curve approach with an external reference gene

(GAPDH) [13].

2.7 Statistical analysis

Data were presented as means7SD (n 5 7–8). Unpaired

t-tests were performed on plasma parameters and protein

spot data, and p-values o0.05 were considered statistically

significant. Data which were not normally distributed were

log-transformed before analysis. Principal component

analysis was performed after unit variance scaling of the

data, and partial least squares-discriminant analysis

(PLS-DA) was performed on centred data in SIMCA-P1

version 10.5.0.0 (Umetrics, UK). Protein spots with a

substantial positive or negative contribution to the outcome

were identified from the loading plots [16–18]. Analysis of

correlations between plasma measurements, and adipose

tissue, hepatic and muscle proteins was performed with

Pearson correlation coefficients. The analysis of multiple

hypotheses testing was done by determining the false

discovery rate, using the method of Storey & Tibshirani [20].

q-values estimate the probability that a significant correla-

tion is false positive. For example, a q-value of 0.05

indicates that we should expect that 5 out of 100 associations

that were tested significant are in fact false positive.

A network of significant pair-wise interactions was

constructed using the spring embedded algorithm in

Cytoscape 2.4.1.

3246 B. de Roos et al. Proteomics 2009, 9, 3244–3256

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

3 Results

3.1 Food intake, body weight and the metabolic

profile

Food intake did not differ between the intervention groups

(data not shown). Both groups gained similar body weight

after 16 wk on the high-fat diet (19.777.2 g in IL-1RI�/�mice

and 23.274.4 g in control mice, p40.05). IL-1RI�/� mice,

compared with control animals, had significantly

lower plasma TAG (0.6270.15 versus 0.8970.16 mmol/L,

po0.01), NEFA (574.58734.42 versus 775.63788.69mmol/L,

po0.001), and glucose (5.8270.46 versus 8.0570.73 mmol/L,

po0.001) concentrations. After the high-fat diet, HOMA, an

index of insulin resistance, was improved in obese IL-1RI�/�

mice compared with their obese control counterparts

(9.0973.77 versus 18.1376.54, po0.01). Revised QUICKI, an

index of insulin sensitivity, was improved in the IL-1RI�/�

group compared with control mice (0.2070.01 versus0.1870.01, po0.0001). Plasma IL-6 concentrations were

significantly lower (737.67780.81 versus 1454.9782.87 pg/

mL, po0.05), whereas adiponectin concentrations were

higher (9609.57833.08 versus 7665.67609.8 ng/mL, po0.05)

in IL-1RI�/� mice, compared with controls.

3.2 Tissue weight, and hepatic fat and adipophilin

protein

Mean weight of liver was significantly lower (po0.01) whereas

that of adipose tissue was significantly higher (po0.05) in

IL-1RI�/�, compared with C57lBL/6 mice (Table 1). Hepatic

fat accumulation was less and hepatic TAG levels were

significantly lower (po0.01) in IL-1RI�/�mice compared with

controls. Hepatic adipophilin levels, a marker of hepatic fat

deposition, were significantly lower (po0.05) in IL-1RI�/�

compared with control mice (Table 1).

3.3 Changes in the proteome of insulin-sensitive

organs

3.3.1 Epididymal adipose tissue

A total of 92 proteins, including post-translationally modi-

fied isoforms of proteasomes (Mr range 24.9–47.1; pH range

5.5–6.8), ubiquitin-protein ligase E1 (Mr range 114.4–115.2;

pH range 5.6–5.7), cathepsin D (Mr range 28.4–42.2; pH

range 6.1–6.5), vimentin (Mr range 47.2–55.1; pH range

5.3–5.6), L-plastin (Mr range 63.2–63.5; pH range 5.5–5.6),

T-complex protein 1 (Mr range 54.0–61.3; pH range

6.1–6.7), gelsolin (Mr range 96.1–98.8; pH range 6.2–6.5),

and transaldolase (Mr range 35.4–36.5; pH range 6.3–6.9),

were significantly altered in IL-1RI�/� mice (Table 2). Most

of these proteins were down-regulated and implicated in

protein degradation, apoptosis, cytoskeletal re-organisation

and oxidative stress. Five proteins were responsible for the

separation between IL-1RI�/� and control mice using PLS-

DA without showing significant group differences in spot

intensity on the 2-D gels (Table 2). Adipose tissue protein

levels of NF-kB P65, as determined by Western blot analysis

(Fig. 1A), were significantly lower in IL-1RI�/� compared

with control mice (0.85570.037 A.U. versus 1.01270.041

A.U., po0.05).

3.3.2 Hepatic tissue

Proteomics analysis revealed that 32 hepatic proteins were

significantly up- or down-regulated in IL-1RI�/� compared

with control mice. The majority of proteins were involved in

pathways related to inflammation, glucose metabolism,

oxidative stress and amino acid metabolism (Table 3). A

minimum of two positional variants of fructose-1,6-bispho-

sphatase (Mr range 37.0–37.2; pH range 6.4–6.8), aldehyde

dehydrogenase (Mr range 49.0–53.6; pH range 6.0–6.3),

aminoacylase-1 (Mr 41.5; pH range 6.5–6.6), arginase-1 (Mr

range 38.8–39.6; pH range 6.3–6.5), methionine adenosyl-

transferase (Mr range 48.1–48.5; pH range 6.2–6.4) and

CCG1-interacting factor B (Mr range 24.9–25.0; pH

range 6.4–6.7) were identified among these proteins.

Twenty-one proteins were responsible for separation

between IL-1RI�/� and control mice using PLS-DA without

showing significant group differences in spot intensity on

the 2-D gels (Table 3). One of these proteins was sEH, and

Western blot analysis (Fig. 1B) confirmed that hepatic sEH

protein levels were significantly lower in IL-1RI�/�

compared with control mice (1769572248.7 A.U. versus2225373111.9 A.U., po0.05).

3.3.3 Skeletal muscle

Changes in the proteome of skeletal muscle of IL-1RI�/�,

compared with control mice, consisted of 13 differentially

Table 1. Hepatic and adipose tissue weights, and hepatic levels offat, TAG and adipophilin protein in IL-1RI�/� and C57BL/6control mice fed a high-fat diet for 16 wk

IL-1RI�/�

micea)C57Bl/6 controlmicea)

Adipose tissueweight (g)

0.7370.30b) 0.4370.15

Liver weight (g) 0.5870.30c) 1.0770.32Hepatic fat (% of

tissue weight)1173 1676

Hepatic TAG (nmol/mg lipid)

0.12570.013c) 0.14770.013

Adipophilin (arbitraryunits)

0.4770.65b) 3.4072.54

a) Values are means7SD (n 5 7–8).b) po0.05.c) po0.01.

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Table 2. Changes in the proteome of epididymal adipose tissue, stratified by function, of IL-1RI�/� mice compared with C57BL/6 controlmice following a high-fat diet for 16 wk

SSP Accession Identity protein % of controla) p-value q-value

Oxidative stress and inflammation

4106 BC019410.1 Lysophospholipase 1 0.000 0.0183206 P14901 Heme oxygenase (decyclizing) 6 0.034 0.1291402 AK003217.1 Thioredoxin domain-containing protein 4 14 0.017 0.0923309 Q62086 Paraoxonase 2 20 0.002 0.0426 P14069 Calcyclin 33 0.049 0.1384602 Q8C2F4 Glucose-regulated protein 58 kD 50 0.012 0.0803005 P29391 Ferritin light chain 1 53 0.006 0.0545101 AK005031.1 Peroxiredoxin 4 54 0.019 0.1011905 Q5NCS5 Heat shock protein 4 54 0.041 0.1335102 P11352 Glutathione peroxidase 63 0.012 0.0793701 P38647 dnaK-type molecular chaperone precursor 65 0.031 0.1305704 P24527 Leukotriene A4 hydrolase 66 0.011 0.0743203 P67770 Prohibitin 68 0.010 0.0744101 AK008734.1 Dual specificity protein phosphatase 73 0.022 0.1084803 Q9CQN1 Trap 1 protein 81 0.035 0.126

Protein degradation

1503 Q8BUP7 Proteasome (prosome, macropain) 26S subunit, ATPase 3 15 0.039 0.1292901 P52482 Ubiquitin-protein ligase E1 31 0.015 0.0892902 P52482 Ubiquitin-protein ligase E1 36 0.010 0.0733201 Q06323 Proteasome activator complex subunit 1 54 0.001 0.0373102 Q8BK27 Proteasome subunit beta type-4 57 0.010 0.0724501 Q8VDM4 Proteasome 26S subunit, ATPase 2 58 0.001 0.0385404 Q8VDM4 26S proteasome non-ATPase regulatory subunit 2 60 0.059 0.1523303 AK009955.1 COP9 signalosome complex subunit 4 61 0.003 0.0423207 AK008514.1 Similar to proteasome activator complex subunit 1 69 0.014 0.0854408 AK005083.1 Proteasome 26S subunit, ATPase 2b) 83 0.176 0.236

Cytoskeletal integrity

8 AK007994.1 Coactosin 5 0.003 0.0431702 Q61233 L-plastin 8 0.004 0.0411503 P20152 Vimentin 15 0.039 0.1291701 P14733 Lamin B1 16 0.000 0.0121206 P62046 Calponin homology domain 24 0.001 0.0361609 P31001 Desmin 28 0.017 0.0922309 Q61276 Skeletal muscle alpha-actin mRNA 30 0.036 0.1261704 Q61233 L-plastin 39 0.016 0.090504 P20152 Vimentin 39 0.028 0.1251504 Q99KJ8 Dynactin 50 kD subunit 39 0.038 0.1283505 BC005557.1 ARP3 actin related protein 3 48 0.003 0.0435606 O89053 Coro1a protein 48 0.006 0.0551601 P20152 Vimentin 50 0.011 0.0742304 Q5RKN9 Capping protein muscle Z-line, alpha 1 51 0.005 0.0513803 A32621 Gelsolin 57 0.002 0.0404804 A32621 Gelsolin 61 0.006 0.0545502 Q8BQW4 GTPase-activating protein rhoGAP homolog 62 0.006 0.0533613 P80316 T-complex protein 1 64 0.002 0.0396301 Q91YR1 Actin monomer-binding protein twinfilin-1 64 0.032 0.1303304 P47754 F-actin cappin protein alpha-2 subunit 68 0.028 0.1234604 P80316 T-complex protein 1 68 0.035 0.1275604 P80314 T-complex protein 1 70 0.031 0.1312103 O88456 Calpain 72 0.003 0.0413204 Q923G3 F-actin capping protein beta chain 72 0.028 0.1246702 O88342 Actin-interacting protein 1b) 72 0.153 0.220

Apoptosis

2102 P10605 Cathepsin B 2 0.034 0.1283210 P18242 Cathepsin D 2 0.035 0.127

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Table 2. Continued

SSP Accession Identity protein % of controla) p-value q-value

1208 Q9ES94 Cathepsin Z 32 0.001 0.0355202 P18242 Cathepsin D 31 0.001 0.0793405 P18242 Cathepsin D 49 0.000 0.032

Glucose metabolism

3904 U92793.1 Alpha glucosidase II alpha subunit 22 0.003 0.0426604 NM_025700.1 Phosphoglucomutase 46 0.036 0.1275609 Q00612 Glucose-6-phosphate 1-dehydrogenaseb) 107 0.723 0.388

Enzymes

3402 Q04447 Creatine kinase 32 0.007 0.0534103 P40936 Indolethylamine N-methyltransferase 43 0.001 0.0324306 P45377 Aldose reductase-related protein 2 44 0.005 0.0534303 P62207 Serine-threonine protein phosphatase PP1 beta 58 0.003 0.0443308 U67611.1 Transaldolase 60 0.009 0.0734403 P04181 Ornithine-oxo-acid transaminase precursor 66 0.042 0.1325306 U67611.1 Transaldolase 66 0.045 0.1367204 Q9R0P3 Esterase 10 67 0.039 0.1305603 Q9DBF1 Aldehyde dehydrogenase family 7 member A1 73 0.041 0.132

Membrane/vesicular transport

3309 Q9JK48 Endophilin B1b 20 0.002 0.0424505 Q5RKP0 Vesicle amine transport protein 1 29 0.000 0.0113901 Q9EQK5 Major vault protein 39 0.001 0.0354201 AK004795.1 Endoplasmic reticulum protein 29 58 0.005 0.0503404 U58885.1 Endophilin II 63 0.006 0.0544405 Q61598 GDP dissociation inhibitor beta 75 0.021 0.107

Membrane function and signaling

12 Q6P069 Sorcin 12 0.019 0.1021203 Q9Z1Q5 Chloride intracellular channel protein 1 41 0.014 0.0842207 P97429 Annexin A4 50 0.001 0.0372211 NM_008142.2 GTP-binding regulatory protein beta-1 chain 54 0.027 0.1255209 AL590445.1 Phosphatidylinositol transfer protein beta isoform 55 0.003 0.0424502 P17182 PX domain protein 72 0.021 0.108

Energy metabolism

2106 Q9R0Y4 Adenilate kinase 1 protein 10 0.042 0.1322109 Q9D3D9 ATP synthase D chain, mitochondrial 58 0.016 0.0922710 Q8CHX2 ATPase, H1transporting, V1 subunit A 56 0.034 0.1274404 AK078474.1 NADH dehydrogenase Fe-S protein 2 56 0.049 0.1383104 AK004827.1 UMP-CMP kinase 62 0.005 0.052

Protein synthesis

1001 P63242 Translation initiation factor eIF-5A 36 0.001 0.0382310 AK002896.1 Eukaryotic translation initiation factor 3 43 0.001 0.0314805 Q9CZD3 Glycyl-tRNA synthetase 65 0.002 0.0406409 P10126 Tu translation elongation factor 66 0.026 0.1252408 X03039.1 Initiation factor EIF-4AIb) 72 0.273 0.269

Miscellaneous

3605 O54724 Polymerase I-transcript release factorb) 226 0.059 0.1513208 D31951.1 Osteoglycin precursor 2 0.035 0.1262713 Q8C7C7 Serum albumin precursor 5 0.043 0.1334109 Q8C988 Neighbor of Cox4 6 0.036 0.126102 P63028 Translationally-controlled tumor protein 27 0.015 0.0911101 Q8BPI0 Rho GDP dissociation inhibitor alpha 37 0.003 0.0434102 Q60631 Growth factor receptor-bound protein 2 47 0.001 0.0322209 Q9D0B2 Hemoglobin epsilon Y2 chain 73 0.027 0.126

a) Expression of proteins which were significantly up- or down-regulated in IL-1RI�/� mice (po0.05, n 5 7–8), calculated as a % of controls.b) Proteins responsible for separation between IL-1RI�/� and controls using PLS-DA.

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expressed proteins, mostly involved in glucose metabolism

and oxidative stress (Table 4). Six proteins were responsible

for separation between IL-1RI�/� and control mice using

PLS-DA without showing significant group differences in

spot intensity on the 2-D gels (Table 4).

3.4 Gene expression of TNF-a and MCP-1 in

epididymal fat

MCP-1 mRNA levels were significantly lower by 65% in

WAT of IL-1RI�/� mice compared with control mice

(po0.05). Adipocyte TNF-a mRNA levels were also decreased

by 23% in IL-1RI�/�, compared with control mice, albeit not

significantly (p 5 0.054).

3.5 Correlation analysis

Pair-wise correlation analysis of the inter-organ proteome

and plasma measurements of IL-1RI�/� mice, compared

with controls, produced four distinct clusters of potentially

associated proteins with Pearson correlation coefficient

40.854, po0.00005, and a q-value o0.00121 (Fig. 2).

The cluster of adipose tissue proteins (24–34 and 44–70;

cluster A) were down-regulated in IL-1RI�/� mice and were

mainly involved in oxidative stress (glucose-regulated

protein, glutathione peroxidise, dnaK-type molecular

chaperone precursor, Leukotriene A4 hydrolase and prohi-

bitin) and cytoskeletal remodelling (ARP3 actin-related

protein 3, coro1a protein, gelsolin, T-complex protein 1,

calpain, F-actin capping protein a2 and b chain, and

actin-interacting protein). Furthermore typical proteins

involved in ER stress, such as HSP70, T-complex

protein and proteasomes were also down-regulated (Fig. 2,

Table 1).

4 Discussion

Disruption of IL-1RI-mediated signalling improved insulin

sensitivity within an obese phenotype, as indicated by lower

levels of plasma glucose, insulin, TAG, NEFA, and

improved indices of HOMA and QUICKI in IL-1RI�/�

mice, compared with controls. An inter-organ proteomics

approach in key insulin sensitive tissues highlighted

molecular mechanisms relating to oxidative stress, inflam-

mation, glucose and fatty acid metabolism, and apoptosis,

that may underlie protection against obesity-induced insulin

resistance.

Oxidative stress, through increased levels of ROS, is an

early instigator and an important underlying cause of

obesity-associated metabolic syndrome [21]. Oxidative stress

in accumulated fat, via increased NADPH oxidase, causes a

deregulated production of adipocytokines and an increase in

ROS production in WAT of obese mice, whereas treatment

of KKay mice with an NADPH oxidase inhibitor improved

hyperinsulinaemia, hyperglycaemia, hypertriglyceridaemia,

hepatic steatosis and deregulated production of adipocyto-

kines [22]. Indeed, systemic oxidative stress is closely

correlated with fat accumulation in non-diabetic humans

[22]. Infiltrated macrophages in WAT, which are an

important source of inflammatory cytokines [3, 7], may be

involved in augmented NADPH oxidase activity and ROS

production in the obese adipose tissue. Oxidative stress is

inextricably linked to inflammation and both processes may

cause insulin resistance in obese adipose tissue. Oxidative

stress and inflammation are likely to work in vicious circles

and may reflect a shortcoming in the adaptive capacity of

cells to cope with chronic metabolic surplus [23].

Proteomics revealed that both oxidative stress and

inflammatory pathways were differentially regulated

in IL-1RI�/� mice, compared with control animals

(Tables 2–4). Lower levels of 15 antioxidant proteins in the

adipose tissue of IL-1RI�/� mice may suggest that relative

ROS levels in WAT of these mice were lower compared with

control mice, despite being equally obese (Table 2). Altered

levels of mainly WAT proteins of IL-1RI�/� mice may also

indirectly be responsible for a down-regulation of the

inflammatory response in these mice (Tables 2–4). For

instance, our results show a significant decrease in protein

levels of various isoforms/subunits of proteasomes in

adipose tissue, which may reflect inhibition of the ubiquitin-

proteasome system and suppression of activation of NF-kB

by stabilising IkB (Table 2) [24]. Indeed, the expression of

NF-kB P65 was lower in WAT of IL-1RI�/� mice in the

current study (Fig. 1A). However, as an adaptative

mechanism for cellular homoeostasis in response to its

inhibition, the sophisticated machinery of the proteasome

complex may undergo subtle modifications including

altered subunit composition [25], as observed in our study

(Table 2). The variation in molecular weight in pH between

isoforms suggests processing and phosphorylation of

proteins. Such a feedback system may perhaps counteract

56 kDa

C57BL/6 IL−1RI-/-

65 kDa

C57BL/6 IL−1RI-/-

A

B

Figure 1. Adipose tissue NF-kB P65 (A) and hepatic sEH (B)

protein levels (two representative samples per animal strain),

analysed by Western blot analysis.

3250 B. de Roos et al. Proteomics 2009, 9, 3244–3256

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

Table 3. Changes in the proteome of liver, stratified by function, of IL-1RI�/�mice compared with C57BL/6 control mice following a high-fatdiet for 16 wk

SSP Accession Identity protein % of controla) p-value q-value

Oxidative stress and inflammation

202 Q64374 Senescence marker protein 30 156 0.003 0.0578201 BC096645.1 Electron Transferring flavoprotein 189 0.002 0.0434004 P35700 Peroxiredoxin 1b) 147 0.071 0.2397704 Q921I1 Transferrinb) 56 0.058 0.2281717 P38647 75 kDa glucose-regulated protein 136 0.030 0.1893512 1CR6B Epoxide hydrolaseb) 77 0.123 0.2584003 Q64466 Superoxide dismutaseb) 114 0.317 0.3481709 BC094900.1 Hspa8/HSC70/HSC73b) 91 0.627 0.469

Protein degradation

3104 P70195 Proteasome subunit beta type 7 precursor 64 0.041 0.1932303 Q9WVJ2 Proteasome 26S subunit non-ATPaseb) 71 0.389 0.385

Cytoskeletal integrity

3510 P11983 T-complex polypeptide 1 39 0.000 0.007302 M12481.1 Beta actin 56 0.047 0.2051304 CT010212.1 Beta actinb) 95 0.904 0.558

Lipid metabolism

3006 P55050 FABP 62 0.026 0.1791110 CT010212.1 ApoE protein 72 0.031 0.186

Glucose metabolism

7504 P26443 Glutamase dehydrogenaseb) 186 0.109 0.2505201 P97323 Fructose-1,6-bisphosphatase 150 0.003 0.0577212 P13707 Glycerol-3-phosphate dehydrogenase 54 0.011 0.1043406 P17182 Alpha enolase 60 0.007 0.0905408 P17182 Alpha enolase 68 0.042 0.1903203 P97323 Fructose-1,6-bisphosphataseb) 73 0.064 0.2364204 P97323 Fructose-1,6-bisphosphataseb) 82 0.233 0.3077302 AK004692.1 Sorbitol dyhydrogenaseb) 118 0.242 0.3098211 Q91Y97 Fructose-bisphosphate aldolase Bb) 85 0.679 0.483

Enzymes

1714 BC025494.1 Acylpeptide hydrolase 3 0.000 0.0006504 AK012213.1 Aldehyde dehydrogenase 194 0.002 0.0467102 P00920 Carbonic anhydrase II 37 0.047 0.2055410 P47738 Aldehyde dehydrogenase 155 0.030 0.1934601 Y12887.1 Carboxylesterase 138 0.035 0.1814510 Q9DBF1 Aldehyde dehydrogenase/antiquitin 1 135 0.037 0.1822409 AY540997.1 Adenosine kinase (long/short isoform)b) 123 0.065 0.2345503 P47738 Aldehyde dehydrogenaseb) 123 0.237 0.310

Amino acid transport and metabolism

3303 AK002423.1 Aminoacylase-1 6814 0.000 0.0003301 AK002423.1 Aminoacylase-1 2 0.000 0.0004507 AY033912.1 Cysteine sulfonic acid decarboxylase 8 0.000 0.0008208 Q9QXF8 Glycine N-methyltransferase 160 0.023 0.1667207 CT010173.1 Arginase-1 154 0.005 0.0652408 NM_133653.1 Methionine adenosyltransferase 153 0.002 0.0466303 NM_019826.2 Isovaleryl coenzyme A dehydrogenase 140 0.035 0.1785303 P50247 S-Adenosyl-L-homocysteine hydrolaseb) 119 0.253 0.3137403 Q91YI0 Argininosuccinate lyaseb) 114 0.627 0.4682404 NM_133653.1 Methionine adenosyltransferaseb) 112 0.612 0.4643102 AK002281.1 Thioether-S-methyltransferaseb) 108 0.575 0.4456305 CT010173.1 Arginase 1b) 108 0.609 0.4652501 Q11136 Proline dipeptidaseb) 105 0.836 0.533

Proteomics 2009, 9, 3244–3256 3251

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

the reported suppression of GLUT4 by proteasome inhibi-

tion in adipocytes [26] to maintain insulin sensitivity in

obese IL-1RI�/� mice.

Furthermore, many cytoskeletal proteins were down-

regulated in the adipose tissue of these mice and may

suggest cytoskeletal remodelling and apoptosis [27], possibly

due to cell death of macrophages and monocytes [28],

leading to a decreased inflammatory response within the

adipose tissue of the mice. Interestingly, transcriptomic

analyses of WAT in obese human subjects showed a

Table 3. Continued

SSP Accession Identity protein % of controla) p-value q-value

Energy metabolism

2003 BC017684.1 UMP-CMP kinase 48 0.018 0.140

Coagulation

106 P48036 Annexin V 25 0.004 0.066

Unknown function

3004 Q8VCR7 CCG1-interacting factor B 0 0.000 0.0002004 Q8VCR7 CCG1-interacting factor B 286 0.000 0.0013504 Q63836 Selenium binding protein 2 262 0.033 0.1833206 AK088123.1 Hypothetical protein without homologyb) 63 0.057 0.2293205 AK088123.1 Hypothetical protein without homology 66 0.018 0.1443001 AK002535.1 s-Adenosyl-L-methionine dependent methyl transferase 75 0.019 0.144

a) Expression of proteins which were significantly up- or down-regulated in IL-1RI�/� mice (po0.05, n 5 7–8), calculated as a % of controls.b) Proteins responsible for separation between IL-1RI�/� and controls using PLS-DA.

Table 4. Changes in the proteome of skeletal muscle, stratified by function, of IL-1RI�/�mice compared with C57BL/6 control mice followinga high-fat diet for 16 wk

SSP Accession Identity protein % of controla) p-value q-value

Oxidative stress

3605 P10639 Thioredoxin reductaseb) 51 0.332 0.6552702 BC066191.1 Heat shock protein 8 76 0.046 0.5492103 Q99LX0 Similar to DJ-1 proteinb) 86 0.724 0.751

Glycolysis, gluconeogenesis and b-oxidation of fatty acids

2607 Q8R339 E2 component of pyruvate dehydrogenase complex 3 0.003 0.2047508 BC086640.1 Glucose phosphate isomerase 1 23 0.003 0.1752101 Q9R0Y5 Adenylate kinase isoenzyme 1 (myokinase)b) 165 0.068 0.6164406 O55137 Acyl-CoA thioestserase 1b) 42 0.057 0.5711301 BC040100.1 NagDb) 45 0.126 0.6463406 Q8VC63 Pyruvate dehydrogenase kinase, isoenzyme 2b) 63 0.501 0.6558205 AF029843.1 Phosphoglycerate mutase 2 69 0.045 0.571

Energy metabolism

6701 BC094462.1 Aconitase 2, mitochondrial 287 0.003 0.2448504 NM_001126.2 Adenylosuccinate synthase 34 0.029 0.483

Coagulation

205 P48036 Annexin V 18 0.023 0.455

Miscellaneous

4403 P50247 Adenosylhomocysteinase 2 0.002 0.222101 P20801 Troponin C 14 0.004 0.1732413 P60843 Eukaryotic initiation factor 4A-I 25 0.009 0.2432406 Q60854 Proteinase inhibitor Spi3 35 0.005 0.1653303 AK005050.2 Similar to dimeric dihydrodiol dehydrogenase 36 0.010 0.2442602 AJ251461.1 P2X4c receptor subunit 49 0.047 0.535

a) Expression of proteins which were significantly up- or down-regulated in IL-1RI�/� mice (po0.05, n 5 7–8), calculated as a % of controls.b) Proteins responsible for separation between IL-1RI�/� and controls using PLS-DA.

3252 B. de Roos et al. Proteomics 2009, 9, 3244–3256

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

significant up-regulation of genes related to extracellular

matrix constituents, including members of the integrin

family, which highlighted the pathological relevance of extra-

cellular matrix in connecting local inflammatory phenomena to

the alteration of WAT metabolic functions in human obesity

[29]. Taken together, these differential changes in protein levels

indicate that the NF-kB pathway could be attenuated in adipose

tissue of IL-1RI�/�mice to cause a compromised inflammatory

response. Indeed, gene expression of MCP-1 was significantly

lower in adipose tissue of IL-1RI�/� mice, which also had

decreased insulin resistance and hepatic TAG, compared with

control mice. This agrees with the finding that MCP-1 knockout

mice show extensive reduction in insulin resistance, hepatic

steatosis and macrophage accumulation in adipose tissue

following a high-fat diet, and suggests that MCP-1 may link

obesity and insulin resistance through induction of an inflam-

matory response in adipose tissue [30]. Decreased levels of

hepatic sEH protein levels in IL-1RI�/� mice in the present

study may provide another link between inflammation and

insulin resistance. sEH is responsible for the conversion of the

bioactive epoxyeicosatrieonic acids (EET) to their corresponding

‘‘inactive’’ diols [31], and down-regulation of sEH protein may

thus indicate decreased degradation of EET. EET are potent anti-

inflammatory lipid mediators that can inhibit IkB kinase,

preventing IkB degradation and maintaining NF-kB in an

inactive state [32]. In addition, EET may beneficially affect

insulin sensitivity, as sEH activity was elevated in livers of

alloxan- and streptozotoin-diabetic male Fischer-344 rats [33].

Oxidative stress also underlies the pathophysiology of

hepatic steatosis [34], which is commonly associated with

early clinical features of the metabolic syndrome such as

obesity, T2DM and dyslipidaemia. The histological and

metabolic hallmark feature of hepatic steatosis is the accu-

mulation of TAG in the liver [35]. In the current study,

hepatic tissue of IL-1RI�/� mice had significantly lower

TAG levels than control mice, despite being equally obese.

This indicates that selective mechanisms, not necessarily

related to obesity per se, are associated with accumulation of

hepatic TAG. Such mechanisms may include an increased

flux of free fatty acids to the liver, increased de novo lipo-

genesis and a decrease in clearance of TAG through

impairment in b-oxidation or VLDL assembly. Indeed, the

IL-1RI�/� mice had significantly lower plasma levels of

NEFA compared with control mice. The higher adipose

73

72

2

71345

6

7

8

50

59

69

249

70

13

12

2021

22

23

161411

1910

15 17

35

18

36

414338 3937

42

2827

32

30

312944

33

34

75

78

77

79 817674

80

55

6364

47

25

56

68

4846

26

65

5760

49

45

6667

21=E – serum albumin precursor22=E – indolethylamine N-methyltransferase23=E – serine threonine protein phosphatase PP1β24=E - gelsolin25=L – not identified26=E – phosphatidyl inositol transfer protein β27=E – annexin A428=E – esterase 1029=E – F-actin capping protein α230=E – actin-interacting protein31=E – glutathione peroxidase32=E – hemoglobin epsilon Y2 chain33=E – dual specificity protein phosphatase34=E – coro1a protein35=E – cathepsin D36=E – 26S proteasome non-ATPase regulatory subunit 237=E – actin monomer-binding protein twinfillin-138=E – aldose reductase-related protein 239=E – ferritin light chain 140=E – growth factor receptor-bound protein 2

61=E – glycyl-tRNA synthetase62=E - transaldolase63=E – GDP dissociation inhibitor β64=E – leukotriene A4 hydrolase65=E – trap 1 protein66=E – GTPase activaing protein rhoGAP homolog67=E – transaldolase68=E – T-complex protein 169=E – Tu tranlation elongation factor70=E – cathepsin Z71=E – chloride intracellular channel protein 172=E – α-glucosidase II α73=M – similar to DJ-1 protein

41=E – ATP synthase D chain42=E – proteasome subunit beta type-443=E – eukaryotic tranlation initiation factor 344=E – ARP3 actin related protein 345=E – endophilin 246=E – UMP-CMP kinase47=E – T-complex protein 148=E – calpain49=E – dnaK-type molecular chaperone precursor50=E – glucose-related protein 58kD51=E – T-complex protein 152=E – endoplasmic reticulum protein 2953=E – transaldolase54=E – proteasome 26S subunit, ATPas e 255=E – F-actin capping protein β chain56=E – COP9 signalosome complex subunit 457=E – prohibitin58=E – proteasome activator complex subunit 159=E – ornithine-oxo-acid transaminase precursor60=E – PX domain protein

1=E – rho GDP dissociation inhibitor α2=E - proteasome 26S subunit, ATPase 33=E – thioredoxin domain-containing protein 44=M – acylCoA thioesterase 15=E – calponin homology domain6=E – lamin B17=M – thioredoxin reductase8=L - weight9=E – L-plastin

10=S - glucose11=L – CCG1-interacting factor B12=M – troponin C13=S - NEFA14=S – IL-615=E – COP9 signalosome complex subunit 416=E - lysophospholipase17=L – acylpeptide hydrolase18=L - CCG1-interacting factor B19=L – aminoacylase 120=L – cysteine sulfonic acid decarboxylase

61

515352

6258

1

40

54

>200% >150% >100% <100% <75% <50%

Figure 2. Correlation plot illustrating pair-wise correlations (Pearson correlation coefficient 40.854, po0.00005 and q-value o0.00121)

between plasma measurements and adipose, hepatic and skeletal muscle tissue proteins, which were significantly altered in IL-1RI�/�,

compared with control mice, after a high-fat diet for 16 wk. Protein expression in IL-1RI�/� mice is expressed as a percentage of that in

control mice, with red or blue shaded nodes denoting significant up- or down-regulation in IL-1RI�/� mice, compared with controls,

respectively. E 5 epididymal adipose tissue, L 5 liver, M 5 muscle, P 5 plasma.

Proteomics 2009, 9, 3244–3256 3253

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

tissue weight of IL-1RI�/� mice in our study, that agrees

with previous reports of increased WAT mass on disruption

of IL-1 signalling [36, 37], may indicate an increased

buffering capacity for NEFA, resulting in less accumulation

of lipids in the liver of these animals. Moreover, down-

regulation of hepatic fatty acid-binding protein (FABP) in

IL-1RI�/�, compared with control mice, supports a

decreased lipid accumulation into hepatocytes because liver

FABP�/� mice are protected from diet-induced obesity and

hepatic steatosis, compared with C57BL/6J mice [38].

IL-1RI�/� mice also showed lower levels of hepatic glycerol-

3-phosphate dehydrogenase, which may indicate a lower

need for glycerol moieties from carbohydrates for hepatic denovo lipogenesis in these animals. Interestingly, hepatic

senescence marker protein 30 (SMP30) levels were signifi-

cantly increased in IL-1RI�/� compared with control mice.

SMP30, a calcium-binding protein that decreases with

aging, has been suggested to protect cells from apoptosis.

Lowered SMP30 in mice causes accumulation of hepatic

TAG and cholesterol, suggesting that SMP30 may have a

profound role in preventing hepatic lipid accumulation

during aging [39]. The association between hepatic TAG and

insulin resistance is well established, with a potential

causative role for accumulation of hepatic TAG in insulin

resistance [40]. This is consistent with the finding of lower

hepatic lipid accumulation in IL-1RI�/� mice, which were

protected against the development of insulin resistance,

compared with controls in our study (Table 1).

Changes in expression of proteins involved in glucose

metabolism occurred in the three organs studied, which may

explain the improved insulin sensitivity and glycaemic

control in IL-1RI�/� mice. For example, decreased levels of

a-glucosidase in adipose tissue and the overall down-regula-

tion of hepatic fructose-1,6-bisphosphatase in IL-1RI�/� mice

suggests delayed carbohydrate digestion and reduced gluco-

neogenesis, which has been associated with improved

glycaemic control [41]. Differential expression of the three

isoforms of hepatic fructose-1,6-bisphosphatase, with similar

molecular masses but an estimated pH range coverage of

6.1–6.5, may reflect cAMP-mediated regulation of this

enzyme by phosphorylation in favour of improved insulin

sensitivity in IL-1RI�/� mice. Furthermore, both adipose

tissue and hepatic a-enolases were decreased in these mice,

whereas up-regulation of this enzyme in the liver was asso-

ciated with increased insulin resistance in Apoe�/� mice [17].

There was also evidence of increased glycolysis and

insulin sensitivity in the muscle of IL-1RI�/� mice,

where pyruvate dehydrogenase kinase isoenzyme 2 (PDK2)

protein was significantly lower, compared with the control

group. This enzyme selectively inhibits pyruvate dehy-

drogenase, a mitochondrial enzymatic complex that

catalyses the oxidative phosphorylation of pyruvate to acetyl

CoA, which prevents muscle from catabolising carbohydrates

and gluconeogenesis precursors [42]. PDK is activated

by fatty acids through increased levels of acetyl CoA [43],

and inhibited by pyruvate [44] and insulin [45]. Increased

PDK activity via an increased lipid/fatty acid oxidation [46]

or insufficient down-regulation of PDK mRNA in insulin-

resistant individuals [45] has been implicated in the patho-

genesis of insulin resistance and non-insulin-dependant

diabetes mellitus in obese subjects. The significantly lower

PDK2 levels in IL-1RI�/� mice suggests that metabolism

was in favour of glucose oxidation at the cost of fat utilisation

in our study. Indeed, acyl-CoA thioesterase 1, which

hydrolyses CoA esters of various lengths to free fatty

acids and CoA-SH and may play an important role in

maintaining appropriate CoA-SH levels during periods of

increased b-oxidation and fatty acid overload [47], was

significantly lower in IL-1RI�/� mice. This protein has been

used as an indicator of b-oxidation [17, 18], and rates of fatty

acid b-oxidation were probably lower in IL-1RI�/� compared

with control mice.

It has been proposed that activation of the pro-inflam-

matory pathway could be a primary mechanism for obesity-

associated insulin resistance [23], as TNF-a is elevated in

adipose tissue and blood from obese rodents, and neutrali-

sation of TNF-a improves insulin sensitivity in these

animals [48]. Under the defined conditions of our study,

inflammatory mediators in adipose tissue may trigger

insulin resistance independent of other factors, such as

obesity. It may be that disruption of IL-1 signalling attenu-

ates ER stress in the adipose tissue. The ER is a key site

where metabolic signals are detected, integrated and trans-

mitted as stress signals, and ER stress may activate c-Jun-N-

terminal kinase and contribute to insulin resistance [49]. ER

stress also activates IKK [50] and may thus represent a

common mechanism for activation of these two stress/

inflammatory signalling pathways. Both dietary and genetic

obesity increases ER stress in adipose and hepatic tissue

[49], leading to the activation of a coordinated adaptive

program called the unfolded protein response. In response

to the accumulation of unfolded proteins in the ER, the rate

of general translation initiation is attenuated by phosphor-

ylation of eIF2a, expression of ER resident protein chaper-

ones and foldases is induced, the ER compartment

proliferates, and ER-associated degradation by the 26S

proteasome system is activated to eliminate the irreparably

misfolded proteins [51]. When the pro-survival efforts are

exhausted due to persistent oxidative stress, ER stress-rela-

ted apoptosis commences [52]. Correlation analysis of

changes in adipose tissue, hepatic and muscle proteins,

and plasma biomarkers of insulin sensitivity and

inflammation, showed an interesting pattern within the

largest cluster of correlated proteins (Fig. 2, cluster A),

which consisted almost exclusively of adipose tissue

proteins and predominantly relate to the regulation of ER

stress. The concerted down-regulation of these proteins,

which are mainly involved in the unfolded protein response

machinery (HSP70, T-complex protein and proteasomes)

and oxidative stress (glutathione peroxidase I, leukotriene

A4 hydrolase, HSP70, Trap 1, prohibitin and dual specificity

protein phosphatase), suggests that the improved insulin

3254 B. de Roos et al. Proteomics 2009, 9, 3244–3256

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

sensitivity in IL-1RI�/� mice was partly associated with less

cellular stress in their adipose tissue, compared with control

mice.

In conclusion, inter-organ proteomic analysis coupled

with correlation analysis with plasma biomarkers of insulin

sensitivity, indicated a role of attenuated cellular/oxidative

stress and inflammation and decreased hepatic fat accu-

mulation as major mechanisms underlying protection

against obesity-induced insulin resistance in mice with

impaired IL-1 signalling. The higher insulin sensitivity in

these mice, compared with controls, may be explained at

least in part by a metabolic shift from fat utilisation to

glucose oxidation in insulin sensitive tissues. Our data

suggest that not obesity per se, but early activation of

inflammation and oxidative stress-related pathways are

likely to contribute to the development of insulin resistance

during a high-fat diet.

This study was funded by Science Foundation Ireland and bythe Scottish Government Rural and Environment Research andAnalysis Directorate (RERAD).

The authors have declared no conflict of interest.

5 References

[1] Hirosumi, J., Tuncman, G., Chang, L., Gorgun, C. Z. et al., A

central role for JNK in obesity and insulin resistance. Nature

2002, 420, 333–336.

[2] Wellen, K. E., Hotamisligil, G. S., Obesity-induced inflam-

matory changes in adipose tissue. J. Clin. Invest. 2003, 112,

1785–1788.

[3] Weisberg, S. P., McCann, D., Desai, M., Rosenbaum, M.

et al., Obesity is associated with macrophage accumulation

in adipose tissue. J. Clin. Invest. 2003, 112, 1796–1808.

[4] Uysal, K. T., Wiesbrock, S. M., Marino, M. W., Hotamisligil,

G. S., Protection from obesity-induced insulin resistance in

mice lacking TNF-alpha function. Nature 1997, 389, 610–614.

[5] Sartipy, P., Loskutoff, D. J., Monocyte chemoattractant

protein 1 in obesity and insulin resistance. Proc. Natl. Acad.

Sci. USA 2003, 100, 7265–7270.

[6] Inouye, K. E., Shi, H., Howard, J. K., Daly, C. H. et al.,

Absence of CC chemokine ligand 2 does not limit obesity-

associated infiltration of macrophages into adipose tissue.

Diabetes 2007, 56, 2242–2250.

[7] Xu, H., Barnes, G. T., Yang, Q., Tan, G. et al., Chronic

inflammation in fat plays a crucial role in the development

of obesity-related insulin resistance. J. Clin. Invest. 2003,

112, 1821–1830.

[8] Hotamisligil, G. S., Murray, D. L., Choy, L. N., Spiegelman,

B. M., Tumor necrosis factor alpha inhibits signaling from

the insulin receptor. Proc. Natl. Acad. Sci. USA 1994, 91,

4854–4858.

[9] Zhang, H. H., Halbleib, M., Ahmad, F., Manganiello, V. C.

et al., Tumor necrosis factor-alpha stimulates lipolysis in

differentiated human adipocytes through activation of

extracellular signal-related kinase and elevation of intra-

cellular cAMP. Diabetes 2002, 51, 2929–2935.

[10] Nguyen, M. T., Favelyukis, S., Nguyen, A. K., Reichart, D.

et al., A subpopulation of macrophages infiltrates hyper-

trophic adipose tissue and is activated by free fatty acids via

Toll-like receptors 2 and 4 and JNK-dependent pathways.

J. Biol. Chem. 2007, 282, 35279–35292.

[11] Thomas, H. E., Irawaty, W., Darwiche, R., Brodnicki, T. C.

et al., IL-1 receptor deficiency slows progression to diabetes

in the NOD mouse. Diabetes 2004, 53, 113–121.

[12] Juge-Aubry, C. E., Somm, E., Chicheportiche, R., Burger, D.

et al., Regulatory effects of interleukin (IL)-1, interferon-

beta, and IL-4 on the production of IL-1 receptor antagonist

by human adipose tissue. J. Clin. Endocrinol. Metab. 2004,

89, 2652–2658.

[13] Moloney, F., Toomey, S., Noone, E., Nugent, A. et al.,

Antidiabetic effects of cis-9, trans-11-conjugated linoleic

acid may be mediated via anti-inflammatory effects in white

adipose tissue. Diabetes 2007, 56, 574–582.

[14] Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A.

et al., Homeostasis model assessment: insulin resistance and

beta-cell function from fasting plasma glucose and insulin

concentrations in man. Diabetologia 1985, 28, 412–419.

[15] Perseghin, G., Caumo, A., Caloni, M., Testolin, G. et al.,

Incorporation of the fasting plasma FFA concentration into

QUICKI improves its association with insulin sensitivity in

nonobese individuals. J Clin. Endocrinol. Metab. 2001, 86,

4776–4781.

[16] Arbones-Mainar, J. M., Ross, K., Rucklidge, G. J., Reid, M.

et al., Extra virgin olive oils increase hepatic fat accumula-

tion and hepatic antioxidant protein levels in APOE

(�/�)() mice. J. Proteome. Res. 2007, 6, 4041–4054.

[17] de Roos, B., Rucklidge, G., Reid, M., Ross, K. et al., Diver-

gent mechanisms of cis9, trans11-and trans10, cis12-

conjugated linoleic acid affecting insulin resistance and

inflammation in apolipoprotein E knockout mice: a proteo-

mics approach. FASEB J. 2005, 19, 1746–1748.

[18] de Roos, B., Duivenvoorden, I., Rucklidge, G., Reid, M. et al.,

Response of apolipoprotein E�3-Leiden transgenic mice to

dietary fatty acids: combining liver proteomics with

physiological data. FASEB J. 2005, 19, 813–815.

[19] de Roos, B., Geelen, A., Ross, K., Rucklidge, G. et al., Iden-

tification of potential serum biomarkers of inflammation

and lipid modulation that are altered by fish oil supple-

mentation in healthy volunteers. Proteomics 2008, 8,

1965–1974.

[20] Storey, J. D., Tibshirani, R., Statistical significance for

genomewide studies. Proc. Natl. Acad. Sci. USA 2003, 100,

9440–9445.

[21] Houstis, N., Rosen, E. D., Lander, E. S., Reactive oxygen

species have a causal role in multiple forms of insulin

resistance. Nature 2006, 440, 944–948.

[22] Furukawa, S., Fujita, T., Shimabukuro, M., Iwaki, M. et al.,

Increased oxidative stress in obesity and its impact

on metabolic syndrome. J. Clin. Invest. 2004, 114,

1752–1761.

Proteomics 2009, 9, 3244–3256 3255

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

[23] Hotamisligil, G. S., Inflammation and metabolic disorders.

Nature 2006, 444, 860–867.

[24] Wojcik, C. and Di Napoli, M., Ubiquitin-proteasome system

and proteasome inhibition: new strategies in stroke ther-

apy. Stroke 2004, 35, 1506–1518.

[25] Naujokat, C., Fuchs, D., Berges, C., Adaptive modification

and flexibility of the proteasome system in response to

proteasome inhibition. Biochim. Biophys. Acta 2007, 1773,

1389–1397.

[26] Miller, R. S., Diaczok, D., Cooke, D. W., Repression of GLUT4

expression by the endoplasmic reticulum stress response in

3T3-L1 adipocytes. Biochem. Biophys. Res. Commun. 2007,

362, 188–192.

[27] Solary, E., Eymin, B., Droin, N., Haugg, M., Proteases,

proteolysis, and apoptosis. Cell Biol. Toxicol. 1998, 14,

121–132.

[28] Bodles, A. M., Varma, V., Yao-Borengasser, A., Phanavanh,

B. et al., Pioglitazone induces apoptosis of macrophages in

human adipose tissue. J. Lipid Res. 2006, 47, 2080–2088.

[29] Henegar, C., Tordjman, J., Achard, V., Lacasa, D. et al.,

Adipose tissue transcriptomic signature highlights the

pathological relevance of extracellular matrix in human

obesity. Genome Biol. 2008, 9, R14.

[30] Kanda, H., Tateya, S., Tamori, Y., Kotani, K. et al., MCP-1

contributes to macrophage infiltration into adipose tissue,

insulin resistance, and hepatic steatosis in obesity. J. Clin.

Invest. 2006, 116, 1494–1505.

[31] Node, K., Huo, Y., Ruan, X., Yang, B. et al., Anti-inflamma-

tory properties of cytochrome P450 epoxygenase-derived

eicosanoids. Science 1999, 285, 1276–1279.

[32] Campbell, W. B., New role for epoxyeicosatrienoic acids as

anti-inflammatory mediators. Trends Pharmacol. Sci. 2000,

21, 125–127.

[33] Thomas, H., Schladt, L., Knehr, M., Oesch, F., Effect of

diabetes and starvation on the activity of rat liver epoxide

hydrolases, glutathione S-transferases and peroxisomal

beta-oxidation. Biochem. Pharmacol. 1989, 38, 4291–4297.

[34] Roskams, T., Yang, S. Q., Koteish, A., Durnez, A. et al.,

Oxidative stress and oval cell accumulation in mice and

humans with alcoholic and nonalcoholic fatty liver disease.

Am. J. Pathol. 2003, 163, 1301–1311.

[35] Donnelly, K. L., Smith, C. I., Schwarzenberg, S. J., Jessurun,

J. et al., Sources of fatty acids stored in liver and secreted

via lipoproteins in patients with nonalcoholic fatty liver

disease. J. Clin. Invest. 2005, 115, 1343–1351.

[36] Matsuki, T., Horai, R., Sudo, K., Iwakura, Y., IL-1 plays an

important role in lipid metabolism by regulating insulin

levels under physiological conditions. J. Exp. Med. 2003,

198, 877–888.

[37] Garcia, M. C., Wernstedt, I., Berndtsson, A., Enge, M. et al.,

Mature-onset obesity in interleukin-1 receptor I knockout

mice. Diabetes 2006, 55, 1205–1213.

[38] Newberry, E. P., Xie, Y., Kennedy, S. M., Luo, J. et al.,

Protection against Western diet-induced obesity and hepa-

tic steatosis in liver fatty acid-binding protein knockout

mice. Hepatology 2006, 44, 1191–1205.

[39] Matsuyama, S., Kitamura, T., Enomoto, N., Fujita, T. et al.,

Senescence marker protein-30 regulates Akt activity and

contributes to cell survival in Hep G2 cells. Biochem.

Biophys. Res. Commun. 2004, 321, 386–390.

[40] Samuel, V. T., Liu, Z. X., Qu, X., Elder, B. D. et al.,

Mechanism of hepatic insulin resistance in non-alcoholic

fatty liver disease. J. Biol. Chem. 2004, 279, 32345–32353.

[41] Goke, B., Herrmann-Rinke, C., The evolving role of alpha-

glucosidase inhibitors. Diabetes Metab. Rev. 1998, 14, S31–S38.

[42] Sugden, M. C., Holness, M. J., Therapeutic potential of the

mammalian pyruvate dehydrogenase kinases in the

prevention of hyperglycaemia. Curr. Drug Targets Immune

Endocr. Metabol. Disord. 2002, 2, 151–165.

[43] Kerbey, A. L., Randle, P. J., Cooper, R. H., Whitehouse, S.

et al., Regulation of pyruvate dehydrogenase in rat heart.

Mechanism of regulation of proportions of depho-

sphorylated and phosphorylated enzyme by oxidation of

fatty acids and ketone bodies and of effects of diabetes: role

of coenzyme A, acetyl-coenzyme A and reduced and

oxidized nicotinamide-adenine dinucleotide. Biochem.

J. 1976, 154, 327–348.

[44] Ravindran, S., Radke, G. A., Guest, J. R., Roche, T. E., Lipoyl

domain-based mechanism for the integrated feedback

control of the pyruvate dehydrogenase complex by

enhancement of pyruvate dehydrogenase kinase activity.

J. Biol. Chem. 1996, 271, 653–662.

[45] Majer, M., Popov, K. M., Harris, R. A., Bogardus, C. et al.,

Insulin downregulates pyruvate dehydrogenase kinase

(PDK) mRNA: potential mechanism contributing to

increased lipid oxidation in insulin-resistant subjects. Mol.

Genet. Metab. 1998, 65, 181–186.

[46] Randle, P. J., Priestman, D. A., Mistry, S. C., Halsall, A.,

Glucose fatty acid interactions and the regulation of glucose

disposal. J. Cell Biochem. 1994, 55, 1–11.

[47] Hunt, M. C., Solaas, K., Kase, B. F., Alexson, S. E., Char-

acterization of an acyl-coA thioesterase that functions as a

major regulator of peroxisomal lipid metabolism. J. Biol.

Chem. 2002, 277, 1128–1138.

[48] Hotamisligil, G. S., Shargill, N. S., Spiegelman, B. M.,

Adipose expression of tumor necrosis factor-alpha: direct

role in obesity-linked insulin resistance. Science 1993, 259,

87–91.

[49] Ozcan, U., Cao, Q., Yilmaz, E., Lee, A. H. et al., Endoplasmic

reticulum stress links obesity, insulin action, and type 2

diabetes. Science 2004, 306, 457–461.

[50] Hung, J. H., Su, I. J., Lei, H. Y., Wang, H. C. et al., Endo-

plasmic reticulum stress stimulates the expression of

cyclooxygenase-2 through activation of NF-kappaB and

pp38 mitogen-activated protein kinase. J. Biol. Chem. 2004,

279, 46384–46392.

[51] Wu, J., Kaufman, R. J., From acute ER stress to physiolo-

gical roles of the Unfolded Protein Response. Cell Death

Differ. 2006, 13, 374–384.

[52] Malhotra, J. D., Kaufman, R. J., Endoplasmic reticulum

stress and oxidative stress: a vicious cycle or a

double-edged sword? Antioxid. Redox Signal. 2007, 9,

2277–2293.

3256 B. de Roos et al. Proteomics 2009, 9, 3244–3256

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com