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University of Groningen
An adipocentric view of the development of insulin resistanceSzalowska, Ewa
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Introduction
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An adipocentric view of the development of insulin resistance
Ewa Szalowska
Introduction
2
ISBN: 978-90-367-4849-0 Printed by Wӧhrmann Print Service Cover design: Ewa Szalowska Layout: Sacha van Hijum ©2011 Copyright Ewa Szalowska All rights reserved. No parts of this book may be reproduced, stored in retrieval systems, or transmitted in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the author. Printing of this thesis was supported by UMCG, Rijksuniversiteit Groningen
Introduction
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RIJKSUNIVERSITEIT GRONINGEN
An adipocentric view of the development of insulin resistance
Proefschrift
ter verkrijging van het doctoraat in de
Medische Wetenschappen aan de Rijksuniversiteit Groningen
op gezag van de Rector Magnificus, dr. E. Sterken, in het openbaar te verdedigen op
woensdag 13 april 2011 om 13.15 uur
door
Ewa Szalowska
geboren op 14 mei 1975
te Wrocław, Polen
Introduction
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Promotor: Prof. dr. R.J.Vonk Copromotores: Dr. H. Roelofsen
Dr. A. Hoek Beoorelingscommissie: Prof. dr. G.M.M. Groothuis
Prof. dr. B.H. Wolffenbuttel Prof. dr. M. Haluzik
Introduction
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Introduction
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Paranimfen: Marianne Schepers Kees Meijer Front cover: Histological staining of adipose tissue
Introduction
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Contents Introduction 9 Scope of the thesis 20 Results and discussion 23 Concluding remarks and future perspectives 27 Summary 31 Samenvatting 34 References 38 Section I Optimization and development of proteomics technologies 42 Chapter 1 Fractional factorial design for optimisation of the SELDI protocol for human adipose tissue culture media 43 Chapter 2 Characterization of the human visceral adipose tissue secretome 63 Section II Role of adipose tissue in the development of insulin resistance 88 Chapter 3 Sub-chronic administration of stable GIP analogue in mice decreases serum LPL activity and body weight 89 Chapter 4 Adipokines and energy metabolism genes, but not proinflammatory genes are deregulated in patients with higher HOMA and lower HDL 107
Chapter 5 The “adipokine” resistin is more abundant in human liver than in adipose tissue and it is not upregulated by lipopolysaccharide 123 Chapter 6 Comparative analysis of the human hepatic and adipose tissue transcriptomes during LPS-induced inflammation leads to the identification of differential biological pathways and candidate biomarkers 145 Abbreviations 171 Acknowledgements 173 Curriculum Vitae 177
Introduction
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Introduction
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Introduction
Introduction
10
1. Insulin resistance
In an era of increasing obesity, which strongly predisposes to the development of insulin
resistance (IR) and related pathologies such as type 2 diabetes (T2D) and cardiovascular
diseases, adipose tissue became an object of multiple studies devoted to elucidate its role in
regulation of whole body energy metabolism[1].
IR is regarded as a state where peripheral organs involved in the regulation of energy
homeostasis (adipose tissue, liver, and muscles) are not able to maintain physiological blood
glucose concentration with normal insulin level. The increased demand for insulin can be
compensated with enhanced insulin production by pancreatic β -cells, however this
mechanism fails on a long –term basis due to β-cell dysfunction, and ultimately leads to T2D
[2] .
The gold standard for assessment and quantification of IR is the "hyperinsulinemic
euglycemic clamp," which measures the amount of glucose necessary to compensate for an
increased insulin level without causing hypoglycemia [3]. Due to the laborious character of
this test it is difficult to perform in clinical practice therefore are other alternative methods
used to asssess insulin sensitivity. One of them is the Homeostatic Model Assessment
(HOMA). This method measures fasting glucose and insulin levels to calculate IR. The
HOMA values correlate well with the results of the clamping studies [4] and can be used as
its surrogate.
As mentioned above, one of the factors predisposing to the development of IR is obesity.
However, it was reported that abdominal obesity independently of total obesity is strongly
associated with IR. Next to factors related to (visceral) obesity such as increased waist
circumference (WC), and high body mass index (BMI) , IR patients are characterized by
increased levels of free fatty acids (FFA) leading to ectopic fat depots, decreased high-density
lioprotein (HDL)-cholesterol, and increased low-density lipoprotein (LDL)-cholesterol.
Nowadays, it is also commonly accepted that low grade systemic inflammation with inreased
serum levels of CRP, IL-6, TNFα, and IL-1β closely predisposes to IR. However, it is not
fully elucidated if in humans inflammation is a cause or consequence of IR [5; 6].
Introduction
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2. Adipose tissue as an endocrine organ and its link to insulin resistance
In a traditional view, adipose tissue was regarded as a tissue consisting of inert adipocytes
involved in storage of energy in form of triglycerides (TG). However, since the discovery of
leptin acting as a hormone via its receptors localized in peripheral tissues, adipose tissue
became recognized as an endocrine organ. Leptin acts via leptin receptors which have been
identified in the brain areas involved in feeding regulation, and several peripheral tissues
including adipose tissue, ovaries, testis, placenta, adrenal medulla, liver, pancreatic β- cells,
lung, jejunum, peripheral blood mononuclear cells, chondrocytes, heart, and skeletal muscle.
The known leptin functions are related to energy metabolism, reproduction, and
inflammation. For example in isolated skeletal muscles, liver and pancreas acute leptin
treatment increased fatty acid oxidation in an AMP-activated protein kinase (AMPK)
dependent manner and suppressed the hypothalamus signaling. Moreover, leptin modulates
hepatic gluconeogenesis and pancreatic β-cell function, and promotes energy expenditure.
Leptin is also known to modulate inflammatory functions by stimulating TNFα and IL-6
expression, but suppressing resistin and retinol binding protein 4 expression [7]. In addition
to leptin, the endocrine functions of adipose tissue are exerted by other adipose tissue derived
signaling molecules (adipokines) acting in different target organs via their receptors. For
example adiponectin is highly expressed in adipose tissue and it is an abundant plasma
protein [7]. Adiponectin binds to its own receptors: AdipoR1 and AdipoR2 which are
ubiquitously expressed [8]. The role of adiponectin in obesity, IR, and T2D is broadly
investigated and it was demonstrated that adiponectin displays insulin sensitizing properties
and can ameliorate systemic insulin resistance symptoms. In skeletal muscle and adipose
tissue adiponectin stimulates fatty acid oxidation and glucose uptake. In liver adiponectin
suppresses glucose production, and in the central nervous system it was shown to regulate
energy expenditure through activation of AMPK in the hypothalamus [7]. Additionally,
adiponectin exhibits strong anti-inflammatory, anti-diabetic, and anti-atherogenic actions [9].
Next to the established adipocyte-derived adipokines such as leptin and adionectin, there are
adipokines produced mostly by stromal vascular fraction of adipose tissue such as IL-6, IL-
1β, TNFα, resistin, visfatin, or retinol binding protein 4 [7]. The number of adipokines is still
growing with the recently discovered adipokines such as chemerin, omentin or cartonectin
[10; 11]. The known functions of selected adipokines are summarized in Table 1.
The adipose tissue constellation (complex tissue consisting of adipocytes, in a close
proximity of immune cells (macrophages, lymphocytes ) surrounded by a network of blood
Introduction
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vessels, with endothelial cells [12]) implies on tight interactions between metabolic,
endocrine, and immune functions within the organ. Thereby it seems to be logical why
metabolic perturbations observed in adipose tissue of obese and IR patients are very often
associated with inflammation-like symptoms manifested by elevated production of pro-
inflammatory adipokines such as IL-6, TNFα or CRP. A comprehensive understanding of
endocrine actions of adipose tissue and exact feedback mechanisms regulating adipokines
expression by their target organs is just an emerging issue. At present, adipose tissue
endocrine functions are regarded as interplay between environment, genetic factors and
multi-organ interactions, schematically depicted in Figure 1. In future the Systems Biology
approach is expected to bring more insights into the dynamic and complex mode of action of
adipose tissue [13; 14].
Introduction
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Table 1. Examples of adipokines that display dual inflammatory and immunological functions [10; 11].
Adipokines
with dual
function Metabolic function Immune function
Adiponectin insulin sensitizing, anti-diabetic anti-inflammatory
Leptin
regulation of satiety, energy
expenditure, pancreatic functions, and
intracellular lipid content immune-modulating
Resistin insulin-desensitizing
pro-inflammatory?, depending on the
cell types
TNFα insulin-desensitizing pro-inflammatory
IL-6, IL-8, IL-1β insulin-desensitizing pro-inflammatory
Visfatin
interfering with insulin-receptor
signaling pro-inflammatory
C3A TG synthesis, insulin sensitivity
adipose tissue macrophage infiltration
and cytokine production
Cartonectin regulation of adipokine secretion anti-inflammatory, acting on monocytes
Chemerin
Insulin sensitizing, enhances insulin
dependent glucose uptake andIRS-1
phosphorylation in humans
Anti-inflammatory effects on activated
macrophages expressing the chemerin
receptor
Omentin
Enhances insulin –stimulated glucose
transport and Akt phosphorylation-
insulin sensitizing in humans Not known
Retinol binding
protein 4
Insulin desensitizing in humans,
lifestyle modification and exercise
training reduces serum RBP4 levels Not known
Vaspin Insulin sensitizing Suppresses TNFα and resistin expression
Introduction
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Figure 1. Adipose tissue endocrine functions as interplay between environment, life style, genes, and multi-organ interactions. Green arrows illustrate stimulation and red arrows suppression of gene expression, adopted from Rabe K. et al [10].
During the development of obesity and IR the homeostasis in adipose tissue can be
disturbed, what results in a changed adipokine profile with decreased adiponectin levels and
increased levels of leptin, several cytokines, visfatin and others [15-18]. Due to the altered
adipose tissue endocrine profile the released adipokines affect functions of the target organs,
what results in their altered metabolic functioning and these events synergistically can lead to
disturbance of metabolic and immune homeostasis resulting in systemic IR [19]. There is
evidence for implication of several mechanisms involved in the development of systemic IR
such as (a) excess of free fatty acids released from adipose tissue activating Toll like
receptors in other tissues and causing systemic inflammation [20], (b) GIP signaling
overstimulation in obesity [21] (c) oxidative stress and mitochondrial dysfunction [22; 23],
(d) endotoxemia and inflammation [24-27], (e) alternations in T-cells populations in adipose
tissue and infiltration of macrophages [28-30], however still the exact triggers leading to the
development of systemic IR remain unknown.
Introduction
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3. Mechanisms leading to the development of insulin resistance
As indicate above several mechanisms are proposed to be involved in the development of IR;
in this thesis we will focus on the role of adipose tissue in the development of IR related to
glucose dependent insulinotropic polypeptide (GIP) signaling and inflammation.
A) GIP as a link between overnutrition, obesity and T2D
Glucose dependent insulinotropic polypeptide (GIP), also known as gastric inhibitory
polypeptide, is a gastrointestinal hormone secreted from gastrointestinal track upon fat
ingestion [31] and its serum level positively correlates with the intestinal glucose influx rate,
suggesting that GIP secretion is also influenced by glucose absorption [32].
GIP it is one of gastrointestinal hormones which acts on adipose tissue and pancreatic beta
cells thereby it is suggested as a link with obesity and T2D [31; 33]. Besides acting on the
beta cells and adipose tissue GIP exerts its functions on bone and central nervous system
[31]. Mainly due to the GIP’s beneficial actions on the beta cells this hormone gained a lot of
attention as an anti-diabetic factor. It was shown that GIP stimulates glucose induced insulin
secretion, enhances insulin gene transcription and biosynthesis, induces beta-cell neogenesis,
proliferation, and differentiation [31].
The GIP receptor (GIPR) knockout mice were resistant to obesity on a high fat diet and did
not accumulate fat in liver or muscles instead [21]. Besides the spectacular effects in the
GIPR knockout mice on preventing the development of obesity on a high fat diet there are
several other studies exploring this phenomenon in chemical/pharmacological GIPR
knockout mice [34-36]. For example chronic administration of the GIPR antagonist (Pro3)
GIP to adult diabetic ob/ob mice fed high-fat-diet resulted in substantial improvement in
metabolic status [34].
These observations might suggest that opposite actions, such as GIP oversignaling, could
contribute or accelerate the development of obesity and eventually IR and T2D. Indeed, GIP
was shown in vitro to stimulate lipoprotein lipase (LPL) activity, responsible for
accumulation of triglycerides (TG) in adipose tissue, to increase lipogenesis, fatty acids and
glucose uptake, to enhance insulin –induced fatty acid incorporation leading to hypertrophy
of adipocytes and possibly promoting obesity and eventually T2D in vivo [31]. However,
there is still not enough evidence that overstimulation of GIP signaling or elevated levels of
GIP in obesity function as a link between overnutrition and the development of obesity and
type T2D in vivo in humans [37; 38] . In contrast, there is an emerging view from clinical
Introduction
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trials that application of GIP agonists (overstimulation of GIP signaling) led to weight loss,
blood pressure reduction, and, as expected, beta-cell function improvements [39].
B) Inflammation in adipose tissue-is it a cause (or consequence) of insulin resistance
Adipose tissue of obese animals and humans can display low grade inflammation with
upregulated proinflammatory proteins in both adipose tissue and blood such as TNFα, IL-1β,
IL-6, IL-8, PAI-1, SAA, CRP, MCP-1, [5; 5; 15; 25; 40; 41] and others. Addressing the
question if the whole body (systemic) IR is a cause or a consequence of inflammation
initiated in adipose tissue one may refer to studies described by Park et all [42] and Xu et all
[40]. In both studies IR was developed by diet induced obesity (DIO) in C57BL/6 mice.
Study of Park at all showed that IR occurred in heart after 1.5 week of high fat diet and it was
followed after 3 weeks by IR in adipose tissue, liver and skeletal muscles. Since week 1.5 a
significant increase in blood leptin and resistin levels were observed and hyperinsulinemia
occurred after 3 weeks. In studies conducted by Xu et all it was shown that in DIO a
significant increase in proinflammatory markers in adipose tissue but not other organs
occurred after 16 weeks of high fat diet, The comparison of these two studies suggests that
decreased glucose uptake indicative for IR occurs before inflammatory markers are up
regulated in adipose tissue thereby excluding inflammation as a pivotal event leading to IR.
However, in other studies it was shown that endotoxemia, associated with elevated LPS
levels can be a pivotal event leading to IR. It was shown that LPS induced inflammation
leads to IR in rodents and humans [24-27; 43-45]. In humans during short term endotoxemia
insulin sensitivity assessed by frequently sampled intravenous glucose tolerance testing
(FSIGT) was significantly decreased. Furthermore, DIO induced upregulation of multiple
proteins associated with IR , exampled by IL-6, TNFα, MCP-1 and CXCL10 and components
of insulin signaling pathway were affected: insulin receptor substrate-1 was decreased ,while
suppressor of cytokine signaling proteins (1 and 3) were markedly induced [24].
4. Omics technologies to be developed for the current research questions
Proteomics technologies involve experimental approaches that allow studying the total
proteome or specific group of proteins from the proteome such as secretome, focusing on
secreted proteins and peptides or phosphorylated proteins (phosphoproteomics). The
technologies used in proteomics are often based on mass spectrometry (MS). One of them is
Introduction
17
commonly used in biomarkers research Surface Enhanced Laser/Desorption Ionization
(SELDI) technology. In SELDI biological samples are applied to a protein chip with specific
biochemical binding properties on the surface such as hydrophobic, anion, cation, metal.
During the chip preparation, proteins with specific characteristics are bound to the chip
allowing then reduction of sample complexity. After washing the chip is treated with the
energy-adsorbing matrix. The matrix allows flying of the bound proteins upon laser-induced
ionization and time of flight (TOF) separation leads to determination of mass to charge ratio
(m/z) of the proteins. SELDI generates protein profiles which have to be analyzed with
statistical and bioinformatical tools in order to find differential proteins or protein profiles
which could be used as biomarkers. Further downstream analyses are required such as protein
digestion and peptide mass fingerprinting or MS/MS analysis to identify the differential
protein(s) [46].
The commonly used technique in the experimental cell lines research and proteomics is
Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC). SILAC is a
straigforward approach where two cell populations (control and treatment) are grown in cell
culture media that are identical except that one of them contains a “heavy”and the other one a
“light”from of a particular amino acid (e.g. 13C and 12C labeled respectively). The labeled
amino acids are incorporated in 100% after a number of call devisions and after processing of
the cultures it is possible to apply MS based protein identification and to determine the
differences between two experimental conditions. Example of another MS-based technology
is CILAIR (Comparison of Isotope Labeled Amino Acid Incorporation Rates) [47] developed
for application in non-dividing cells and tissue cultures. In CILAIR experiments (adipose)
tissue samples are depleted from lysine and subsequently cultured in the presence of 13C-
labeled lysine. Newly synthesized proteins will incorporate the 13C-labeled lysine allowing to
discriminate between proteins changed in abundance due to the experimental conditions (13C-
labeled lysine) and proteins present a priori, derived form serum or tissue (12C- lysine). After
the culturing, the obtained biological samples (adipose tissue culture media) are concentrated
by ultra-filtration, fractionated by SDS-PAGE followed by in-gel digestion of excised bands.
The resulting peptide digests are analyzed by liquid chromatography coupled with mass
spectrometry (LC-MS/MS). The MS data are subsequently analyzed by software matching
the detected peptide masses into known proteins and performing protein identification. By
comparison of the C13\C12 ratios in identified proteins, information about quantitative
changes between experimental groups can be derived. The advantage of CILAIR in
comparison to a similar technique such as SILAC is that CILAIR does not require 100% label
Introduction
18
incorporation, which is only possible in dividing cells. In the tissue culture systems mostly
non-dividing cells are present and the only requirement for detection of altered proteins due
to the treatment is de novo protein synthesis.
Analysis of proteomics data requires application of complex bioinformatitcal tools. There are
several tools developed for analysis of proteomic data freely or commercially available. For
proteomic analysis the basic tools are for example: Expasy (Expert Protein Analysis System)
(http://www.expasy.org) the proteomics server of the Swiss Institute of Bioinformatics (SIB)
dedicated to the analysis of protein sequences and structures. UniProt
(http://www.uniprot.org) aims to provide the scientific community with a comprehensive,
high-quality and freely accessible resource of protein sequence and functional information
and databases for protein ontology. Other supporting tools such as SecretomeP 2.0 server
produces ab initio predictions of non-classical i.e. not signal peptide triggered protein
secretion. The method queries a large number of other feature prediction servers to obtain
information which is integrated into the final secretion prediction information and can be
used for prediction of secreted and intracellular proteins by means of algorithms
http://www.cbs.dtu.dk/services/SecretomeP/ [29; 48; 48]. STRING (Search Tool for the
Retrieval of Interacting Genes/Proteins) is being developed for reconstruction of functional
protein/gene networks based on known and predicted protein-protein interactions
(http://string.embl.de).
Another commonly used omics technology is the DNA microarray approach, which
facilitates the simultaneous quantification of thousands of mRNAs and provides
comprehensive information about their expression level under different experimental
conditions or in different patients groups.
DNA microarrays contain thousands of nucleotide probes fixed to a solid surface. Each of a
probe represents a short section of a gene or other DNA constituent, which can hybridize to
reverse transcribed mRNA sample or copy RNA (cRNA). These RNA probes are labeled
with a fluorescent dye which can be excited with a laser light. The resulting fluorescent
emission is representative for expression level of a certain mRNA and can be compared
within the analyzed samples. Next challenge in the DNA microarray technology is the
statistical analysis and the biological interpretation [49]. There are several software packages
which are developed for the statistical analysis such as GeneSpring GX developed by Agilent
Technologies, which can be used for many DNA microarray platforms (Affymetrix, Illumina,
Agilent) and can be used for identification of significantly affected genes. The biological
interpretation of the data can be performed by means of freely available bioinformatical
Introduction
19
resources such as DAVID [50; 51], STRING or commercial ones such as MetaCore
developed by GeneGo. These bioinformatical tools enable to perform GO analysis aiming to
extract the significantly affected (overrepresented) biological processes and to identify
affected biological pathways (DAVID, MetaCore), or to build gene functional networks
(MetaCore, STRING), which are built based on the information present in the literature from
all the known and predicted gene/protein interactions.
Omics technologies are broadly used to identify biomarkers for T2D [46; 49; 50; 52]. The
potential biomarkers should be specific to the disease and it should be possible to detect them
early in order to undertake actions preventing development of the disease. At present, there
are multiple candidate biomarkers for insulin resistance exampled by CRP, TNFα, IL-6
however their specificity for early detection of IR and clinical application still has to be
proven [19; 41].
Introduction
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Scope of the thesis
The discoveries of leptin and other adipokines had greatly contributed to the recognition of
adipose tissue as an endocrine organ involved in regulation of the energy and inflammatory
homeostasis [53]. Nowadays it is generally acknowledged that impairment of adipose tissue
endocrine and metabolic characteristics correlated with obesity can lead to systemic IR and
eventually T2D.
The main aims of this thesis were:
(1) to explore role of adipose tissue in the development of insulin resistance related to:
(A) GIP action,
(B) clinical parameters,
(C) inflammation.
(2) to optimize and develop omics technologies for the specified above research questions
(1A) In order to study the GIP signaling in adipose tissue we applied stable GIP analogue to
mice and tested the hypothesis if excess of GIP can enhance or accelerate the development of
obesity and IR. (1B) In order to find the primary events in adipose tissue implicated in the
development of IR we studied the adipose tissue selected gene expression in association with
clinical parameters indicative for early IR such as high BMI and HOMA and low HDL in
non-diabetic women. (1C) Low grade inflammation associated with obesity and IR was
mimicked by application of LPS to human adipose tissue ex vivo in order to identify specific
candidate biomarkers indicative for inflammation/IR of adipose tissue.
(2) For this specific adipose tissue research several techniques had to be developed with a
special attention to novel proteomics technologies and integration of proteomics and
transcriptomics data.
Therefore the thesis is divided into two sections: (I) Development and optimization of
proteomics techniques; (II) Role of adipose tissue in the development of IR in relation to: (A)
GIP signaling (B) clinical parameters (C) inflammation leading to identification of
candidate biomarkers
Section I is devoted to the optimization of proteomic methodologies such as SELDI and LC-
MS/MS based proteomics technology for the purposes of investigation of the adipose tissue
secretome, and consists of 2 chapters: (1) “Fractional Factorial Design for Optimization of
the SELDI Protocol for Human Adipose Tissue Culture Media” and (2) “Characterization of
The Human Visceral Adipose Tissue Secretome”.
Introduction
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In chapter 1 we performed optimization of SELDI sample preparation and application for
the SELDI protein chip protocol. One of the major challenges in SELDI technology is to find
out the most optimal sample preparation protocol where factors such as protein concentration,
salt concentration, pH , background reduction, type of applied matrix (energy absorbing
molecule (EAM)), and many others can be optimized. For the protocol optimization the
fractional factorial design was used.
In chapter 2 the characterization of the visceral adipose tissue secretome by means of LC-
MS/MS proteomic technology is described. The article describes optimization of several
adipose tissue culture set-ups aiming to remove highly abundant serum proteins such as
albumin or hemoglobin in order to apply adipose tissue culture media for LC-MS/MS
analysis. After selection of the best culture set up an experiment was performed where
adipose tissue samples were cultured in media containing 13C6,15N2 L-lysine to validate the
origin of the identified proteins (adipose tissue or serum derived). After SDS-PAGE
fractionation, and protein digestion with trypsin, proteins were identified by LC-MS/MS
technology. The characterisation of the adipose tissue secretome was performed in order to
deepen our understanding of the endocrine functions of the adipose tissue.
Section II is devoted to investigations of the role of adipose tissue in the development of IR
related to different mechanisms such as: (A) GIP signaling, (B) clinical parameters, (C)
inflammation. In Section IIA, Chapter 3 entitled:” Sub-Chronic Administration of Stable
GIP Analogue in Mice Decreases Serum LPL Activity and Body Weight” we described
experiments where we treated mice fed both chow or high fat diets with the stable GIP
analogue and followed the GIP effect on the development of obesity assessed by body mass,
serum LPL activity, serum biochemical parameters, and gene expression in adipose tissue.
The aim of these experiments was to evaluate the role of GIP on the development of obesity
and IR and in particular to investigate the role of GIP on the adipose tissue gene expression.
In Section IIB, Chapter 4 entitled “Expression of selected proinflammatory and metabolic
genes in human adipose tissue in relation to IR” we measured gene expression of subsets of
proinflammatory and metabolic genes in adipose tissue of non diabetic women and associated
the gene expression with anthropometric and biochemical characteristics such as body mass
index (BMI), waist circumference (WC), homeostasis model assessment index (HOMA),
glucose, insulin, and high density lipoprotein (HDL) serum levels. In this study we aimed to
find a link between adipose tissue gene expression related to altered anthropometric and
serum biochemical parameters indicative on early IR such as increased HOMA or decreased
Introduction
22
HDL levels in order to understand changes in the adipose tissue gene expression associated
with early events leading to the development of IR in humans.
In Section IIC, Chapter 5 “Resistin is More Abundant in Liver than Adipose Tissues and Is
Not Up-Regulated by Lipopolysachcaride” we evaluated carefully if indeed resistin can be
considered as an adipokine correlated with markers of inflammation in humans, as suggested
in the literature. There is evidence that serum resistin levels are elevated in obese patients,
however the role of resistin in IR and T2D remains controversial. We were interested whether
inflammation induces expression of resistin in organs involved in regulation of total body
energy metabolism, such as liver and adipose tissue. We therefore studied resistin gene
expression, protein abundance and localization in human healthy liver and human visceral
adipose tissue and analyzed the effect of LPS on resistin regulation.
In Section IIC, Chapter 6 “Comparative Analysis of The Hepatic and Adipose Tissue
Transcriptome During LPS-Induced Inflammation Leads to The Identification of Differential
Pathways and Candidate Biomarkers” we induced inflammation with LPS in adipose and
liver tissues to simulate endocrine activity of these organs as observed in vivo during
systemic low grade inflammation associated with IR. Our aim was to compare the inflamed
transcriptomes of both tissues in order to better understand their contribution to the
development of systemic inflammation and IR. Additionally the in silico predicted
inflammatory secretome (generated from the transcriptome data of adipose tissue) was
integrated with quantitative proteomics data (CILAIR).
Introduction
23
Results and Discussion
In Section I (Development and optimization of proteomics techniques) we described
optimization of protocols for adipose tissue culture media applicable for SELDI and LC-
MS/MS.
The SELDI protocol was optimized for the number of peaks and spectra quality by use of the
fractional factorial design. Using this statistical method we tested several factors derived
from the SELDI protocol in a relatively small number of experiments and identified critical
and not important factors. The protocol factors that significantly improved the SELDI spectra
were pretreatment of the sample, type of energy absorbing molecule (EAM), solvent of EAM,
saturation of EAM, and amount of EAM applied to the chip. Whereas, the molecular
concentration of the binding buffer did not influence the spectra quality and quantity (Chapter
1). Moreover, it is important to realize that different protein samples require individual
optimization experiments depending on the protein composition, background of the sample,
protein concentration etc.
The optimized protocol is intended for use in screening for differences in the adipose tissue
culture media derived from lean and obese patients in order to identify biomarkers associated
with early events of the development of IR.
The identification of the adipose tissue secretome by means of LC-MS/MS involved
optimization of experiments leading to removal of high abundant serum proteins (Chapter 2).
This approach resulted in identification of adipose tissue culture set up yielding a sample
(adipose tissue culture media) free of albumin and high abundant proteins. Addition of
labeled Lys (13C) into the culture media allowed distinguishing between proteins synthesized
by adipose tissue during the culture time from contaminating proteins (serum proteins or
proteins released by the tissue during preparation). Thereby, for the first time the human
visceral adipose tissue secretome was determined. In total 259 proteins were identified with
≥99% confidence; 108 proteins contained a secretion signal peptide of which 70 incorporated
the label and were considered secreted by adipose tissue. Within the secretome proteins such
as: adiponectin, adipsin, gelsolin, macrophage colony stimulating factor-1 (M-CSF), pigment
epithelium-derived factor (PEDF), plasma retinol binding protein (RBP), plasminogen
activator inhibitor-1 (PAI-1), and others were identified. Secreted proteins were classified
into functional groups such as: signaling/regulation, extra-cellular matrix, immune function,
degradation and other, which are in line with the present knowledge about the endocrine and
immuno-modulating actions of adipose tissue.
Introduction
24
The knowledge about the secretome of adipose tissue contributes to a better understanding of
the endocrine activities of adipose tissue and could facilitate identification of the specific
adipose tissue biomarkers related to IR.
In Section II (Role of adipose tissue in the development of obesity and IR) we investigated
different aspects related to adipose tissue actions and the development of IR such as (A)
effect of GIP oversignaling, (B) adipose tissue gene expression in respect to clinical
parameters and (C) inflammation. The application of the stable GIP analogue (Section IIA,
Chapter 3) did not accelerate the development of obesity thereby the hypothesis built on
observations in vitro that GIP enhances TG accumulation in adipose tissue and can promote
obesity was not confirmed. Our study shows that the effect observed in GIPR KO mouse can
not be simply reversed by mimicking the opposite conditions, as we tried to achieve here by
applying stable GIP agonist and thereby over- stimulating GIP signaling. However, our study
in mice is in line with results of the clinical trials showing that GIP agonists lead to weight
loss [39]. We observed a decrease in LPL activity in mice injected with the GIP agonist
which can be one of the mechanisms responsible for the body weight loss. Moreover, this
study led to identification of novel candidate genes regulated by GIP in adipose tissue.
Between these novel GIP targets are genes known to be involved in lipid metabolism which
is in line with the known role of GIP in lipid metabolism. No target genes related to
carbohydrate metabolism could be detected, which was hypothesized based on our previous
findings that GIP was highly correlated with the intestinal glucose influx rate. The exact
functions of the novel GIP target genes in adipose tissue have to be elucidated in the future.
In Section IIB in Chapter 4 we aimed to identify a link between changes the adipose tissue
gene expression associated with altered clinical parameters indicative on IR such as high
BMI and WC, increased HOMA or decreased HDL serum level. Moreover, we wanted to
determine differences in the selected gene expression between subcutaneous adipose tissue
(SAT) and omentum. As expected we found that leptin expression in omentum had positive
correlation with BMI and WC, adiponectin expression level in SAT had negative correlation
with HOMA and positive correlation with HDL levels. In respect to the other energy
metabolism genes, we observed that expression of LPL, GLUT4, PPARγ, INSR, SREBP1,
GLUCR, and GIPR was positively associated with HDL and negatively associated with
HOMA, indicating that there is a link between early events in IR and the energy metabolism
gene expression in fat tissue. The pro- inflammatory genes showed positive correlation only
with WC, which is a measure indicative for body fat distribution however the exact
relationship between WC and IR is not well understood [6]. In summary, our study showed
Introduction
25
that in early phases of the development of IR metabolic and not proinflammatory gene
expression is altered in adipose tissue. These findings suggest that energy metabolism
alternations in adipose tissue, but not inflammatory once can be the pivotal events inducing
IR. Moreover, the relationship between adiponectin and metabolic genes expression and HDL
levels suggest that HDL can be involved in regulation of expression of these genes. Indeed, it
was recently shown that adiponectin gene expression level and adipocyte metabolism are
controlled by HDL levels [54]. Furthermore the differential gene expression in SAT and
omentum indicates on different physiological roles of these both fat depots.
In Section IIC in chapters 5 and 6 we investigated inflammatory challenge towards
adipose tissue and aimed to identify candidate biomarkers for inflamed/IR adipose tissue.
Originally, based on the data obtained from studies in rodents, it was proposed that in humans
resistin is an adipokine and can be used as a biomarker indicative for inflammation and IR.
However, our studies did not support these data (Chapter 5). We showed that although
resistin is expressed in human adipose tissue, it is significantly higher abundant in human
liver on both gene and protein levels. In addition, during LPS induced inflammation resistin
gene and protein levels in adipose and liver tissues were not affected while known
proinflammatory cytokines such as IL1β, IL-6, and TNFα were significantly upregulated.
These results suggest that resistin is not directly linked to inflammation, in a similar manner
to IL-6 or TNFα, and it is highly disputable if resistin can be used as a biomarker indicative
for inflammation and IR of adipose tissue in humans.
These results prompted us to search for other biomarkers indicative for inflammation in
adipose tissue as described in (Chapter 6).
The inflammatory insult of both human adipose and human liver tissues ex vivo showed that
during inflammation adipose tissue displays a more proinflammatory profile compared to
liver, as assessed by number of GO processes and number of genes involved in inflammatory
processes. These finding is in line with the hypothesis that adipose tissue is the major
proinflammatory organ during the development of IR/T2D. Furthermore the comparative
analysis led to identification of common pathways involved in inflammation/IR for both
tissues and differential ones indicating on common and differential mechanisms involved in
induction of inflammation and presumably IR. For example in liver we observed upregulation
of Jak-STAT and NFκB signaling. In adipose tissue we found upregulation of SOCS
signaling and downregulation of PPARγ signaling. These signaling pathways are known to be
Introduction
26
associated with the development of IR in vivo and support our inflammatory ex vivo model to
study processes related to IR.
The study of the inflammatory predicted secretomes of adipose and liver tissues, revealed a
similar phenomenon: the adipose tissue predicted secretome contained more genes encoding
for secreted proteins related to inflammation compared to the liver (399 vs. 236). The
predicted secretomes of both tissues contained common and differential genes, indicating the
presence of common and tissue specific biomarkers related to inflammation/IR. Within the
common predicted secretome we identified IL6, IL-1β, visfatin, CCL3, and examples of the
differential predicted secretomes were SELE, TNFα, CSF2, CSF3 in adipose tissue and
CXCL9, CXCL3, CSF1 in liver tissue. Furthermore, the predicted adipose tissue secretome
was compared with data obtained from quantitative proteomics technology approach-
CILAIR. The comparison of transcriptomics and proteomics data showed a very good
correlation and a subset of genes predicted to be secreted by adipose tissue were identified in
the adipose tissue culture as differentially affected by LPS. These proteins were regarded as
top candidate biomarkers indicative for adipose tissue inflammation/IR and were exampled
by matrix metalopeptidase-1(MMP-1), pentraxin related gene product (PTX3), fractalkine
(CX3CL1), and PAI 1. The identified common and differential tissue specific biomarkers
and signaling pathways suggest common and differential mechanisms involved in
inflammation/IR in both tissues. Therefore, it has to be verified in the future, if the identified
candidate biomarkers can be applied for recognition of IR in human liver and/or adipose
tissue and if it could facilitate a more targeted, tissue specific treatment of IR.
Introduction
27
Concluding remarks and future perspectives
(Section I) The application of human adipose tissue culture in combination with omics
technology is a powerful model to study adipose tissue biology in men and gives advantage
above monoculture of adipocytes or cell lines models of adipocytes such as murine 3T3 cells.
By application of omics technologies is it possible to study changes in a large number of
proteins or genes in an unbiased way and to screen for potential biomarkers or differentially
expressed genes between experimental groups. The proteomics technology and other omics
techniques aim to get comprehensive view of changes in biological processes. Hypothesis
generated by means of omics data in ex vivo experiments have to be verified in biological
systems in vivo by means of functional genomics or molecular biology techniques. The
potential candidate biomarkers have to be validated in patients before their application in
clinical practice. In the future, omics technologies together with the simultaneous
development of bioinformatics and modeling tools will enable to generate more accurate
hypotheses by increasing the power of prediction of the interactions in biological systems and
thereby the validation studies will become more explicit. Moreover, the tight collaborations
of scientists from different disciplines and laboratories (physiologists, molecular biologists,
geneticists, biochemists, bioinformaticians) will be necessary to interpret the data, deliver a
comprehensive view and deepen our understanding on complex metabolic diseases by a
Systems Biology approach [55].
(Section II A) The finding that application of stable GIP analogue (D-Ala2-GIP) can lead to
body mass reduction by decreased LPL activity is a novel finding and supports application of
stable GIP agonists in the treatment of obesity and T2D. These findings are in contradiction
to the in vitro studies, proposing GIP as pro-obesity and pro-diabetic agent, but in agreement
with recent clinical studies. The exact mechanism behind the stable GIP agonist actions in
vivo should be explored in more details and the functions of the novel GIP targets in adipose
tissue will deepen our understanding of GIP actions on adipose tissue in vivo.
(Section IIB) The study aiming to identify early mechanisms related to IR in humans,
suggested that alternation in metabolic gene expression preceeds changes in proinflammatory
genes expression in adipose tissues of non-diabetic women, thereby suggesting that
inflammation is not a pivotal event in the development of IR in this group of patients.
Whether this finding can be translated into a general mechanism of the development of
IR/T2D in man remains to be elucidated. The outcome of similar studies performed in non
diabetic overweight and obese Pima Indians [17], [56] showed that in cultured adipocytes and
Introduction
28
peradipocytes/stromal vascular cells of obese, healthy subjects both proinflammatory and
genes involved in lipid and carbohydrate metabolism were altered. However, it is difficult to
compare our studies and those regarding the Pima Indians, because (i) differences in the
experimental set-up (ex vivo adipose tissue vs. cultured adipose tissue cells), and (ii) the
patient groups differed significantly in genetic background, and many other biochemical and
anthropometric characteristics, for example the average BMI for Pima Indians was ~50, while
in our studies ~33. Moreover we studied much smaller subset of genes compared to the above
mentioned study where DNA microarrays were used. Therefore, changes in both metabolic
and proinflammatory genes expression could be characteristic for certain patient groups, or
be associated with high BMIs (~50). At present, there are limited data about gene expression
in adipose tissue of non-diabetic, overweight and obese patients, so it is difficult to speculate
which changes in gene expression occur primarily during the development of IR/T2D in men.
However, in comparable studies in animal models, it was found that during development of
IR in adipose tissue there were not the metabolic genes and neither the proinflammatory ones
that were affected at first. In a very recent study of Kleemann et al in 2010 [57] it was
described that primarily genes involved in “energy derivation by oxidation” were
downregulated [57] (in week 6 and sustained until week 12 of high fat diet). The acute phase
response genes were upregulated in week 6 of the high fat diet and maintained until week 12
however the upregulation of inflammatory response genes (upregulated cytokines) occurred
in week 9, and this response was further intensified in week 12. The genes involved in
carbohydrate and lipid metabolism remained unchanged during the 12 weeks of high fat diet.
These findings indicate then that primary deviations in adipose tissue involve changes related
to mitochondrial activity and acute phase response and are followed by changes in genes
involved in chronic inflammation. Therefore these data suggest, that mitochondrial genes are
the primary targets during the development of insulin resistance and the resulted oxidative
stress and ROS production will be the pivotal event leading to the development of IR. A
similar study is needed to test this hypothesis in humans.
(Section IIC) The novel finding described in this thesis, that resistin is a non- adipose tissue
specific adipokine, unrelated to inflammation, brought a novel light in the resistin research
field and requires further studies aiming to elucidate the role of resistin in liver physiology. In
studies investigating human resistin serum levels in relation to liver pathologies, it was shown
that patients with liver cirrhosis, had increased resistin serum levels which correlated with the
severity of disease [58]. Furthermore the serum resistin level was inversely correlated with
insulin sensitivity and positively correlated with markers of inflammation and with portal
Introduction
29
hypertension [59]. At present there are only few studies dedicated directly to role of resistin
in liver (patho)physiology. Zhou L. et al [60] showed that in adult human hepatocytes (L-02
cells) resistin overexpression impaired glucose tolerance and Shenk C.G.et al [61] found that
resistin was present in human hepatocytes and its overexpression led to insulin resistance.
And in a recent study described by Bertolani et al[62] resistin overexpression was observed
during chronic injury and induced proinflammatory actions. The above mentioned studies
synergistically point out towards role of resistin in liver IR and inflammation. Further
development of this research field is necessary to deepen our understanding of the role of
resistin in humans and its link to liver inflammation/IR in both liver and adipose tissue.
The new findings that adipose tissue and liver display common and differential molecular
response towards inflammatory challenge, imply that there are common and tissue specific
pathways involved in the induction of systemic IR. This phenomenon is further reflected by
the presence of the common and tissue specific biomarkers indicative for inflammation/IR.
These findings led to the development of a new hypothesis that it should be possible, based
on the presence of differential biomarkers in serum, to distinguish between inflammation/IR
in adipose tissue and the liver, thereby facilitating tissue targeted treatment of IR. Such an
approach would require extensive knowledge about the physiology of liver and adipose tissue
and the tissues specific alternations during the development of IR. This subject has to be
explored further in both animal models of obesity and in humans.
To support our hypothesis, we refer to a recently described study preformed by Kleeman et al
58 [57]. During 12-weeks long high fat feeding, the gene expression in the liver, adipose
tissue, and muscles displayed distinct patterns of changes. For example, in the liver genes
involved in mitochondrion function, energy derivation by oxidation, lipid and carbohydrate
metabolic processes were upregulated from week 9 while in adipose tissue and muscles these
processes were downregulated at the same time. Interestingly, processes such as defense
response, inflammatory response, immune response, and acute inflammatory response were
downregulated in liver and muscles while in adipose tissue the same processes were
upregulated. These findings indicate that during the development of IR each tissue undergoes
specific changes which are related to its specific functions. Thereby synergistic however
distinct actions of different organs might result in the development of systemic IR.
Furthermore it was shown that by both pharmacological interventions (i.e. thiazolidinediones
(TZDs) and life style modification (i.e. exercise training) the whole body insulin sensitivity
can be improved [63]. Moreover, these different types of interventions and different drugs
can affect metabolism and gene expression in a tissue in a specific manner. For example it
Introduction
30
was shown that PPAR gamma agonists improve insulin sensitivity by preventing toxic
accumulation of lipids in skeletal muscle and by reversing hepatic steatosis [64; 65], while
exercise training induces several adaptations that may promote glucose uptake and fatty acid
oxidation, including mitochondrial biogenesis, improved insulin signal transduction, and
elevated GLUT4 protein content [66]. In another study [67] investigating the effects of
rosiglitazone, or exercise training , or both on lipid and glucose metabolism in high fat fed
rats showed that exercise training improved insulin stimulated glucose uptake and increased
rates of fatty acid oxidation in skeletal muscle. In contrast, rosiglitazone treatment increased
lipid accumulation and decreased insulin –stimulated glucose uptake in skeletal muscle.
However, in adipose tissue the same treatment increased GLUT4 and acetyl CoA expression.
The combination of both exercise training and rosiglitazone treatment decreased liver TG
content. The above mentioned data show that although both interventions can improve the
whole body insulin sensitivity, they produce divergent effects on protein expression and
triglyceride content in different tissues. In case patients might have a predisposition to the
development of insulin resistance in a certain tissue it would be very attractive to apply tissue
specific/targeted- treatment. Therefore, in future studies aiming to examine the effects of
antidiabetic drugs, or other types of therapies, with a potential to improve insulin sensitivity,
it would be beneficial to investigate their effects in respect to tissue-specific actions in order
to provide tissue specific treatment.
Introduction
31
Summary
From the classical point of view adipose tissue is known for energy storage in the form of
triglycerides. However, during the last 15 years adipose tissue gained a lot of interest due to
its endocrine activity. Nowadays, adipose tissue is commonly accepted as an endocrine
organ secreting numerous hormonal factors called adipokines. These adipokines are involved
in divergent biological processes such as inflammation (IL-6, TNFα, IL-1β), energy
metabolism (adiponectin, leptin), reproduction (leptin) and many others. In obesity and
associated with it low grade systemic inflammation, the adipokine secretory profile changes,
which might result in deregulation of the metabolism of the adipose tissue itself and
eventually lead to systemic insulin resistance. However, the exact role of the adipose tissue in
the development of insulin resistance during these conditions is not completely known,
therefore in this dissertation our aims were: ( 1) to study the role of adipose tissue in the
development of systemic insulin resistance in relation to (A) nutritional overstimulation (GIP
signaling ), (B) clinical parameters involved in obesity and inflammation, (C) exogenous
inflammatory triggers and (2) to identify biomarkers specific for inflammation/insulin
resistance in adipose tissue by means of omics technologies such as various proteomics
techniques and DNA microarrays.
Glucose dependent insulinotropic polypeptide (GIP) was proposed as a link between
overnutrition and insulin resistance (IR), due to the fact that in several in vitro studies it was
shown to stimulate TG accumulation in the adipose tissue thereby promoting development of
obesity and insulin resistance. Additionally, it was shown that GIP receptor knockout mice
were protected from obesity on high fat diet. Therefore, in order to test the hypothesis that
access of GIP might accelerate development of obesity /IR, we performed experiments where
mice were injected with a GIP analogue during high fat or chow diets and monitored several
serum biochemical parameters and expression of subsets of proinflammatory and energy
metabolism genes in adipose tissue. Additionally, in order to identify GIP target genes in the
adipose tissue we performed DNA-microarray to screen for adipose tissue GIP target genes.
Results obtained from these studies did not confirm that excess of GIP leads directly to the
accelerated development of obesity or IR, thereby excluding GIP as a direct link between
overnutrition and IR in vivo. However, we identified several new GIP target genes in adipose
tissue: Apo –gene family members and other genes involved in lipid metabolism, and genes
with as yet unknown functions in the adipose tissue.
Introduction
32
In order to answer the question if inflammation in adipose tissue is a cause or consequence of
insulin resistance in humans, we studied gene expression of selected proinflammatory and
metabolic genes in adipose tissue in non diabetic women and their relation to several clinical
parameters indicative for early IR.
In our study group we found that the tested metabolic genes had altered expression associated
with parameters of obesity and insulin resistance. However, we did not find such correlations
for a subset of proinflammatory genes. These findings suggest that metabolic alternations in
adipose tissue precede the inflammation, thereby excluding inflammation as the pivotal event
leading to IR, at least in this particular patient group. Recent literature data [58] indicate that
dysfunction of mitochondria and the resulting overproduction of ROS could be the key events
initiating insulin resistance.
Despite the fact that there are several candidate biomarkers for systemic insulin resistance
their application in clinical practice as a supporting tool for early detection and its organ
specific origin is still futuristic. In this dissertation we aimed to validate if resistin is indeed a
good candidate biomarker (over)produced by the adipose tissue during inflammation/insulin
resistance. Our ex vivo studies did not confirm that resistin is induced by inflammation
(evoked by LPS), thereby excluding it as a biomarker indicative for inflammation / insulin
resistance. Moreover, we found that the human liver is an abundant source of resistin on both
gene and protein levels, thereby opening new avenues for the investigations devoted to the
role of resistin in the liver metabolism and its possible link to IR.
In order to find novel biomarkers and pathways indicative for inflammation/IR in adipose
tissue we studied in detail the changes in the adipose tissue secretome during inflammation
and compared the inflammatory adipose tissue secretome with the inflammatory liver
secretome. Our study led to the identification of differential pathways and biomarkers,
revealed by transcriptomic and proteomic approaches. The presence of these differential
biomarkers and pathways suggests tissue specific changes in response to
inflammation/insulin resistance, which could be applied for tissue specific detection and
treatment of IR. The adipose tissue specific biomarkers were represented by fractalkine,
tumor necrosis factor, pentraxin-related protein or interstitial collagenase (matrix
metallopeptidase 1) and the liver tissue specific biomarkers were for example chemokine (C-
X-C motif) ligand 9, chemokine (C-X-C motif) ligand 3, or follistatin-like 3 (secreted
glycoprotein).
In conclusion, in a search for the major players in insulin resistance we found that: (excess)
of GIP does not serve as a link between obesity and insulin resistance. Seeking for the
Introduction
33
primary changes in adipose tissue gene expression in the early stages of the development of
insulin resistance we found, that metabolic genes had altered expression in patients with
increased HOMA and decreased HDL serum levels while the proinflammatory genes were
unaffected in these patients. These findings suggest therefore, that metabolic alternations
might precede inflammatory ones in the early development of insulin resistance, and exclude
inflammation as a cause of IR in humans, but accommodate it as a consequence of IR.
Our finding that during inflammation adipose tissue displays a unique pattern of gene/protein
expression compared to the liver, suggests that the adipose tissue specific proteins could be
used as biomarkers to detect (adipose) tissue specific IR. Further investigations and
validation studies should explore the possibilities for the development of novel tissue specific
diagnosis of IR and thereby more targeted strategies for its treatment.
Introduction
34
Samenvatting
Van oudsher is vetweefsel bekend als een opslag plaats voor energie in de vorm van
triglyceriden. Gedurende de laatste 15 jaar is echter de interesse in vetweefsel verhoogd
doordat dit weefsel ook hormonen in de bloedbaan uitscheidt.Hierdoor wordt vetweefsel
nu gezien als een endocrien orgaan dat talloze hormonale factoren (adipokines) uitscheidt.
Adipokines zijn betrokken bij diverse biologische processen, zoals ontsteking (IL-6, TNFα,
IL-1β), energie huishouding (adiponectin, leptin), voortplanting (leptin). In vetzucht en
daaraan gekoppelde ontstekingen verandert het adipokine uitscheidings profiel waardoor het
metabolisme van het vetweefsel ontregeld kan worden. Dit zou tevens kunnen leiden tot de
ontwikkeling van systemische insuline resistentie. De exacte rol van vetweefsel in de
ontwikkeling van systemische insuline resistentie is echter niet duidelijk.
De onderzoeksdoelen gepresenteerd in deze dissertatie zijn:
(1) het onderzoeken van de rol van vetweefsel in de ontwikkeling van systemische insuline
resistentie in relatie tot (A) overvoeding (overstimulatie van GIP transmissie ketens), (B)
clinische parameters gerelateerd aan vetzucht en ontstekingsparameters, (C) LPS
geinduceeerde ontsteking en (2) het identificeren van biologische markers (indicatoren voor
een bepaalde conditie) die specifiek zijn voor ontstekingen / insuline resistentie in vetweefsel.
Deze indicatoren zijn bepaald door toepassing van zogenaamde omics technologieën
(moleculair biologische technieken die vele meetpunten opleveren) zoals diverse proteomics
technieken (bepalen eiwit profielen) en DNA microarrays (bepalen gen expressie).
Glucose afhankelijke insulinotropic poly peptide (GIP) is in wetenschappelijke studies
voorgesteld als een link tussen over-voeding en insuline resistentie. In diverse in vitro studies
is beschreven dat GIP de triglyceride ophoping in vetweefsel stimuleert, en daarmee vetzucht
en insuline resistentie (IR) bevordert. Tevens is aangetoond dat muizen waarbij de GIP
receptor (een specifieke bindings plaats voor GIP) was weggehaald, door een zogenaamd
knock-out experiment, beschermd waren tegen vetzucht indien ze een dieet hadden met een
hoog vet gehalte. Om de hypothese te testen dat toediening van GIP de ontwikkeling van
vetzucht / IR versneld, hebben we experimenten gedaan waarbij muizen zijn geïnjecteerd met
een GIP-achtige stof (GIP analoog) gedurende een controle dieet of een dieet met een hoog
vet gehalte. Vervolgens zijn diverse biochemische parameters in het serum bepaald alsmede
de expressie van een deel verzameling van genen betrokken bij energie metabolisme en
genen betrokken bij ontsteking in vet weefsel. Tevens is een DNA microarray analyse gedaan
om GIP doel-genen in vetweefsel te identificeren.
Introduction
35
De resultaten van bovengenoemde studies hebben niet bevestigd dat een overmaat aan GIP
leidt tot de versnelde ontwikkeling van vetzucht of IR. Hiermee is ook GIP als directe schakel
tussen overvoeding en IR in vivo niet aangetoond. We hebben echter wel een aantal nieuwe
GIP doel genen in vetweefsel geidentificeerd. Deze doel genen bestaan onder andere uit (i)
een aantal van de Apo-genen familie, (ii) genen betrokken bij metabolisme van vetten, en (iii)
genen met tot nu toe onbekende functie in vetweefsel.
Om de vraag te beantwoorden of in de mens een ontsteking in vetweefsel een oorzaak dan
wel een gevolg is van IR, hebben we de activiteit bepaald van een aantal genen betrokken bij
ontsteking en metabolisme in vetweefsel van niet- diabetische vrouwen en de relatie tussen
gen activiteit en clinische parameters van vroege IR geanalyseerd.
In de patiënten groep die we hebben bestudeerd, is een relatie gevonden tussen de veranderde
activiteit van metabole genen met parameters voor vetzucht en IR. We hebben echter geen
relatie kunnen vinden voor een selectie van genen betrokken bij ontsteking. Hieruit
concluderen we dat metabole veranderingen in vetweefsel voorafgaan aan een ontsteking, en
tevens dat ontsteking niet de initierende factor is in de IR ontwikkeling, in ieder geval in deze
patiënten groep. Recente literatuur [57] suggereert dat de disfunctie van mitochondrien en de
resulterende overproductie van ROS de belangrijkste factoren kunnen zijn bij de aanvang van
IR.
Ondanks het feit dat er diverse kandidaat biomarkers zijn voor systemische IR, is de klinische
toepassing hiervan als ondersteuning van vroege waarneming van (orgaan specifieke) IR iets
voor de toekomst. In deze dissertatie wilden we valideren of resistin een goede kandidaat
biomarker is die wordt (over) geproduceerd in vetweefsel gedurende ontsteking en IR.
In onze ex vivo studies hebben we niet aangetoond dat resistin wordt verhoogd door
ontsteking (door LPS geinduceerd), waardoor het waarschijnlijk niet een biomarker is voor
ontsteking / IR. Bovendien hebben we gevonden dat de menselijke lever een overvloedige
bron van resistin is zowel wat betreft gen activiteit als op eiwit niveaus. Dit opent nieuwe
mogelijkheden voor onderzoek naar de rol van resistin in lever metabolisme in relatie met
IR.
Vervolgens hebben we geprobeerd om nieuwe biomarkers en of metabole / regulatoire
cellulaire wegen te vinden die indicatief zijn voor weefsel-specifieke ontsteking / IR in
vetweefsel. Hiervoor hebben we de LPS geïnduceerde veranderingen vergeleken tussen de
door vetweefsel uitgescheiden eiwitten en lever uitgescheiden eiwitten (secretoom). Deze
studie heeft geleid tot de identificatie van diverse metabole / regulatoire cellulaire paden
waarvan de expressie verschilt tussen het vetweefsel en de lever. Tevens zijn vergeleken de
Introduction
36
biomarkers, afgeleid van het transcriptoom (bepalen gen activiteiten) en biomarkers afgeleid
van het proteoom (bepalen eiwit hoeveelheden) . De aanwezigheid van deze specifieke
biomarkers en cellulaire paden geeft indicatie dat weefsel -specifieke veranderingen
plaatsvinden bij ontsteking / IR. Deze zouden kunnen worden toegepast voor weefsel-
specifieke detectie en behandeling van IR. De vetweefsel-specifieke biomarkers bestonden
onder andere uit: fractalkine, tumor necrosis factor, pentraxin-gerelateerd eiwit en interstitial
collagenase (matrix metallopeptidase 1). Lever specifieke biomarkers waren onder andere:
chemokine (C-X-C motif) ligand 9, chemokine (C-X-C motif) ligand 3, en follistatin-like 3
(secreted glycoprotein).
Concluderend hebben we in een zoektocht naar de grootste spelers in IR gevonden dat een
(overmaat) aan GIP niet leidt tot een relatie tussen vetzucht en IR. Echter, GIP heeft een
effect op vetweefsel doordat het de activiteit veranderd van diverse genen betrokken bij vet
metabolisme, alsmede van genen die een nog onbekende functie hebben in het vetweefsel
metabolisme.
In de zoektocht naar de veranderingen in genen activiteit van vetweefsel in de vroege stadia
van ontwikkeling van IR hebben we gevonden dat metabole genen een veranderde activiteit
hadden in patiënten met verhoogd HOMA en verlaagde serum HDL spiegels, terwijl de
ontstekings gerelateerde genen een onveranderde expressie hadden in die situatie in deze
patiënten. Deze bevindingen suggereren dat metabole veranderingen de ontstekings
indicatoren voorafgaan, waardoor ontsteking als oorzaak voor IR in mensen minder voor de
hand liggend zou zijn, hoewel ontsteking wel het gevolg van IR zou kunnen zijn.
Onze bevinding dat vetweefsel een uniek patroon van genen activiteiten / eiwit hoeveelheden
laat zien ten opzichte van lever weefsel suggereert dat vetweefsel specifieke eiwitten zouden
kunnen dienen als biomarkers voor (vet) weefsel specifieke IR. Vervolg (validatie) studies
kunnen de mogelijkheden voor de ontwikkeling van nieuwe weefsel specifieke diagnose van
IR in kaart brengen en daarmee meer doelgerichte strategieen voor behandeling.
Introduction
37
References
[1] B.M.Spiegelman, J.S.Flier, Obesity and the regulation of energy balance Cell 104, (2001) 531-543. [2] V.G.Athyros, K.Tziomalos, A.Karagiannis, D.P.Mikhailidis, Preventing Type 2 Diabetes Mellitus:
Room for Residual Risk Reduction after Lifestyle Changes? Curr.Pharm.Des(2010). [3] R.Muniyappa, S.Lee, H.Chen, M.J.Quon, Current approaches for assessing insulin sensitivity and
resistance in vivo: advantages, limitations, and appropriate usage Am.J.Physiol Endocrinol.Metab 294, (2008) E15-E26.
[4] T.M.Wallace, J.C.Levy, D.R.Matthews, Use and abuse of HOMA modeling Diabetes Care 27, (2004) 1487-1495.
[5] T.Fulop, D.Tessier, A.Carpentier, The metabolic syndrome Pathol.Biol.(Paris) 54, (2006) 375-386. [6] S.Klein, D.B.Allison, S.B.Heymsfield, D.E.Kelley, R.L.Leibel, C.Nonas, R.Kahn, Waist
Circumference and Cardiometabolic Risk: a Consensus Statement from Shaping America's Health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association Obesity.(Silver.Spring) 15, (2007) 1061-1067.
[7] S.Galic, J.S.Oakhill, G.R.Steinberg, Adipose tissue as an endocrine organ Mol.Cell Endocrinol. 316, (2010) 129-139.
[8] C.Buechler, J.Wanninger, M.Neumeier, Adiponectin receptor binding proteins--recent advances in elucidating adiponectin signalling pathways FEBS Lett. 584, (2010) 4280-4286.
[9] Y.Matsuzawa, Adiponectin: a key player in obesity related disorders Curr.Pharm.Des 16, (2010) 1896-1901.
[10] K.Rabe, M.Lehrke, K.G.Parhofer, U.C.Broedl, Adipokines and insulin resistance Mol.Med. 14, (2008) 741-751.
[11] A.Schaffler, J.Scholmerich, Innate immunity and adipose tissue biology Trends Immunol. 31, (2010) 228-235.
[12] G.S.Hotamisligil, Inflammation and metabolic disorders Nature 444, (2006) 860-867. [13] R.Breitling, Robust signaling networks of the adipose secretome Trends Endocrinol.Metab 20, (2009)
1-7. [14] V.Emilsson, G.Thorleifsson, B.Zhang, A.S.Leonardson, F.Zink, J.Zhu, S.Carlson, A.Helgason,
G.B.Walters, S.Gunnarsdottir, M.Mouy, V.Steinthorsdottir, G.H.Eiriksdottir, G.Bjornsdottir, I.Reynisdottir, D.Gudbjartsson, A.Helgadottir, A.Jonasdottir, A.Jonasdottir, U.Styrkarsdottir, S.Gretarsdottir, K.P.Magnusson, H.Stefansson, R.Fossdal, K.Kristjansson, H.G.Gislason, T.Stefansson, B.G.Leifsson, U.Thorsteinsdottir, J.R.Lamb, J.R.Gulcher, M.L.Reitman, A.Kong, E.E.Schadt, K.Stefansson, Genetics of gene expression and its effect on disease Nature 452, (2008) 423-428.
[15] K.Clement, D.Langin, Regulation of inflammation-related genes in human adipose tissue J.Intern.Med. 262, (2007) 422-430.
[16] M.Dolinkova, I.Dostalova, Z.Lacinova, D.Michalsky, D.Haluzikova, M.Mraz, M.Kasalicky, M.Haluzik, The endocrine profile of subcutaneous and visceral adipose tissue of obese patients Mol.Cell Endocrinol. 291, (2008) 63-70.
[17] Y.H.Lee, S.Nair, E.Rousseau, D.B.Allison, G.P.Page, P.A.Tataranni, C.Bogardus, P.A.Permana, Microarray profiling of isolated abdominal subcutaneous adipocytes from obese vs non-obese Pima Indians: increased expression of inflammation-related genes Diabetologia 48, (2005) 1776-1783.
[18] O.Poulain-Godefroy, C.Lecoeur, F.Pattou, G.Fruhbeck, P.Froguel, Inflammation is associated with a decrease of lipogenic factors in omental fat in women Am.J.Physiol Regul.Integr.Comp Physiol 295, (2008) R1-R7.
[19] M.E.Trujillo, P.E.Scherer, Adipose tissue-derived factors: impact on health and disease Endocr.Rev. 27, (2006) 762-778.
[20] H.Shi, M.V.Kokoeva, K.Inouye, I.Tzameli, H.Yin, J.S.Flier, TLR4 links innate immunity and fatty acid-induced insulin resistance J.Clin.Invest 116, (2006) 3015-3025.
[21] K.Miyawaki, Y.Yamada, N.Ban, Y.Ihara, K.Tsukiyama, H.Zhou, S.Fujimoto, A.Oku, K.Tsuda, S.Toyokuni, H.Hiai, W.Mizunoya, T.Fushiki, J.J.Holst, M.Makino, A.Tashita, Y.Kobara, Y.Tsubamoto, T.Jinnouchi, T.Jomori, Y.Seino, Inhibition of gastric inhibitory polypeptide signaling prevents obesity Nat.Med. 8, (2002) 738-742.
[22] A.Bloch-Damti, N.Bashan, Proposed mechanisms for the induction of insulin resistance by oxidative stress Antioxid.Redox.Signal. 7, (2005) 1553-1567.
Introduction
38
[23] L.E.Fridlyand, L.H.Philipson, Reactive species and early manifestation of insulin resistance in type 2 diabetes Diabetes Obes.Metab 8, (2006) 136-145.
[24] N.N.Mehta, F.C.McGillicuddy, P.D.Anderson, C.C.Hinkle, R.Shah, L.Pruscino, J.Tabita-Martinez, K.F.Sellers, M.R.Rickels, M.P.Reilly, Experimental Endotoxemia Induces Adipose Inflammation and Insulin Resistance in Humans Diabetes(2009).
[25] R.Shah, Y.Lu, C.C.Hinkle, F.C.McGillicuddy, R.Kim, S.Hannenhalli, T.P.Cappola, S.Heffron, X.Wang, N.N.Mehta, M.Putt, M.P.Reilly, Gene profiling of human adipose tissue during evoked inflammation in vivo Diabetes 58, (2009) 2211-2219.
[26] A.Virkamaki, I.Puhakainen, V.A.Koivisto, H.Vuorinen-Markkola, H.Yki-Jarvinen, Mechanisms of hepatic and peripheral insulin resistance during acute infections in humans J.Clin.Endocrinol.Metab 74, (1992) 673-679.
[27] A.Virkamaki, H.Yki-Jarvinen, Mechanisms of insulin resistance during acute endotoxemia Endocrinology 134, (1994) 2072-2078.
[28] S.P.Weisberg, D.McCann, M.Desai, M.Rosenbaum, R.L.Leibel, A.W.Ferrante, Jr., Obesity is associated with macrophage accumulation in adipose tissue J.Clin.Invest 112, (2003) 1796-1808.
[29] C.N.Lumeng, I.Maillard, A.R.Saltiel, T-ing up inflammation in fat Nat.Med. 15, (2009) 846-847. [30] S.Nishimura, I.Manabe, M.Nagasaki, K.Eto, H.Yamashita, M.Ohsugi, M.Otsu, K.Hara, K.Ueki,
S.Sugiura, K.Yoshimura, T.Kadowaki, R.Nagai, CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity Nat.Med. 15, (2009) 914-920.
[31] P.R.Flatt, Dorothy Hodgkin Lecture 2008. Gastric inhibitory polypeptide (GIP) revisited: a new therapeutic target for obesity-diabetes? Diabet.Med. 25, (2008) 759-764.
[32] R.E.Wachters-Hagedoorn, M.G.Priebe, J.A.Heimweg, A.M.Heiner, K.N.Englyst, J.J.Holst, F.Stellaard, R.J.Vonk, The rate of intestinal glucose absorption is correlated with plasma glucose-dependent insulinotropic polypeptide concentrations in healthy men J.Nutr. 136, (2006) 1511-1516.
[33] V.Marks, Human obesity: its hormonal basis and the role of gastric inhibitory polypeptide Med.Princ.Pract. 15, (2006) 325-337.
[34] V.A.Gault, N.Irwin, B.D.Green, J.T.McCluskey, B.Greer, C.J.Bailey, P.Harriott, F.P.O'Harte, P.R.Flatt, Chemical ablation of gastric inhibitory polypeptide receptor action by daily (Pro3)GIP administration improves glucose tolerance and ameliorates insulin resistance and abnormalities of islet structure in obesity-related diabetes Diabetes 54, (2005) 2436-2446.
[35] V.A.Gault, P.L.McClean, R.S.Cassidy, N.Irwin, P.R.Flatt, Chemical gastric inhibitory polypeptide receptor antagonism protects against obesity, insulin resistance, glucose intolerance and associated disturbances in mice fed high-fat and cafeteria diets Diabetologia 50, (2007) 1752-1762.
[36] P.L.McClean, N.Irwin, R.S.Cassidy, J.J.Holst, V.A.Gault, P.R.Flatt, GIP receptor antagonism reverses obesity, insulin resistance, and associated metabolic disturbances induced in mice by prolonged consumption of high-fat diet Am.J.Physiol Endocrinol.Metab 293, (2007) E1746-E1755.
[37] J.Kozawa, K.Okita, A.Imagawa, H.Iwahashi, J.J.Holst, K.Yamagata, I.Shimomura, Similar incretin secretion in obese and non-obese Japanese subjects with type 2 diabetes Biochem.Biophys.Res.Commun. 393, (2010) 410-413.
[38] I.R.Jones, D.R.Owens, S.D.Luzio, T.M.Hayes, Obesity is associated with increased post-prandial GIP levels which are not reduced by dietary restriction and weight loss Diabete Metab 15, (1989) 11-22.
[39] V.A.Fonseca, B.Zinman, M.A.Nauck, A.B.Goldfine, J.Plutzky, Confronting the type 2 diabetes epidemic: the emerging role of incretin-based therapies Am.J.Med. 123, (2010) S2-S10.
[40] H.Xu, G.T.Barnes, Q.Yang, G.Tan, D.Yang, C.J.Chou, J.Sole, A.Nichols, J.S.Ross, L.A.Tartaglia, H.Chen, Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance J.Clin.Invest 112, (2003) 1821-1830.
[41] R.B.Goldberg, Cytokine and cytokine-like inflammation markers, endothelial dysfunction, and imbalanced coagulation in development of diabetes and its complications J.Clin.Endocrinol.Metab 94, (2009) 3171-3182.
[42] S.Y.Park, Y.R.Cho, H.J.Kim, T.Higashimori, C.Danton, M.K.Lee, A.Dey, B.Rothermel, Y.B.Kim, A.Kalinowski, K.S.Russell, J.K.Kim, Unraveling the temporal pattern of diet-induced insulin resistance in individual organs and cardiac dysfunction in C57BL/6 mice Diabetes 54, (2005) 3530-3540.
Introduction
39
[43] A.O.Agwunobi, C.Reid, P.Maycock, R.A.Little, G.L.Carlson, Insulin resistance and substrate utilization in human endotoxemia J.Clin.Endocrinol.Metab 85, (2000) 3770-3778.
[44] P.D.Cani, J.Amar, M.A.Iglesias, M.Poggi, C.Knauf, D.Bastelica, A.M.Neyrinck, F.Fava, K.M.Tuohy, C.Chabo, A.Waget, E.Delmee, B.Cousin, T.Sulpice, B.Chamontin, J.Ferrieres, J.F.Tanti, G.R.Gibson, L.Casteilla, N.M.Delzenne, M.C.Alessi, R.Burcelin, Metabolic endotoxemia initiates obesity and insulin resistance Diabetes 56, (2007) 1761-1772.
[45] H.Sugita, M.Kaneki, E.Tokunaga, M.Sugita, C.Koike, S.Yasuhara, R.G.Tompkins, J.A.Martyn, Inducible nitric oxide synthase plays a role in LPS-induced hyperglycemia and insulin resistance Am.J.Physiol Endocrinol.Metab 282, (2002) E386-E394.
[46] E.M.Scott, A.M.Carter, J.B.Findlay, The application of proteomics to diabetes Diab.Vasc.Dis.Res. 2, (2005) 54-60.
[47] H.Roelofsen, M.Dijkstra, D.Weening, M.P.de Vries, A.Hoek, R.J.Vonk, Comparison of isotope-labeled amino acid incorporation rates (CILAIR) provides a quantitative method to study tissue secretomes Mol.Cell Proteomics. 8, (2009) 316-324.
[48] J.D.Bendtsen, L.J.Jensen, N.Blom, H.G.Von, S.Brunak, Feature-based prediction of non-classical and leaderless protein secretion Protein Eng Des Sel 17, (2004) 349-356.
[49] G.Sun, Application of DNA microarrays in the study of human obesity and type 2 diabetes OMICS. 11, (2007) 25-40.
[50] G.Dennis, Jr., B.T.Sherman, D.A.Hosack, J.Yang, W.Gao, H.C.Lane, R.A.Lempicki, DAVID: Database for Annotation, Visualization, and Integrated Discovery Genome Biol. 4, (2003) 3.
[51] d.W.Huang, B.T.Sherman, R.A.Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Nat.Protoc. 4, (2009) 44-57.
[52] J.L.Sebedio, E.Pujos-Guillot, M.Ferrara, Metabolomics in evaluation of glucose disorders Curr.Opin.Clin.Nutr.Metab Care 12, (2009) 412-418.
[53] P.E.Scherer, Adipose tissue: from lipid storage compartment to endocrine organ Diabetes 55, (2006) 1537-1545.
[54] L.S.Van Linthout, A.Foryst-Ludwig, F.Spillmann, J.Peng, Y.Feng, M.Meloni, C.E.Van, U.Kintscher, H.P.Schultheiss, G.B.De, C.Tschope, Impact of HDL on adipose tissue metabolism and adiponectin expression Atherosclerosis 210, (2010) 438-444.
[55] E.E.Schadt, S.H.Friend, D.A.Shaywitz, A network view of disease and compound screening Nat.Rev.Drug Discov. 8, (2009) 286-295.
[56] S.Nair, Y.H.Lee, E.Rousseau, M.Cam, P.A.Tataranni, L.J.Baier, C.Bogardus, P.A.Permana, Increased expression of inflammation-related genes in cultured preadipocytes/stromal vascular cells from obese compared with non-obese Pima Indians Diabetologia 48, (2005) 1784-1788.
[57] R.Kleemann, E.M.van, L.Verschuren, A.M.van den Hoek, M.Koek, P.Y.Wielinga, A.Jie, L.Pellis, I.Bobeldijk-Pastorova, T.Kelder, K.Toet, S.Wopereis, N.Cnubben, C.Evelo, O.B.van, T.Kooistra, Time-resolved and tissue-specific systems analysis of the pathogenesis of insulin resistance PLoS.One. 5, (2010) e8817.
[58] S.Kakizaki, N.Sohara, Y.Yamazaki, N.Horiguchi, D.Kanda, K.Kabeya, K.Katakai, K.Sato, H.Takagi, M.Mori, Elevated plasma resistin concentrations in patients with liver cirrhosis J.Gastroenterol.Hepatol. 23, (2008) 73-77.
[59] E.Yagmur, C.Trautwein, A.M.Gressner, F.Tacke, Resistin serum levels are associated with insulin resistance, disease severity, clinical complications, and prognosis in patients with chronic liver diseases Am.J.Gastroenterol. 101, (2006) 1244-1252.
[60] L.Zhou, Y.Li, T.Xia, S.Feng, X.Chen, Z.Yang, Resistin overexpression impaired glucose tolerance in hepatocytes Eur.Cytokine Netw. 17, (2006) 189-195.
[61] C.H.Sheng, J.Di, Y.Jin, Y.C.Zhang, M.Wu, Y.Sun, G.Z.Zhang, Resistin is expressed in human hepatocytes and induces insulin resistance Endocrine. 33, (2008) 135-143.
[62] C.Bertolani, P.Sancho-Bru, P.Failli, R.Bataller, S.Aleffi, R.DeFranco, B.Mazzinghi, P.Romagnani, S.Milani, P.Gines, J.Colmenero, M.Parola, S.Gelmini, R.Tarquini, G.Laffi, M.Pinzani, F.Marra, Resistin as an intrahepatic cytokine: overexpression during chronic injury and induction of proinflammatory actions in hepatic stellate cells Am.J.Pathol. 169, (2006) 2042-2053.
[63] A.L.Hevener, D.Reichart, J.Olefsky, Exercise and thiazolidinedione therapy normalize insulin action in the obese Zucker fatty rat Diabetes 49, (2000) 2154-2159.
[64] B.M.Jucker, T.R.Schaeffer, R.E.Haimbach, M.E.Mayer, D.H.Ohlstein, S.A.Smith, A.R.Cobitz, S.K.Sarkar, Reduction of intramyocellular lipid following short-term rosiglitazone treatment in Zucker fatty rats: an in vivo nuclear magnetic resonance study Metabolism 52, (2003) 218-225.
Introduction
40
[65] P.D.Hockings, K.K.Changani, N.Saeed, D.G.Reid, J.Birmingham, P.O'Brien, J.Osborne, C.N.Toseland, R.E.Buckingham, Rapid reversal of hepatic steatosis, and reduction of muscle triglyceride, by rosiglitazone: MRI/S studies in Zucker fatty rats Diabetes Obes.Metab 5, (2003) 234-243.
[66] C.R.Bruce, J.A.Hawley, Improvements in insulin resistance with aerobic exercise training: a lipocentric approach Med.Sci.Sports Exerc. 36, (2004) 1196-1201.
[67] S.J.Lessard, D.A.Rivas, Z.P.Chen, A.Bonen, M.A.Febbraio, D.W.Reeder, B.E.Kemp, B.B.Yaspelkis, III, J.A.Hawley, Tissue-specific effects of rosiglitazone and exercise in the treatment of lipid-induced insulin resistance Diabetes 56, (2007) 1856-1864.
Chapter 1
41
Chapter 1
42
Section I
Optimization and development of proteomics
technologies
Chapter 1
43
Chapter 1
Fractional factorial design for optimisation of
the SELDI protocol for human adipose tissue
culture media
Ewa Szalowska
Sacha A.F.T. van Hijum
Han Roelofsen
Annemieke Hoek
Roel J.Vonk
Gerard J. te Meerman
Biotechnology Progress 2006
Chapter 1
44
Abstract
The early factors inducing insulin resistance are not known. Therefore, we are interested to
study the secretome of the human visceral adipose tissue as a potential source of unknown
peptides and proteins inducing insulin resistance.
Surface - enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass
spectrometry is a high throughput proteomics technology to generate peptide and protein
profiles (MS spectra). To obtain good quality and reproducible data from SELDI-TOF many
factors in the sample pretreatment and SELDI protocol should be optimized.
In order to identify the optimal combination of factors resulting in the best and the most
reproducible spectra we designed an experiment where factors were varied systematically
according to a fractional factorial design. In this study 7 protein chip preparation protocol
factors were tested in 32 experiments. The main effects of these factors and their interactions
contributing to the best quality spectra were identified by ANOVA. To assess the
reproducibility in a subsequent experiment the 8 protocols generating the highest quality
spectra were applied to samples in quadruplicates on different chips.
This approach resulted in the development of an improved chip protocol, yielding higher
quality peaks, and more reproducible spectra.
Introduction
Diabetes mellitus type 2 is becoming globally one of the major health problems in next
decades (1). Since obesity is recognized as one of the major risk factors in the development
of insulin resistance and eventually metabolic diseases such as diabetes (2,3), it became
important to investigate the endocrine functions of adipose tissue (2,4). Adipose tissue
consists of many cell types (adipocytes, endothelial cells, macrophages, connective tissue
cells) and secretes numerous peptides and proteins (adipokines). These adipokines affect
metabolic processes such as glucose uptake, lipolysis, lipogenesis of different tissues and
organs such as liver, muscle, and adipose tissue itself. Deregulation of adipokines secretion
may lead to metabolic alternations and eventually metabolic diseases (2,4). In future we aim
to analyse media derived from the human adipose tissue culture and compare protein profiles
Chapter 1
45
obtained from both healthy lean and obese people. Identified differences in the protein
profiles could lead to the discovery of relevant proteins related to obesity and possibly
involved in the development of insulin resistance [5,6].
In our studies we aim to find an optimal protocol for medium derived from human adipose
tissue culture and apply it in SELDI-TOF-MS (Surface- Enhanced Laser Desorption
Ionization Time-of-Flight Mass Spectrometry) technology developed by Ciphergen
Biosystems (Fremont, CA). This technology combines matrix- assisted laser
desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and surface
chromatography. Ciphergen developed ProteinChip Arrays with different chromatographic
surfaces (e.g. weak cationic or strong anionic exchange, hydrophobic interphase) to bind
proteins from biological samples during the incubation process based on their biochemical
properties. After incubation, unbound proteins are removed by washing and the proteins
bound to the chip surface are analyzed by TOF mass spectrometry [7, 8, 9, 10].
SELDI TOF MS is a suitable technology for high throughput analysis of divergent biological
samples [11, 12, 13]. However the success of its application requires optimization of many
factors involved in sample pretreatment and chip protocol, e.g. : sample storage, protein
concentration applied on the chip, binding/washing buffer, type of applied matrix (energy
absorbing molecule-EAM), amount of applied EAM, age of EAM, number of EAM
application to the chip, and many others [14,15]. The scheme of the SELDI protocol is
depicted in Figure 1.
Chapter 1
46
Figure 1. Scheme of Chip Array protocol and tested factors. The major steps in the protocol of the Protein Chip Array. In the fractional design for optimization of the protocol we tested A, B, C, D, F, and G. In the second design for the reproducibility studies factors labeled with a star (*) were tested (C*, D*, F*, and G*). Factors in italics were not tested in factorial experiments, but the information about the applied levels were collected from prescreening experiments performed it the laboratory (results not shown).
Protein Chip array Protocol
Total protein amount loaded per spot
Molar concentration of the binding buffer B
Denaturation,no denat. A
Number of chip washes F*
Solvent of EAM C*
Type of EAM E
Saturation of EAM D*
Amount of EAM G*
Sample volume
Chip incubation with binding buffer
Sample binding
Water wash
Chip analysis in the ProteinChip Reader
Washing with binding buffer
pH of the binding buffer
EAM application
Chapter 1
47
To test all these factors one by one and by trial and error is expensive and laborious and does
not yield information on possible interactions.
Fractional factorial designs allow analyzing all major effects, determining main secondary
interactions, and confound some interactions to save on the number of experiments required
[16,17,18].
Here we describe the fractional factorial design for CM10 weak cation exchange chip
protocol for media derived from cultures of the human adipose tissue. We designed two
fractional factorial designs. The first design was to find the major factors involved in the
protocol preparation and to identify the protocols giving the best spectra quality. We analyzed
32 different combinations of conditions by analysis of variance (ANOVA) and found the
protocol generating the highest quality spectra. The second design investigated factors which
had major effects on the spectra reproducibility. In total 8 new protocols were applied to
samples in quadruplicates on 4 different chips. The protocol generating the most reproducible
spectra was selected.
The final protocol combines the findings both for the quality and reproducibility studies and
significantly contributes to improvement of generated spectra.
Materials and methods
Adipose tissue culture
About 5.5 g of human omental surgical biopsies were placed in transfer buffer (PBS
containing 5.5 mM glucose and 50 µg/ml Gentamycine), minced with scissors into pieces
(20-80 mg) and transferred to a sterile kitchen sieve with a cotton filter. On the filter the
tissue was washed thoroughly with 2 x 300 ml PBS at room temperature (RT), then briefly
with 200 ml PBS at 37ºC to remove residual blood components, leaked proteins, damaged
cells, and other low molecular debris. After washing, the fat was transferred to a 50 ml tube
with 40 ml PBS and centrifuged for 1 min at 1200 rpm to remove red blood cells and other
tissues containing insufficient amount of adipocytes to float. Afterwards the adipose tissue
was transferred to a filter and weighed again. The fat was cultured in 15 mm tissue culture
dishes. 1 g of fat was cultured in 10 ml M199 medium supplemented with 50 µg/ml
Gentamycine (Sigma) and 10 nM insulin (NovoNordisk) for 24 hrs at 37ºC in 5% CO2 The
culture medium was collected and stored at -20 ºC for further processing and analysis.
Chapter 1
48
Protein sample preparation
The culture media derived from human omental adipose tissue culture were concentrated in
Microcon Centrifugal Filter DevicesYM-3 (Millipore) according to the manufactures’
description at 15000 x g. After filtration the samples were resuspended in binding buffer (20
or 50nM NH4-Ac and 0.5% Triton, pH 4) to a final concentration of 200µg/ml. Protein
concentration was determined using a modified for 96 well plate Bradford Assay and
reagents from Bio-Rad Laboratories Ltd. Half of the concentrated media was treated with
20% ACN. The other half was untreated
Design of the Factorial Experiment
The SYSTAT 11 (Copyright © 2002 SYSTAT Software Inc.) statistical software package
was used to construct the fractional designs. The first design consisted of 7 dependent
variables (protocol factors) with two levels: 0 and 1(Table 1).
Table 1. 7 protocol factors were tested (A-G) in first fractional factorial design (quality studies). Factors labeled with * (C, D, F, G) were tested in the second fractional factorial design (reproducibility studies). Each factor was represented by two levels, defined here as level 0 and level 1. Abbreviations used in the table: ACN - acetonitrile, EAM – energy absorbing molecule, TFA – trifluoroacetic acid, SPA- sinapinic acid, CHCA - cyano -hydroxycinamic acid, DHBA - dihydroxybenzoic acid.
Symbol Factor Level 0 Level 1
A Denaturation ACN
pretreatment No ACN pretreatment
B Binding buffer 20 mM NH4Ac 50 mM NH4Ac
C* EAM solvent 50% ACN, 0.5%
TFA
30% ACN, 15% isopropanol, 0.5% TFA,
0.05% Triton
D* EAM saturation saturated 50% saturated
E Type EAM SPA CHCA/DHBA
F* Number of water
washes 1 2
G* Volume of EAM
depositions 1µl 0.5µl
Chapter 1
49
The factor A (denaturation/no denaturation), B (molar concentration of binding buffer), and F
(number of chip water washes) are related to the sample preparation and application/binding
to the chip. Factors C (solvent of EAM), D (saturation of EAM), E (type of EAM), and G
(amount of applied EAM) are related to the matrix preparation/ application. In total 32
different combinations of independent variables (protocol factors) and their levels were
applied to 4 chips (each chip had 8 spots). The independent variables and their levels were
randomly distributed on the spots using a random number table and each level of a factor is
represented 16 times, but each specific combination of 7 factors and their levels is
represented once. By performing the quality part of the experiment we aimed to estimate the
marginal contribution of each factor to the quality of spectra. Consequently we designed the
experiment in such a way that no information about all factors is available (e.g. lack of
replicates in the first design).
In the second design those factors which did not influence the quality were discarded. In this
way factors having significant effects on the spectra quality (see section Quality Criteria and
Ranking for details) were determined, in order to obtain more information on interactions and
confirm results from the first design with higher accuracy. These variables (C, D, F, and G;
the factors are depicted in the Table 1 and labeled with*) were analyzed in 8 spots (8
different combinations of tested factors at two levels) for the main effects and their
interactions. Each spot was replicated four times in order to assess the reproducibility of
obtained spectra.
Sample application on ProteinChip Array
Samples were applied to CM 10 weak cation exchange ProteinChip Arrays containing 8
spots present in a 96-well format bioprocessor (both from Ciphergen Biosystems). In total 8
chips were used in the two experiments. All chips were coming from the same lot number.
200 µl aliquots were applied in each well and covered with parafilm to avoid evaporation and
contamination during incubation (30 min. with shaking at 250 rpm on a circular shaker at
RT). After incubation the unbound material was removed by inverting the Bioprocessor and
striking it onto paper towels. Three 5- min washes with 200 µl binding buffer were performed
on a shaker at 250 rpm (RT). The chips were removed from the bioprocessor and briefly
washed in a 15 ml tube with Milli-Q water. Finally the chips were air-dried for 20 min (RT).
Chapter 1
50
Matrix preparation and application
Two types of energy absorbing molecules (EAM) were tested in these experiments: (i)
sinapinic acid (SPA), and (ii) a mix of (alpha)-cyano 4-hydroxycinamic acid (CHCA) and
dihydroxybenzoic acid (2, 3- DHBA); all EAM were purchased from Sigma-Aldrich).
Solutions of each of the EAMs were prepared approximately 10 min before application on the
dried spots containing bound proteins. Two solvents were tested (1) 50% ACN, 0.5% TFA
and (2) 30% ACN, 15% isopropanol, 0.5% TFA, 0.05% Triton. The solutions were prepared
in light-resistant 1.5 ml tubes. The tubes with the solvents were mixed vigorously using a
vortex. Afterwards the solutions were incubated for 5 min RT and vortexed again,
centrifuged 2 min at 14000 x g. For each spot EAM was applied twice (saturated) or (50%
saturated). Before first and second EAM application the spots were dried for 5 min. After
applying the matrix and an additional 10 min drying period, the chips were measured in a
SELDI-TOF mass spectrometer (PBS-II; Ciphergen Biosystems).
Chips measurement
Prior to the chip measurements, the SELDI-TOF mass spectrometer was calibrated using the
All-in-1 Peptide Standard for mass ranges up to 20 kDa and the protein molecular weight
calibrant kit for the mass range higher than 20 kDa (both from Ciphergen Biosystems). Each
chip was measured for the following mass ranges: (1) 3-20 kDa and (2) 20-150 kDa. Data
were captured according to an automated data collection protocol with proteinchip software
3.1 (Ciphergen). For the range 3-20 kDa the following settings were used: detector voltage
2050; detector sensitivity 9; 2 warming shots at laser intensity 235 (not collected); collection
shots at laser intensity 230, 5 shots were collected every five positions between 20 and 80;
high mass 20 kDa; mass optimization 3-16 kDa. For the range 20-150 kDa the settings were
as follows: detector voltage 2050; detector sensitivity 9; 2 warming shots at laser intensity
255 (not collected); collection shots at laser intensity 250, 5 shots were collected every five
positions between 20 and 80; high mass 150 kDa; mass optimization 20-70 kDa. After
baseline subtraction, peaks were assigned by automatic peak detection with the Ciphergen
ProteinChip Software (version 3.1). For the M/Z range from 3-20 kDa peaks with signal to
noise (S/N) ratio ≥3 were labeled, and for the M/Z range from 20-150kDa peaks with S/N-
ratio ≥5 were labeled. In addition to the automatic peak labeling we also labeled peaks
manually according the same criteria since the Ciphergen ProteinChip Software often omits
peaks in automatic labeling. Peak information was exported to Microsoft Excel. Analysis of
the spectra quality, reproducibility, and ranking was performed in Microsoft Excel.
Chapter 1
51
Quality criteria (dependent variables) and ranking
The criteria to describe the quality of the generated spectra (dependent variables) were as
follows: (1) total number of peaks (NRP), (2) average signal to noise ratio (S/N) (3) shape of
the peaks (PC1); (4) resolution of the peaks (PC2). We aimed for the highest number of
detectable peaks, since we want to screen for as many peptides/proteins as can be detected. A
high signal to noise ratio is beneficial, since this reduces detection of high noise levels in
some cases recognized by ProteinChip Software as potential peaks and facilitates peak
detection. The shape of peaks was determined by the peak cleanness (PC) 1 formula 1:
∑=½
11
W
M
NPPC (1)
Where NP is the number of peaks, M is the mass of a peak, W ½ its width at half-height. The
summation is taken of all the detected peaks (Cordingley H.C. et al.2003). The PC1 value is
also supplied by the Ciphergen Protein Chip Software where it is named MZ resolution. The
applied criterion was to maximize the PC 1 value and thereby select a spectrum with the best
shaped peaks. Peak resolution was determined by the peak cleanness PC2 formula (2):
PC2=W2½½1
)12(2
+−
W
MM (2)
Where M is the mass of a peak, W½ its width at half-height (Cordingley H.C. et al.2003). A
higher value indicates better resolution between adjacent peaks.
After calculation of the above mentioned criteria, each spectrum was ranked for the total
number of peaks, the average signal to noise ratio, and the averages of PC1 and PC2 values.
The best spectrum was ranked as 1, second best as 2 and so on. Two spectra with identical
properties for a specific quality criterion obtained the same average rank. Ranks were
calculated for each quality criterion and for each spectrum.
In the reproducibility studies 8 different protocols were tested. Samples treated according to
each protocol were applied in quadruplicate on 4 different chips. The reproducibility of the
protocol was assessed based on coefficient of variation (CV) for average of mass deviation
(MS), signal to noise value (SN), peak cleanness (PC1), and peak resolution (PC2). The
spectra generating the lowest CV value for a particular variable were ranked as 1, second
lowest CV as 2 and so on. Ranks were calculated for each reproducibility criterion and for
each spectrum.
Chapter 1
52
Statistical analysis (ANOVA)
Two fractional factorial experiments were performed: in the first experiment we assessed the
major factors significantly influencing the spectrum quality and determine the major
interactions between these factors and their levels (7 protocol factors were tested in 32
protocols). We identified protocol factors and their levels yielding the highest quality of the
spectra.
For the reproducibility study a second fractional design was created. In this design we tested
4 selected factors (independent variables) in 8 new protocols. Each protocol was applied in
quadruplicate on four different chips Afterwards we calculated major effects and 2 way
interactions.
ANOVA was performed using SPSS 12.0.1 for Windows (SPSS Inc., Chicago, IL, USA)
using the general linear model; all other calculations were performed in Microsoft Excel.
In both experiments we analyzed the main effects and the two- and three way interactions
that could be estimated. Effects and interactions were considered significant at p ≤ 0.02. A
number of 2- and 3-way interactions could not be estimated due to the confounding that is
intrinsic to the fractional factorial design.
Results and discussion
Quality study
In the first fractional factorial experiment we tested 7 factors at two levels of the CM10 chip
protocol and obtained 32 spectra. All tested protocol factors and their levels are shown in
Table 1.
Our goal was to maximise the values obtained for the four quality criteria (dependent
variables): number of peaks (NR), signal to noise values (S/N), peak shape (PC1), and peak
resolution (PC2). The obtained spectra showed many differences regarding the quality
criteria. With ANOVA we determined the main protocol factors and some of their secondary
interactions contributing to the development of the best quality traces. Since we are interested
in finding biomarkers we analysed spectra in a wide range of masses from 3 to 150 kDa. All
major effects and interactions are shown in Table 2.
Chapter 1
53
Table 2. The main effects determined by ANOVA in the quality studies. All main effects and interactions with p ≤ 0.02 were considered as significant. A number of interactions between 2 factors and their levels were determined, others were confounded. The main effects and the major interactions were established for peptides and proteins in the mass range 3-150 kDa.
The analysis showed that factor A at level 1 (A1, no ACN pretreatment of the sample)
generates spectra with the highest number of peaks, what may be explained by protein
precipitation caused by ACN and depletion of some proteins from the sample, when it is
ACN treated. Better signal to noise values were obtained by applying E0 (SPA) but not E1
(DHBA/CHCA mixture). Application of 1 µl of matrix (G0), instead of 0.5 µl (G1) generates
better spectra regarding signal to noise value, what may be caused by better crystallization
process and therefore better ionization and flying of proteins. A clear 2 way interaction
affecting the S/N (signal to noise) was found for factors A1*E0 (no ACN pretreatment *
SPA, p=0.002), and D1*E0 (50% EAM * SPA, p=0.000). Almost all found interactions for
type of EAM clearly benefit SPA (E0) instead of CHCA/DHBA (E1) mixture. However for
PC1 value (peak shape) the best settings are achieved when a combination of factors D1*E1
(50% saturated CHCA) was applied (p=0.009). It is difficult to explain why a combination of
50% saturated solution of CHCA/DHBA yields overall better peak shapes. The analysis
showed that also for different analysed spectrum ranges the best settings for PC1 were
achieved when using D1*E1 (6-9, 10-20 (data not shown), and 3-20kDa). Moreover all 2-
way interactions found for 3-150 kDa range show better results for 50% saturated EAM
solution (D1) compared to 100% saturated EAM (D0). Mean rank values for all identified
combination of factors for the mass range 3-150 kDa are shown in Figure 2.
Spectrum
range kDa
Dependent variable
Main effect
Type III sum of squares
F value
P value 2-way
interactions
Type III sum of squares
F value
P value df
3-150
NRP A1 176.781 7.208 0.013 1
SN E0 G0
1378.125 300.125
48.891 10.647
3,13e-007 0.003
A1* E0 D1* E0
136.125 242.000
17.211 30.598
0.002 4.34e-008
1
PC1 D1 364.500 5.693 0.025 D1* E1 496.125 9.938 0.009 1 PC2 A1* E0 666.125 11.418 0.006 1
Chapter 1
54
Figure 2. Mean rank values for mass range 3-150 kDa for all significant 2 way interactions (p ≤ 0.02). A: Signal to noise A*E interaction,; B: Signal to noise D*E interaction; C: Peak shape D*E interaction; D: Peak resolution A*E interaction.
Since it is known that SELDI-TOF sensitivity varies for different spectrum ranges and even
specific proteins, it was also interesting to narrow down the analysed spectrum ranges and see
if indeed identified main effects and their interactions are unique for diverse spectrum areas.
We analysed the following mass ranges: 3-10, 6-9, 9-12.5, 10-20, 3-20, 20-150 kDa and
determined major effects and interactions influencing the quality (in Table 3 are shown data
for, 3-20, 20-150, 6-9 kDa spectrum ranges, data found for mass ranges 3-10, 9-12.5, and 10-
20 kDa are not shown ).
Chapter 1
55
Table 3. The main effects determined by ANOVA in the quality studies. All main effects and interactions with p ≤ 0.02 were considered as significant. A number of interactions between 2 factors and their levels were determined, others were confounded. The main effects and the major interactions were established for peptides and proteins, divided into three groups 3-20, 20-150 and 6-9 kDa.
For the 3-20 kDa range, the most sensitive of SELDI TOF measurement, NRP (number of
peaks) gives better results by applying factor A1 (no ACN pretreatment). C0 (EAM solvent
as 50% ACN and 0.5% TFA), D1 (50 % saturated EAM), and F0 (one water wash) yields
spectra with the highest number of peaks either. The higher NRP value (number of peaks)
achieved by application of 1 water wash (F0) instead of 2, could be explained by less
extensive removal of proteins bound to the chip surface and visualized in the spectra. Strong
2 way interactions were found for S/N (signal to noise) value; better results were achieved by
applying A0*D0 (no ACN pretreatment*100% EAM solution), p=0.004, and C0*D0 (50%
ACN, 0.5% TFA*100% EAM solution), p=0.011. PC1 (peak shape) value is improved by
applying D1 (50% saturated EAM, and D1*E1 (50% EAM*CHCA/DHBA, p=0.007) and
A0*C1 (ACN pretreatment*30% ACN, 15% isopropanol 0.5% TFA. 0.05% Triton, p=
0.020). PC2 values (peak resolution) are improved by applying A0 (ACN pretreatment,
p=0.001). This fact may be explained by a precipitation of some proteins while applying
ACN in the pretreatment of the sample, which could reduce the number of proteins bound to
the chip, thereby raising the resolution. For our purposes it is more relevant to increase the
Spectrum
range kDa
Dependent variable
Main effect
Type III sum of squares
F value P value 2-way
interactions
Type III sum of squares
F value P value df
3-20
NRP
A1 C0 D1 F0
242.000 36.125 78.125 45.125
39.177 5.848 12.648 7.305
1,80e-006 0.024 0.002 0.012
1
SN A0* D0 C0* D0
38.281 26.281
13.543 9.297
0.004 0.011
1
PC1 D1 810.031 21.322 0.000109 D1* E1 A0* C1
306.281 215.281
11.079 6.650
0.007 0.020
1
PC2 A0 D0
420.500 722.000
12.951 22.237
0.001 8,55e-005
1
20-150
NRP E0 300.125 73.877 8,65e-009 D1* E0 40.500 18.758 0.001 1
SN E0 G0
1391.281 294.031
51.410 10.865
2,07e-007 0.003
A1* E0 D1* E0 D0* G0
101.531 205.031 87.781
10.168 20.532 8.791
0.009 0.001 0.013
1
PC1 E1 166.531 7.201 0.013 1
PC2 D0 457.531 5.815 0.024 A1* E0 488.281 7.216 0.021 1
6-9
NRP E1* F0 16.531 7.707 0.018 1 SN E1 634.500 6.727 0.016 A1* E1 264.500 8.015 0.016 1
PC1
C1 D1 E0 G1
318.781 657.031 282.031 318.781
7.536 15.532 6.667 7.536
0.011 0.001 0.016 0.011
D1* E1 413.281 20.174 0.001 1
PC2 D0 648.000 13.344 0.001 D0* E1 450.000 12.807 0.004 1
Chapter 1
56
peak number than resolution. For this reason we chose no ACN pretreatment of the sample
(A1).
The mass range 20 -150 kDa clearly shows the advantageous effect of SPA (E0) on NRP
(number of peaks), S/N (signal to noise), but not PC1 (peak shape) values, here the
CHCA/DHBA mixture is better (E1). This finding for NRP and S/N values is expected since
it is known that SPA is a better EAM for high molecular weight proteins. It is intriguing why
the shape of peaks is improved with application of the CHCA/DHBA mixture. We cannot
offer an explanation for that. Only for this mass range the volume of EAM depositions has a
major effect on S/N (signal to noise); 1µl (G0) is better than 0.5 µl (G1). This fact is difficult
to understand theoretically, but it may be hypothesized that a larger volume of matrix
improves crystallization and ionization of larger proteins. For NRP (nr of peaks) and S/N
(signal to noise) we found a very strong interaction between 50% saturated EAM solution
and SPA (D1*E0) (p=0.0001 for both quality criteria). Signal to noise ratio is also improved
in protocols where no ACN pretreatment was combined with application of SPA (A1*E0,
p=0.009). For the PC2 (peak resolution) value the higher results are achieved by application
of saturated EAM (D0).
In these studies we generated in total 32 unique spectra with significant variation in quality.
ANOVA revealed that a number of main factors were associated with multiple quality criteria
(dependent variables) across different mass ranges (e.g. E0 (SPA) gives better results for
signal to noise (SN) values for mass ranges 3-150 and 20-150 kDa; number of peaks (NRP)
for the mass range 20-150 kDa; peak shape (PC1) for the mass range 6-9 kDa). Some factors
relate to very specific quality criteria of single spectra ranges (e.g. C0 (EAM solvent as 50%
ACN and 0.5% TFA) increases number of peaks (NRP) only for the mass range 3-20 kDa;
G1 (0.5µl of EAM) improves peak shape (PC1) only for the mass range 6-9 kDa). Moreover
the analysis showed that there is a clear difference between combinations of factors yielding
the best quality for small (3-20 kDa) and high mass ranges (20-150 kDa). In practice it means
that different protocols should be applied to obtain optimal results for low and high molecular
weight proteins. In Figure 3 the spectra from 3-20 and 20-150 kDa ranges obtained with the
best and the worst protocol settings are shown.
Chapter 1
57
Figure 3. SELDI MS spectra from the factorial design experiment for protocol optimization. The spectra are in the range 3-20 kDa (A and B) and 20-150 kDa (C and D). The pictures show the traces performed with the best (A and C) and the worst (B and D) protocol. Next to the spectrum symbol (A, B, C and D) factors levels yielding the particular trace are shown ( A0, B1 etc.).
0
10
20
30
0
10
20
30
5000 10000 15000
0
2.5 5
7.5 10
0
2.5 5
7.5 10
25000 50000 75000 100000 125000
5000 10000 15000 kDa
25000 100000 125000 kDa
30 20 10 0
30 20 10 0
10 5 0
10 5 0
Mass/ Charge (m/z)
Rel
ativ
e In
ten
sity
R
elat
ive
Inte
nsi
ty
A: A1, B0, C0, D0, E1, F1, G1 B: A0, B0, C1, D0, E0, F0, G1
C: A1, B0, C0, D1, E0, F1, G1 D: A0, B1, C1, D1, E1, F1, G1
Chapter 1
58
Reproducibility study
In the second fractional design used in a reproducibility study we tested following factors: C,
D, F, and G on both levels (0 and 1); factors A, B, and E were set up at level 1, 1, and 0
respectively. We generated 8 new protocols in SYSTAT 11, and each protocol was applied to
four distinct chips. We used ANOVA to identify main effects and interactions related to
spectrum reproducibility. We calculated coefficients of variation (CV) for mass deviation
(MS), signal to noise value (S/N), peak shape (PC1), and peak resolution (PC2). The obtained
CV values strongly indicate that reproducibility can vary considerably depending on the chip
protocol. For the range of 3-20 kDa we could improve the CV for MS, S/N, PC1, and PC2.
The improvement was 33% for the mass deviation 55.6% for signal to noise, 54% for peak
shape, and 25% for peak resolution.
For the range 20-150 kDa the improvement was 55% for mass deviation (MS), 47% for
signal to noise (S/N), 62% for peak shape (PC1), and 58% for peak resolution (PC2). All CV
values are in Table 4.
Table 4. CV values obtained in the reproducibility studies with the best and the worst protocol, for mass ranges 3-20 and 20-150 kDa.
Different main factors and different levels affecting different quality criteria were identified.
There was a clear distinction between pattern of factors affecting the reproducibility of 3-20
kDa range and 20 -150 kDa. For the range 3-20 kDa the highest reproducibility for CV of
mass deviation is achieved by applying EAM solvent at level 1 (C1); 50% saturated EAM
(D1); two water washes (F1). Two water washes (F1) and 0.5µl of EAM (G1) reduces the CV
for signal to noise. The CV for a peak shape (PC1) is improved by applying EAM solvent at
level 0 (C0), saturated EAM (D0), one water wash (F0), and 1µl of applied EAM (G0). The
Spectrum range kDa Dependent variable Best CV Worst CV
3-20
MS 0.03 0.09
SN 22.6 40.6
PC1 11.3 21
PC2 4.7 18.6
20-150
MS 0.11 0.20
SN 15 32
PC1 13.3 21.4
PC2 17 29.3
Chapter 1
59
CV for peak resolution (PC2) is improved similarly to the CV of PC1 by using C0 and F0,
however 0.5µl of EAM (G1) is better for the CV of PC2. For the range 20-150 kDa the
highest reproducibility regarding mass deviation is achieved by applying C0, D1, F1 (EAM
solvent at level 0; 50% saturated EAM, two water washes, respectively). D0, F1 and G0
(saturated EAM, two water washes, 1µl of applied EAM, respectively) improve the CV for
signal to noise values. The CV for peak shape in reduced in all protocols when two water
washes (F1) were applied. The CV for peak resolution were decreased in protocols where
50% saturated EAM (D1) and two water washes (F1) where applied. For all factors and
interactions in the reproducibility studies see Table 5.
Table 5. The main effects and 2 way interactions determined in the reproducibility studies by ANOVA. All main effects and interactions with p ≤ 0.02 were considered as significant. The main effects and the major interactions were determined for the mass ranges 3-20 and 20-150 kDa.
Spectrum
range kDa
Depend. variable
Main effect
Type III sum of squares
F value
P value
df
2-way int.
Type III sum of squares
F value
P value
df
3-20
MS C1 D1 F1
18.000 32.000 18.000
30.375 54.000 30.375
7.73e-006 6.63e-008 7.73e-006
1
C1D1 C1F1 C1G1 F1G1 D1F1 D1G0
58.000 38.000 38.000 38.000 50.000 42.000
19.333 7.389 7.389 7.389 12.963 8.909
5.43e-007 0.002 0.001 0.001
1.71e-005 0.000263
3
SN F1 G1
32.000 50.000
10.537 16.463
0.003 0.00038
1
C1F1 C1G1 F1G1 D0G1
84.000 84.000 84.000 84.000
9.333 9.333 9.333 9.333
0.000193 0.000193 0.000193 0.000193
3
PC1
C0 D0 F0 G1
98.000 32.000 8.000 18.000
220.500 72.000 18.000 40.500
1.64e-014 4.24e-009 0.00023
8.15e-007
1
C0D0 C0F0 C0G1 F0G1
132.000 124.000 124.000 124.000
34.222 26.303 26.303 26.303
1.66e-009 2.69e-008 2.69e-008 2.69e-008
3
PC2 C0 F0 G1
32.000 32.000 32.000
12.000 12.000 12.000
0.002 0.002 0.002
1
C0F0 C0G1 F0G1 D0F0 D1G1
96.000 96.000 96.000 64.000 64.000
12.444 12.444 12.444 5.744 5.744
2.36e-005 2.36e-005 2.36e-005
0.003 0.003
3
20-150
MS C0 D1 F1
32.000 8.000 32.000
54.000 13.500 54.000
6.63e-008 0.001
6.63e-008 1
C0D1 D1G1
8.000 16.000
6.500 26.000
0.005 6.27e-007
2
SN D0 F1 G0
32.000 32.000 32.000
13.500 13.500 13.500
0.001 0.001 0.001
1 C1D0 D0G0
64.000 32.000
26.000 6.500
6.27e-007 0.005
2
PC1 F1 72.000 27.000 1.79e-005 1 D1F1 40.000 13.000 0.00012 2
PC2 D1 F1
32.000 72.000
18.000 40.500
0.000232 8.15e-007
1 C0D1 D1F1 D1G1
40.000 40.000 64.000
13.000 13.000 52.000
0.00012 0.00012
8.19e-010 2
Chapter 1
60
In some cases is difficult to understand the reason why some factors at particular levels yield
more reproducible spectra. Moreover the optimal levels of protocol factors show in a number
of cases opposite parameters for the optimal outcome of a specific criterion for a particular
mass range due to interactions. In practice it means that when choosing the most reproducible
protocol compromises have to be made, and very often it may be necessary to prioritize some
criteria above others. For example in seeking for biomarkers the number of peaks is more
important than peak shape, but for a better visualization of a peak of interest, the resolution
and the peak shape would be more important then the total number of peaks in a spectrum.
Definitely there is a large difference between a characterization of 3-20 kDa range spectrum
and the 20-150 kDa range spectrum, which indicates that it is actually favorable to have
separate protocols for these two ranges of spectra in order to achieve an optimal outcome.
Conclusions
The final protocol we advocate for the screening within mass range 3-150 kDa is a
compromise between quality and reproducibility studies, but also between small and higher
mass ranges characteristics, prioritizing some criteria above others, such as number of peaks
above peak shape etc.. The optimal settings are: A1 (no ACN pretreatement), C1 (30% ACN,
15% isopropanol, 0.5%TFA, 0.05% Triton as EAM solvent), D1 (50% saturated EAM), E0
(SPA), F1 (two water washes), and G1 (0.5µl of EAM). The level of factor B does not have a
major effect on the analyzed criteria, and can be set at level 1 (50mM NH4Ac). In case of
interest in more narrowed-down spectrum range the protocol should be adjusted according to
the found characteristics for this specific mass range.
Many results found in these studies are as expected and can be scientifically explained but
some of them are difficult to understand. The systematic statistical approach results in a
protocol that generates high quality spectra and significantly improves the reproducibility
over previous protocols. It is possible that the outcome is specific for our experimental
condition. We recommend the application of fractional factorial design experiments for
different sample types and chip types because it is an efficient way to gain insight into the
major effects and especially their interactions involved in a particular preparation process.
Chapter 1
61
References
(1) Freeman, H.; Cox R.D. Type-2 diabetes: a cocktail of genetic discovery. Hum. Mol. Genet. 2006, 15, Suppl 1: R202-9.
(2) Esposito K.; Giugliano G.; Scuderi N.; Giugliano D. Role of adipokines in the obesity-inflammation relationship: the effect of fat removal. Plast. Reconstr. Surg. 2006, 118, 1048-1059.
(3) Bastard J.P.; Maachi M.; Lagathu C.; Kim M.; J.; Caronn M.; Vidal H.; Capeau J.; Feve B. Recent advances in the relationship between obesity, inflammation, and insulin resistance. 2006, 17, 4-12.
(4) Vendrell J.; Broch M., Vilarrasa N.; Molina A.; Gomez J.M.; Gutierrez C.; Simon I.; Soler J.; Richart C. Resistin, adiponectin, ghrelin, leptin and proinflammatory cytokines: relationships in obesity. 2004, 12, 962-971.
(5) Greenberg, A.S.; Obin, M.S. Obesity and the role of adipose tissue in inflammation and metabolism. Am J. Clin. Nutr. 2006, 83, 461S-465S.
(6) Scherer, P.E. From lipid storage compartment to endorcine organ. Diabetes 2006, 55, 1537-1545. (7) Merchant M.; Weinberger S.R. Recent advancements in surface-enhanced laser desorption/ionization-time
of flight-mass spectrometry. Electrophoresis 2000, 21, 1164-1177. (8) Bertucci F.; Birnbaum D.; Goncalves A. Proteomics of breast cancer: principles and potential clinical
applications. Mol. Cell Proteomics 2006, in press. (9) Clarke C.H.; Buchley J.A.; Fung E.T. SELDI-TOF-MS proteomics of breast cancer. Clin. Chem. Lab. Med.
2005, 43 , 1314-1320. (10) Issaq H.J.; Veenstra T.D.; Conrads T.P.; Felschow D. The SELDI-TOF MS approach to proteomics:
protein profiling and biomarker identification. Biochem. Biophys. Res. Commun. 2002, 292, 587-592. (11) Nomura F.; Tomonaga T.; Sogawa K.; Ohashi T.; Nezu M.; Sunaga M.; Kondo N.; Iyo M.; Shimada H.;
Ochiai T. Identification of novel and downregulated biomarkers for alcoholism by surface enhanced laser desorption/ionization-mass spectrometry. Proteomics 2004, 4, 1187-1194.
(12) Roelofsen H.; Balgobind R.; Vonk R. J. Proteomic analyzes of copper metabolism in an in vitro model of Wilson disease using surface enhanced laser desorption/ionization-time of flight-mass spectrometry. J. Cell Biochem. 2004, 93, 732-740.
(13) Cadieux P.A.; Beiko D.T.; Watterson J.D.; Burton J.P.; Howard J.C.; Knudsen B.E.; Gan B.S.; McCormick J.K.; Chambers A.F.; Denstedt J.D.; Reid G. Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS): a new proteomic urinary test for patients with urolithiasis. J. Clin. Lab. Anal. 2004, 18, 170-175.
(14) Chertov O.; Biragyn A.; Kwak L.W.; Simpson J.T.; Boronina T.; Hoang V.M.; Prieto D.A.; Conrads T.P.; Veenstra T.D.; Fisher R.J. Organic solvent extraction of proteins and peptides from serum as an effective sample preparation for detection and identification of biomarkers by mass spectrometry. Proteomics 2004, 4 , 1195-1203.
(15) Laugesen S.; Roepstorff P. Combination of two matrices results in improved performance of MALDI MS for peptide mass mapping and protein analysis. J. Am. Soc. Mass Spectrom. 2003, 14, 992-1002.
(16) Cordingley H.C.; Roberts S.L.; Tooke P.; Armitage J.R.; Lane P.W.; Wu W.; Wildsmith S.E. Multifactorial screening design and analysis of SELDI-TOF ProteinChip array optimization experiments. Biotechniques 2003, 34, 364 -373.
(17) Park J.T.; Bradbury L.; Kragl F.J.; Lukens D.C.; Valdes J.J. Rapid optimization of antibotulinum toxin antibody fragment production by an integral approach utilizing RC-SELDI mass spectrometry and statistical design. Biotechnol. Prog. 2003, 22, 233-240.
(18) Baranda A.B.; Etxebarria N.; Jimenez R.M.; Alonso R.M. Improvement of the chromatographic separation of several 1,4-dihydropyridines calcium channel antagonist drugs by experimental design. J.Chromatogr. Sci. 2005, 43, 505-512.
Chapter 2
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Chapter 2
63
Chapter 2
Characterization of the human visceral adipose
tissue secretome
Gloria Alvarez-Llamas
Ewa Szalowska
Marcel P. de Vries
Desiree Weening
Karloes Landman
Annemieke Hoek
Bruce H.R. Wolffenbuttel
Han Roelofsen
Roel J. Vonk
Molecular and Cellular Proteomics 2007
Chapter 2
64
Summary
Adipose tissue is an endocrine organ involved in storage and release of energy, but also in
regulation of energy metabolism in other organs via secretion of peptide and protein
hormones (adipokines). Especially visceral adipose tissue has been implicated in the
development of metabolic syndrome and type 2 diabetes. Factors secreted by the stromal-
vascular fraction contribute to the secretome and modulate adipokine secretion by adipocytes.
Therefore, we aimed at the characterization of the adipose tissue secretome rather than the
adipocyte cell secretome. The presence of serum proteins and intracellular proteins from
damaged cells, released during culture, may dramatically influence the dynamic range of the
sample and thereby identification of secreted proteins. Part of the study was therefore
dedicated to the influence of the culture set-up on the quality of the final sample. Visceral
adipose tissue was cultured in five experimental set-ups and the quality of resulting samples
was evaluated in terms of protein concentration and protein composition. The best set-up
involved one wash after the first hour in culture followed by two or three additional washes
within an eight hour period, starting after overnight culture. Thereafter, tissue was maintained
in culture for additional 48 to 114 hrs to obtain the final sample. For the secretome
experiment, explants were cultured in media containing 13C6,15N2 L-lysine to validate the
origin of the identified proteins (adipose tissue or serum derived). In total, 259 proteins were
identified with ≥99% confidence. 108 proteins contained a secretion signal peptide of which
70 incorporated the label and were considered secreted by adipose tissue. These proteins were
classified into five categories according to function. This is the first study on the (human)
adipose tissue secretome. The results of this study contribute to a better understanding of the
role of adipose tissue in whole body energy metabolism and related diseases.
Chapter 2
65
Introduction
Adipose tissue is a key organ for the regulation of energy metabolism. Besides its function as
an energy storage depot in the form of triglycerides, adipose tissue secretes a variety of
peptide and protein hormones (adipokines) involved in the regulation of energy metabolism
such as leptin, adiponectin, visfatin, retinol binding protein-4, adipsin, tumor necrosis factor α
(TNF-α) and interleukin 6 (IL-6) (1-3). Dysregulation of the production of adipokines and
free fatty acids contributes to the pathogenesis of diseases associated with energy metabolism
such as insulin resistance, metabolic syndrome and type 2 diabetes. Especially visceral
adipose tissue has been implicated in the development of these diseases (2-4). Therefore,
more insight into the visceral adipose tissue secretome will contribute to a better
understanding of its role in energy metabolism and related diseases and may lead to the
discovery of unknown peptides/proteins involved in regulation of energy metabolism and
new targets for therapy. Besides adipocytes, adipose tissue contains endothelial cells,
macrophages and fibroblasts (stromal fraction) which may modulate the overall peptide and
protein secretion pattern of the tissue via cross talk between the different cell types. For
example, factors secreted by macrophages have been shown to induce changes in the
secretion of adipokines, free fatty acids and glucose uptake by 3T3-L1 adipocytes (5). These
interactions between cells from the stromal fraction and adipocytes are necessary for
physiological functions of adipose tissue, and deregulation of this cross talk is regarded as an
important mechanism leading to insulin resistance and type 2 diabetes (6-9). Therefore, the
tissue secretome provides more relevant information for the in vivo situation than the
adipocyte cell secretome. To date, no studies have been published on the adipose tissue
secretome. Several studies investigated the human (10), mouse (11-14) and rat (15) adipose
tissue proteome, mostly using a two-dimensional gel electrophoresis (2DE) approach. Celis et
al. (16) analysed the human mammary adipose tissue proteome. The secretome from
adipocyte cells has been investigated by Kratchmarova et al. (17) and Wang et al. (18). They
studied changes in protein secretion during differentiation of the 3T3-L1 mouse pre-
adipocyte cell line to adipocytes. In another study in isolated rat adipocytes, Chen et al. (19)
identified 84 secreted proteins using 2DLC-MS/MS.
The major challenge for characterization of the adipose tissue secretome is the quality of the
secretome sample. The presence of serum proteins inside the tissue pieces that slowly diffuse
into the culture medium and the presence of intracellular proteins that are released from
damaged cells due to the cutting of the tissue, necessary for culture, can dramatically
Chapter 2
66
influence the dynamic range of the sample and thereby the detection of the secreted proteins.
Also the relevance of the identified proteins can be unclear if the source of the proteins
(secreted, serum or intracellular) is not clear due to a high level of contaminant proteins. This
has been a problem in previous studies with tissue explants where e.g. adiponectin, secreted
by the tissue, could not be reliably measured due to diffusion of serum adiponectin, as a
second source of adiponectin, from the tissue into the culture medium (20). In addition, the
duration of the culture influences the level of secreted proteins that accumulate in the
medium, but may also affect the function and breakdown of cells in the tissue. In view of
secretome complexity and considering that adipokines are expected in low concentrations
(ng/ml), the quality of the sample obtained from tissue culture is crucial to obtain high
quality, relevant secretome data. Specific removal of high-abundance serum proteins in
biological samples has been previously described (21), but does not resolve the problem of
contamination derived from intracellular proteins. Besides, low-abundance proteins that bind
to the high-abundance ones may also be removed at the same time. Therefore, we did not
consider this option but carefully evaluated the influence of the culture set-up on the quality
of the sample for secretome analysis. For this purpose, the influence of several culture set-ups
that varied in the number of washes and the distribution of washes in-time was investigated.
Protein concentration, dynamic range and composition of the resulting samples were then
evaluated. From these experiments the best culture set-up was chosen for further secretome
characterization. Proteins were then identified by LC-MS/MS after SDS-PAGE fractionation.
The resulting list of secreted proteins was validated by culturing adipose tissue in the
presence of stable isotope-labelled L-lysine. Proteins that contain a signal peptide sequence
and incorporate the label are derived from adipose tissue and not from an external source
such as serum.
Experimental Procedures
Adipose tissue culture
Human visceral adipose tissue explants were obtained from five women (age 25-64; BMI 19-
31) undergoing surgery for non-carcinogenic gynaecologic disorders. For each culture set-up
(see figure 1), adipose tissue from one subject was used. The study had the approval of the
local ethical committee.
Chapter 2
67
The adipose tissue culture protocol is based on Fried et al. (22). Briefly, adipose tissue
explants were transported from the operating room to the laboratory in transport buffer (PBS,
5.5 mM glucose, 50 µg/ml gentamicin) at room temperature. The following procedures were
carried out under a laminar flow hood, using sterile equipment. Immediately upon arrival, the
tissue was transferred to a Petri dish containing 20 ml of PBS and was finely minced in 20 to
80 mg pieces using scissors. The tissue pieces were extensively washed with 400 ml of PBS
over a filter containing sterile cotton bandage fabric. Thereafter, the tissue pieces were
transferred to a 75 cm2 culture flask containing 200 ml of PBS and were gently shaken for a
short period. Next the contents of the flask were poured over the filter and the tissue pieces
were washed with 300 ml of warm PBS (37°C). The tissue pieces were transferred to a tube
containing 50 ml of PBS and centrifuged for 1 minute at 277 g at room temperature to
remove red blood cells and debris. The tissue was then removed from the tube and the weight
was determined. 1 g of tissue was then placed in a Petri dish with 10 ml M199 (Gibco)
culture media supplemented with 50 µg/ml gentamicin. Adipose tissue pieces were cultured
at 37 ºC and 5% CO2 following the different culture set-ups (figure 1). The final secretome
sample and the media collected after every washing step in culture (figure 1) were stored at -
80 ºC.
Sample pre-treatment
Nine to ten ml adipose tissue culture medium was concentrated 25 to 30 fold by ultra-
filtration (Centriplus, 3 kDa cut-off, Millipore). The concentrated sample was used for total
protein concentration measurement (Bradford assay, Bio-Rad), SELDI-TOF-MS profiling
and protein identification by LC-MS/MS.
SELDI-TOF-MS protein profiling
Spots of a CM10 (weak cation exchanger) ProteinChip® array (Ciphergen Biosystems,
Fremont, CA), inserted into a bioprocessor, were pre-incubated twice with 200 µl binding
buffer (100 mM ammonium acetate, 0.05% Triton, pH 4.0) for five minutes at room
temperature with vigorous shaking. The buffer was then removed and 4-12 µl concentrated
sample (depending on protein concentration) was applied on every spot. Binding buffer was
added to a total volume of 100 µl per well (µg protein was adjusted so that the same protein
amount per spot was applied). After incubation for 30 minutes, the sample was removed and
the spots were washed three times with 200 µl binding buffer for five minutes followed by a
wash with 200 µl ultra pure water. The water was removed and the chip was allowed to air-
Chapter 2
68
dry before applying two times 0.5 µl of 5 mg α-cyano-4-hydroxy cinnamic acid (CHCA)
diethylamine salt dissolved in 1 ml of 50% ACN, 0.5% TFA. Mass analysis was performed
in a ProteinChip® reader (model PBS II, Ciphergen Biosystems) according to an automated
data collection protocol. Calibration was performed with the All-in-One peptide mix
(Ciphergen Biosystems) and spectra were obtained in the mass range 3.5-100 kDa at several
laser intensities (175-225).
Label experiment
An adipose tissue explant from a patient undergoing surgery for uterus myomatosus (47 years
old, BMI 22.9) was collected, cut, washed and divided into two Petri dishes as described
above. In this experiment, tissue was cultured from the start in lysine-free M199 media (ref.
22340 Lys-free, Gibco) in order to deplete lysine from other sources (serum). The media was
renewed after 1h, 21.5h, 25.5h and 29.5h. In the last wash (time point 29.5h), one dish
received normal M199 containing 70 mg/l of 12C6, 14N2 L-lysine and 60 nM insulin. The
other dish received M199 lysine-free media contained 70 mg/l labeled lysine (L-Lysine:
2HCl, U-13C6, 98%; U-15N2, 98%, Cambridge Isotope Laboratories, Inc., Andover MA, USA)
and 60 nM insulin. Tissue was maintained in culture for an additional 72 hrs. Thereafter,
media were collected and stored at -80 ºC until analysis. Normal and labeled media were then
mixed in 1:2 ratio and were concentrated by ultra-filtration before SDS-PAGE fractionation.
Protein identification by LC-MS/MS
Proteins present in the concentrated adipose tissue media sample were fractionated by SDS-
PAGE on a 4-12% Bis-Tris gel with a MOPS buffer system, according to manufacturer
protocol (NuPAGE®-Novex, Invitrogen, Carlsbad, CA, USA). Protein separation occurred
for 50 minutes at 200 V and visualization of bands was performed overnight by Coomassie
Brilliant Blue G-250 based staining (PageBlue Staining Solution, Fermentas). The whole lane
was excised into 28 bands which were processed for tryptic digestion. Each band was cut into
small pieces and stored at -20 ºC until analysis. Then, they were washed in ultra pure water
and dehydrated in ACN. In-gel reduction with dithiothreitol (for one hour at 60 ºC) and
carbamidomethylation with iodoacetamide (for 45 minutes at room temperature in the dark)
were performed. Gel pieces were subsequently washed with ultra pure water, 50% ACN and
pure ACN. Next, 0.1 µg trypsin in 50 mM ammonium bicarbonate was added and gel pieces
were allowed to rehydrate on ice for 20 minutes. Digestion was carried out overnight at
37 ºC.
Chapter 2
69
Separation of the resulting tryptic peptide mixtures was performed by nanoscale reversed-
phase liquid chromatography tandem mass spectrometry (LC-MS/MS). The Agilent 1100
nanoflow/capillary LC system (Agilent, Palo Alto, CA, USA) was equipped with a trapping
column (5 x 0.3 mm C18RP) (Dionex/LC Packings, Amsterdam, The Netherlands) and a
nanocolumn (150 x 0.075 mm, C18Pepmap) (Dionex/LC Packings). Peptides mixtures were
injected into the trapping column at a flow rate of 10 µl/min (3%ACN/0.1%FA). After 10
minutes the trapping column was switched into the nano flow system and the trapped
peptides were separated using the nano column at a flow rate of 0.3 µl/min in a linear
gradient elution from 95%A ( 3%ACN/0.1%FA) to 50%B (97%ACN/0.1%FA) in
50 minutes, followed by an increase up to 80% B in 3 minutes. The eluting peptides were on-
line electro-sprayed into the QStar XL Hybrid ESI Quadrupole time-of-flight tandem mass
spectrometer, ESI-qQTOF-MS/MS (Applied Biosystems, Framingham, MA;
MDSSciex,Concord, Ontario, Canada) provided with a nano spray source equipped with a
New Objective ESI needle (10 µm tip diameter). Typical values for needle voltage were 2 kV
in positive ion mode. Analyst QS 1.1 software (Applied Biosystems) was used for data
acquisition in the positive ion mode, typically with a selected mass range of 300-1200 m/z.
Peptides with +2 to +4 charge states were selected for tandem mass spectrometry, and the
time of summation of MS/MS events was set to be 2 seconds. The three most abundant
charged peptides above a 40 count threshold were selected for MS/MS and dynamically
excluded for 40 seconds with 100 ppm mass tolerance.
ProID 1.1 software (Applied Biosystems) (23) was used to identify proteins from the mass
spectrometric datasets according to Swiss-Prot database (May 2005, ~181000 entries). Mass
tolerance was set to 0.15 Da (MS) and 0.1 Da (MS/MS) and carboxamidomethylation and
methionine oxidation were chosen as modifications for database search.
Classification of identified proteins in terms of secretion pathways was performed according
to SecretomeP 2.0 Server (24). Those proteins with a signal peptide predicted by SignalP
were considered as secreted proteins via a classical pathway (Endoplasmic Reticulum/Golgi-
dependent pathway). If no signal peptide was predicted but the NN-score exceeded 0.6 value,
proteins were classified as secreted via non-classical pathway. Trans-membrane helices and
location were predicted according to TMHMM Server (25). MS spectra of identified peptides
which showed a lysine in the C-terminus were searched for shifts of 8, 4 or 2.666 m/z (singly,
doubly or triply charged ions, respectively). If a peptide incorporated the label, the derived-
protein was considered to be synthesized by adipose tissue.
Chapter 2
70
Results
Evaluation of culture set-up and sample quality
The standard adipose tissue culture protocol involves cutting of the tissue explants into small
pieces followed by several washing steps to remove serum and intracellular proteins before
culturing, as described in the Experimental Procedures section. Because of the cutting,
damaged cells will slowly lose their contents into the media. Furthermore, serum proteins still
present in the tissue pieces will diffuse out during culture. Therefore, additional washing
steps during culture were necessary to obtain a sample for secretome analyses containing
mainly adipose tissue-derived secreted proteins. In preliminary studies we evaluated the
protein composition of the adipose tissue media, cultured for 48 hrs with one washing step
after the second hour of culture (figure 1, set-up A). After in-solution digest of the final
sample and LC-MS/MS analysis, 42 proteins were identified with ≥95% confidence (results
not shown). According to the Human Protein Reference Database (HPRD) (26) none of them
could be directly related to a protein secreted by adipose tissue. Typical serum proteins
(albumin, hemoglobin and transferrin) and intracellular proteins (actin, histones and perilipin)
dominated the secretome composition.
To reduce the concentration of these high-abundance contaminating proteins, five different
culture set-ups were evaluated including the set-up mentioned above as control (see figure 1).
Chapter 2
71
Figure 1. Distribution of washing steps in time for the different culture set-ups assayed. An insert is included for visualizing the distribution of washes at the beginning of the culture period (first 6h).
The number of washes (replacement with fresh media) during tissue culture, as well as their
distribution in time, were varied in order to evaluate effects on the quality of the final sample
for secretome analyses. Processing of the visceral adipose tissue explants, obtained from the
five patients (A-E), was the same (described in the experimental section). During culture,
washing steps were performed by replacing media (10 ml) with 10 ml of fresh media,
according to the different schemes (A-E) depicted in figure 1. The media of the washing steps
and the final secretome samples were collected for further analyses. Protein concentration
was measured to evaluate the effectiveness of the different culture set-ups on removing (high-
abundance) serum and intracellular proteins. According to figure 2, protein concentrations in
the final media samples were the lowest in set-up D and E. This suggests set-ups D and E
were better in removing high-abundance proteins when compared to A and B while set-up C
gave an intermediate result.
0 20 40 60 80 100 120 140
Time (h)
A
B
C
D
E
0 1 2 3 4 5 6
Time (h)
Chapter 2
72
Figure 2. Total protein concentration measured in media collected from every washing step and in the final secretome sample. In x-axis, consecutive time points are displayed per experiment and every time point represents the culture time since the previous wash (periods of accumulation). Bars in black represent the initial protein concentrations and bars in grey refer to the final protein concentrations for every set-up. In figure 1, it is indicated the time points when the samples were collected.
The protein composition of the final samples obtained from each culture set-up was
investigated by SDS-PAGE (figure 3) and SELDI-TOF-MS (figure 4). When comparing the
lanes with samples of the indicated culture set-ups (figure 3), it was clear that samples from
set-ups A and B showed less uniformity in intensity of bands than those of set-ups C, D and
E.
0
5
10
15
20
25
30
2h 48h 1h
16.5
h
74h
30m
30m
30m
30m
30m
30m
48h 1h 21h 8h 48h 1h 15h 4h 4h
113.
5h
A B C D E
Pro
tein
co
ncen
tra
tion
(µg/
ml)
Chapter 2
73
Figure 3. SDS-PAGE (12% Bis-Tris gel) of final secretome samples obtained from the different culture set-ups as indicated in figure 1 (m: marker; A-E: final secretome samples).
We also used SELDI profiling to monitor changes in the dynamic range of the samples,
which are expected to occur if high-abundance contaminating proteins are removed more
efficiently. High-abundance proteins are likely to suppress ionization of low-abundance
proteins (27). As a consequence, with a certain laser energy, a lower total number of peaks
can be expected in a sample with large concentration differences compared to a sample with
small differences in concentration of individual proteins. We made use of this phenomenon to
evaluate the dynamic range of the final samples resulting from the different culture set-ups
(A-E). For this, spectra were obtained for every sample at four different laser intensities in
the mass range from 3.5 to 17.5 kDa. Figure 4.1 shows an example of spectra obtained for the
different set-ups with the lowest laser intensity (175). From these spectra it can already be
deduced that samples of set-up D and E showed considerable more peaks than samples from
the other three set-ups at this laser intensity. Total peak number (S/N>5) was also calculated
from all spectra and plotted against laser intensity (figure 4.2). At laser intensity 190 still no
Chapter 2
74
maximum is reached in the number of detected peaks for set-ups A, B and C, while the
number of peaks that could be detected with set-ups D and E plateaus at laser intensities 180
and 185, respectively. This indicates that the dynamic range of sample D and E is lower than
that of the other samples. We also monitored peak intensities of the α and β chain of
haemoglobin (predicted to be appearing at 15.1 and 15.9 kDa, respectively) and albumin (66
kDa) in the SELDI spectra which clearly showed a reduction in peak intensity (abundance) in
spectra from sample D and E compared to A, B and C (figure 4.3).
When results of these analyses are combined we conclude that one or two washing steps as
used in set-up A and B before isolation of the final media sample is clearly not sufficient. Set-
up D and E contained 3 or 4 washing steps, respectively, distributed over a period of 24 hrs
before the final sample was obtained. This clearly gave better results, also when compared to
set-up C. Here, six washing steps were applied but all in the beginning of the tissue culture
period. Based on these analyses we conclude that culture protocols D and E performed best.
Chapter 2
75
Figure 4. (4.1) SELDI-TOF-MS profiles (CM10 weak cation exchange chip) in the 3.5-17 kDa mass range at the lowest laser intensity (175); (4.2) graph of total peak number plotted against laser intensity by SELDI-TOF-MS, as indication of the dynamic range of final secretome samples; (4.3) SELDI-TOF-MS spectra zoomed-in at the mass range of hemoglobin and albumin (laser intensities 185 and 225, respectively) for the five set-ups.
A
B
C
D
E
5000 7500 10000 12500 15000
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
Mass (Da)
Pea
k in
tens
ity
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
4.1
0
5
10
15
20
25
170 175 180 185 190 195
laser intensity
tota
l pea
k nu
mb
er (
S/N
>5)
B
CD
E
A
4.2
A
B
C
D
E
14000 15000 16000
Pea
k in
tens
ity
Mass (Da)
Hemoglobin
0
1
2
00.5
11.5
00.250.500.75
00.10.20.3
00.250.500.75
15000
α β
50000 60000 70000 80000
0246
0246
0246
0246
0246
A
B
C
D
E
Mass (Da)
Pea
k in
tens
ity
4.3Albumin
Chapter 2
76
Secretome characterization
After the optimal culture set-up was established, this protocol was used for characterization
of the secretome. To determine whether identified proteins are secreted by adipose tissue or
are derived from serum that may still be present as a contaminate of the sample, a labelling
experiment was carried out. In this qualitative approach, an adipose tissue explant was
divided into two dishes. After the washing steps were performed in lysine-free media (see
Experimental Procedures section), the tissue was cultured for an additional 72 hrs. One dish
contained media supplemented with 13C6,15N2 L-lysine and the other dish contained normal
media containing 12C6,14N2 L-lysine. To facilitate detection of label incorporation by mass
spectrometry, media from both dishes were mixed in a 1:2 ratio in favour of the stable isotope
label. After concentration, the sample was fractionated by SDS-PAGE. The lane was excised
in 28 bands for tryptic digestion and digests were analysed by LC-MS/MS for protein
identification as described in the experimental section. The complete list of identified
proteins and peptide sequences obtained from the MS/MS data can be found in the
supplemental data file C. Peptide confidences > 5 are shown for each identified protein.
Confidence scores generated by the Pro ID software are explained in a paper by Tang et al.
(23). By this approach, a total of 297 proteins could be identified with ≥95% confidence.
Within this group, 259 proteins were identified with ≥99% confidence. Proteins, identified
with the highest confidence score (≥99%), were analysed for the presence of a signal peptide
using the SecretomeP 2.0 Server to determine whether they were secreted. This analyses
reveals that 108 out of 259 proteins were secreted following a classical pathway
(Endoplasmic Reticulum/Golgi-dependent pathway) (see tables 1 and 2). For all these
proteins a signal peptide was predicted by SignalP. Some of them (marked with an asterisk in
tables 1 and 2) contained trans-membrane helices located more than 40 amino acids away
from the N-terminus according to the TMHMM Server. This implies that they were anchored
to the membrane. However, because a signal peptide was predicted we considered them as
secreted. The possibility exists that they were released from the cell via cleavage of the extra-
cellular part of the protein. Proteins identified with ≥99% confidence that contained a signal
peptide were classified according to whether or not they incorporated the label (tables 1 and
2). Out 108 proteins containing a signal peptide, label incorporation could be confirmed for
70 of them (see table 1). These proteins were considered genuine adipose tissue secreted
proteins. For this, the MS spectra of the corresponding peptides were manually checked for
mass shifts due to label incorporation as described in the experimental section. Some proteins
Chapter 2
77
were only identified by peptides ending on an arginine at the C-terminus (no lysine present).
This leaves open the possibility that they are derived from adipose tissue although the label
incorporation could not be detected (see first group of non-labeled proteins in table 2).
Table 1. Secreted proteins (classical pathway; signal predicted by SignalP) that incorporated the label. They were identified after SDS-PAGE fractionation and tryptic digest analysis by LC-MS/MS (≥99% confidence).
ACCESSION NO. PROTEIN NAME
LABELED PROTEINS
Signalling / Regulatory function
gi|14916999 78 kDa glucose-regulated protein (GRP 78) (Immunoglobulin heavy chain binding protein)
gi|20137531 Adipocyte-derived leucine aminopeptidase (A-LAP) (ARTS-1) (Aminopeptidase PILS)
gi|2493789 Adiponectin §┼#
gi|117501 Calreticulin (CRP55) (Calregulin) §
gi|1171064 Cell surface glycoprotein MUC18 (Melanoma-associated antigen MUC18) *
gi|20177861 Complement C1q tumor necrosis factor-related protein 5
gi|23396772 Ectonucleotide pyrophosphatase/phosphodiesterase 2 (E-NPP 2)
gi|17865698 Endoplasmin (94 kDa glucose-regulated protein) (GRP94)
gi|23396609 Insulin-like growth factor binding protein 7 (IGFBP-7) (IBP-7) (IGF-binding protein 7) §
gi|117558 Macrophage colony stimulating factor-1 (CSF-1) (MCSF) (M-CSF) *
gi|9297107 Neuropilin-1 (Vascular endothelial cell growth factor 165 receptor) *
gi|118090 Peptidyl-prolyl cis-trans isomerase B (PPIase) (Rotamase) (Cyclophilin B)
gi|46576887 Periostin (PN) (Osteoblast-specific factor 2) (OSF-2)
gi|3024715 Peroxiredoxin 4 (Prx-IV) (Thioredoxin peroxidase AO372)
gi|20178323 Pigment epithelium-derived factor (PEDF) (EPC-1) §#
gi|124096 Plasma protease C1 inhibitor (C1 Inh) §
gi|62298174 Plasma retinol-binding protein (PRBP) (RBP) §
gi|129576 Plasminogen activator inhibitor-1 (PAI-1) (Endothelial plasminogen activator inhibitor) §
gi|401413 Von Willebrand factor precursor (vWF)
ECM
gi|24212664 Basement membrane-specific heparan sulfate proteoglycan core protein (HSPG) (Perlecan)
gi|115269 Collagen alpha 1(I) chain #
gi|115306 Collagen alpha 1(III) chain ┼#
gi|13878903 Collagen alpha 1(VI) chain ┼#
gi|62901508 Collagen alpha 1(XIV) chain (Undulin)
gi|728996 Collagen alpha 1(XV) chain
gi|45644997 Collagen alpha 1(XVIII) chain
gi|8039779 Collagen alpha 2(I) chain #
gi|115349 Collagen alpha 2(IV) chain §#
gi|27808647 Collagen alpha 2(VI) chain ┼#
gi|5921193 Collagen alpha 3(VI) chain ┼
gi|62510689 EGF-containing fibulin-like extracellular matrix protein 1 (Fibulin-3)
gi|2506872 Fibronectin (FN) (Cold-insoluble globulin)
gi|47115668 Galectin-3 binding protein (Lectin galactoside-binding soluble 3 binding protein)
gi|121116 Gelsolin (Actin-depolymerizing factor) (ADF) (Brevin) §┼# gi|20141592 Laminin alpha-4 chain
gi|126366 Laminin beta-1 chain (Laminin B1 chain) §
Chapter 2
78
gi|126369 Laminin gamma-1 chain (Laminin B2 chain) §
gi|62298084 Matrilin-2
gi|2506403 Microfibril-associated glycoprotein 4
gi|128199 Nidogen (Entactin) §┼
gi|52783472 Spondin-1 (F-spondin) (Vascular smooth muscle cell growth promoting factor)
gi|3915888 Tenascin (TN) (Hexabrachion) (Cytotactin) (Neuronectin) (GMEM)
gi|135717 Thrombospondin-1 §
gi|549136 Thrombospondin-2
gi|2498193 Transforming growth factor-beta induced protein IG-H3 (Beta IG-H3)
gi|2506816 Versican core protein (Large fibroblast proteoglycan) Immune function
gi|115205 Complement C1s subcomponent (C1 esterase) §
gi|38257345 Complement C2 (C3/C5 convertase) §
gi|116594 Complement C3 ┼#
gi|20141171 Complement C4 §
gi|61252057 Complement component C7
gi|584908 Complement factor B (C3/C5 convertase) (Properdin factor B) §
gi|3915626 Complement factor D (C3 convertase activator) (Properdin factor D) (Adipsin) §┼#
gi|48428995 Lysozyme C (1,4-beta-N-acetylmuramidase C)
Involved in degradation
gi|116856 72 kDa type IV collagenase (Matrix metalloproteinase-2) (MMP-2) §┼#
gi|112911 Alpha-2-macroglobulin (Alpha-2-M)
gi|115711 Cathepsin B (Cathepsin B1) (APP secretase) §
gi|115717 Cathepsin D §
gi|115741 Cathepsin L (Major excreted protein) (MEP)
gi|544413 Chitinase-3 like protein 1 (Cartilage glycoprotein-39)
gi|116852 Interstitial collagenase (Matrix metalloproteinase-1) (MMP-1) (Fibroblast collagenase)
gi|116863 Matrix metalloproteinase-9 (MMP-9) (92 kDa type IV collagenase) (92 kDa gelatinase) §
gi|135850 Metalloproteinase inhibitor 1 (TIMP-1) (Erythroid potentiating activity) (EPA) §
gi|6919941 Procollagen C-proteinase enhancer protein (PCPE) #
Other functions
gi|119576 Liver carboxylesterase 1 (Acyl coenzyme A:cholesterol acyltransferase)
gi|585223 Plasma glutathione peroxidase (GSHPx-P)
gi|61221730 Protein C19orf10 (Stromal cell-derived growth factor SF20) (Interleukin-25) #
gi|2507461 Protein disulfide-isomerase A3 (Disulfide isomerase ER-60) (ERp60)
gi|2507460 Protein disulfide-isomerase (PDI) (Prolyl 4-hydroxylase beta subunit)
gi|136191 Serotransferrin (Transferrin) (Siderophilin) (Beta-1-metal binding globulin) § * Signal peptide predicted by SignalP (24) but also transmembrane helices predicted by TMHMM Server (25) (more than one helices or one helix located more than 40 amino acids away from the N-terminus of the protein). § Secreted proteins also identified in (19). # Secreted proteins also identified in (18). ┼ Secreted proteins also identified in (17).
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79
Table 2. Secreted proteins (classical pathway; signal predicted by SignalP) that did not incorporate the label. They were identified after SDS-PAGE fractionation and tryptic digest analysis by LC-MS/MS (≥99% confidence).
* Signal peptide predicted by SignalP (24) but also transmembrane helices predicted by TMHMM Server (25) (more than one helices or one helix located more than 40 amino acids away from the N-terminus of the protein). § Secreted proteins also identified in (19). # Secreted proteins also identified in (18). ┼ Secreted proteins also identified in (17).
ACCESSION NO. PROTEIN NAME
NON-LABELED PROTEINS. IDENTIFICATION BASED ON ONLY –ARG ENDING PEPTIDES
gi|12643637 ADAMTS-4 (A disintegrin and metalloproteinase with thrombospondin motifs 4)
gi|416746 Azurocidin (Cationic antimicrobial protein CAP37) (Heparin-binding protein)
gi|23396490 Calsyntenin-1 *
gi|12643324 Cathepsin Z gi|116533 Clusterin (Complement-associated protein SP-40,40) (Complement cytolysis inhibitor) §
gi|3023630 Cystatin C §┼
gi|462007 Dermatopontin (Tyrosine-rich acidic matrix protein) (TRAMP)
gi|37537873 EMILIN 1 (Elastin microfibril interface-located protein 1)
gi|134635 Extracellular superoxide dismutase [Cu-Zn] (EC-SOD) # gi|30581038 Fibulin-1
gi|41017299 Latent transforming growth factor-beta-binding protein 2 (LTBP-2)
gi|126279 Leukemia inhibitory factor (LIF) (Differentiation-stimulating factor) (D factor)
gi|119292 Leukocyte elastase (Neutrophil elastase) (PMN elastase) (Bone marrow serine protease)
gi|20141203 Monocyte differentiation antigen CD14 (Myeloid cell-specific leucine-rich glycoprotein) §
gi|129825 Myeloperoxidase (MPO)
gi|8928569 Nidogen-2 (NID-2) (Osteonidogen) gi|50400889 Olfactomedin-like protein 1
gi|1346908 Pentraxin-related protein PTX3 (Pentaxin-related protein PTX3)
gi|62900717 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 3 (Lysyl hydroxylase 3) (LH3)
gi|41017497 Prostaglandin-H2 D-isomerase (Lipocalin-type prostaglandin-D synthase)
gi|2501205 Protein disulfide-isomerase A6 (Protein disulfide isomerase P5)
gi|129283 SPARC (Secreted protein acidic and rich in cysteine) (Osteonectin) §#┼ gi|52783469 Spondin-2 (Mindin)
gi|3334154 Stanniocalcin 1 (STC-1)
gi|1351316 Tumor necrosis factor-inducible protein TSG-6 (TNF-stimulated gene 6 protein)
gi|13432109 Vascular endothelial-cadherin (VE-cadherin) (Cadherin-5) (7B4 antigen) (CD144 antigen) *
NON LABELED PROTEINS. IDENTIFICATION BASED ON –LYS ENDING PEPTIDES gi|57015285 Citrate synthase, mitochondrial precursor gi|3182940 Collagen alpha 1(XII) chain
gi|12643876 Fibulin-5 (FIBL-5) (Developmental arteries and neural crest EGF-like protein)
gi|6175096 Lactotransferrin (Lactoferrin)
gi|1708851 Laminin beta-2 chain (S-laminin) (Laminin B1s chain)
gi|20141464 Lumican (Keratan sulfate proteoglycan lumican) (KSPG lumican)
gi|2501336 Membrane copper amine oxidase (Vascular adhesion protein-1) (VAP-1) gi|129078 Mimecan (Osteoglycin) (Osteoinductive factor)
gi|1171700 Neutrophil gelatinase-associated lipocalin (NGAL) (P25) ┼
gi|51701718 Plexin B2 (MM1)
gi|113576 Serum albumin §
gi|9087217 Tenascin-X (TN-X)
Chapter 2
80
As an example, figure 5 shows two MS spectra obtained for two different proteins,
endoplasmin (figure 5A) and PAI-1 (figure 5B). The mass shift was clearly seen in both cases
(+4 m/z), although the different relative peak intensities for the non-labeled and labeled
peptides points to different incorporation rates of the label for these two proteins. Figure 5C
shows the MS/MS spectra from both the original and the labeled peptides from PAI-1, where
also a mass shift of +8 m/z was obvious.
Figure 5. Examples of different incorporation ratio in labeled proteins. (A) TOF-MS spectra of endoplasmin peptide; (B) TOF-MS spectra of PAI-1 peptide; (C) MS/MS spectra of both original and labeled peptide in PAI-1.
Secreted proteins were classified into groups with similar functions (table 1). For this,
information was obtained from Swiss-Prot and SOURCE (http://source.stanford.edu)
databases. The criteria for classification are not always uniform since many proteins have
more than one function which may place them in more than one functional category. In this
case, what was considered as the main function of the protein was used for classification in
ELISNASDALDK
638 640 642 644 646
m/z
0
20
40
60
80
100
120
140
160
Inte
nsity
, co
unts
638.35
638.85
639.36642.36
642.86639.86643.36
517.0 519.0 521.0 523.0
m/z
0
40
80
120
160
200
240
280
Inte
nsity
, co
unts
521.81
517.81
522.32518.31
522.82518.81
523.31519.31
FIINDWVK
A
B100 200 300 400 500 600 700 800
m/z
0
10
20
30
40
50
Inte
nsi
ty, c
ount
s
233.17
120.08
782.45
261.17 669.36343.15254.21 501.26
529.27
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800m/z
2.0
6.0
10.0
14.0
18.0
22.0
Inte
nsi
ty, c
ount
s
233.17
120.08
774.45343.18
261.16 661.39501.29529.26
C
Chapter 2
81
one of the five categories: Signalling/regulatory, Extra-cellular matrix, Immune function,
Degradation and Other.
In supplementary data table A, non-secreted proteins (no signal peptide predicted by SignalP
according to SecretomeP) and non-classically secreted proteins (no signal peptide predicted
by SignalP but NN-Score>0.6, marked as °) are shown. In supplementary data table B,
classically secreted proteins identified with confidence between 95% and 99% are shown.
Discussion
Adipose tissue is recognized as an important organ for the regulation of the whole-body
energy metabolism through the secretion of adipokines. Although, major adipokines such as
leptin and adiponectin have been shown to be produced by adipocytes, also other cell types
that are part of the adipose tissue produce adipokines or influence production of adipokines
by adipocytes. Fain et al. (28) showed that the majority of adipokines, measured in their
study, was released by non-adipocyte cells in the tissue. This fact points out the relevance of
evaluating the adipose tissue secretome rather than the adipocyte cell secretome. In this paper
we describe the first proteomics study on the adipose tissue secretome. A major issue in
characterizing the adipose tissue secretome is that protein composition of the adipose tissue
culture media is highly dependent on the way the tissue culture is performed. This not only
has implications for proteomics studies but also for other studies where adipose tissue culture
is used to study individual adipokines by ELISA, since these peptide and protein hormones
are also present in serum and in the intracellular protein fraction which, as we demonstrate,
are the main sources of secretome contamination in adipose tissue culture. Therefore, we
established a tissue culture protocol that minimizes contamination of the secretome with
serum-derived and intracellular proteins. After analyses of five different culture set-ups we
conclude that a tissue culture protocol with one wash after the first hour in culture and two or
three additional washes after overnight culture within a period of eight hours followed by a
48 to 114 hrs incubation period (set-ups D and E, figure 1) provides the optimal culture
protocol to obtain a high quality sample for secretome analyses. With this set-up, total protein
concentration was reduced from approximately 17 µg/ml to around 4 µg/ml (set-ups D and E)
at the end of the culture period (figure 2). This reduction is considerably higher than that
observed for set-ups A and B (from about 25 µg/ml to about 18 µg/ml). Albumin and
hemoglobin (serum contaminating proteins) seemed to be highly reduced in set-ups D and E
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82
(figure 4.3) and the dynamic range of the sample has decreased considerably (figures 3 and
4.2) compared to set-ups A and B.
This new improved culture protocol was used to characterize the human adipose tissue
secretome. Proteins were identified and the presence of a signal peptide was investigated in
order to distinguish those that are secreted from intracellular proteins in the media, derived
from damaged cells. Furthermore, a metabolic labelling approach was designed to
differentiate between secreted proteins derived from adipose tissue and serum proteins that
still may be present in low levels as a contaminant of the sample. Proteins that contained a
signal peptide and incorporated the label were considered as genuinely secreted by adipose
tissue and not coming from an external source (serum, intracellular origin). With this
strategy, different incorporation rates of the label into the proteins were noticed, which can be
related to different turn-over rates. In summary, a total of 297 proteins were identified with
≥95% confidence. 259 proteins were identified with ≥99%, among which 108 secreted
proteins (tables 1 and 2). Out of 108 secreted proteins, 70 proteins incorporated the label
(table 1), i.e. adiponectin, adipsin, gelsolin, macrophage colony stimulating factor-1 (M-
CSF), pigment epithelium-derived factor (PEDF), plasma retinol binding protein (RBP),
plasminogen activator inhibitor-1 (PAI-1), among others. Interleukin-6 was also detected,
showing a clear incorporation of the label, although identified with ≥95% confidence,
(supplemental data, table B). Adipocyte fatty acid-binding protein (A-FABP) was identified
as non-classically secreted protein (supplemental data, table A) with label incorporation,
agreeing with a recent study by Xu et al. (29) where they claimed that, A-FABP is a
circulating biomarker released from adipocytes into the bloodstream, closely associated with
obesity and metabolic syndrome. For 38 proteins containing a signal peptide the label could
not be detected (table 2). However, 26 of these were identified with peptides ending on an
arginine in the C-terminus (no lysine present), which implies that it is still possible that these
proteins are secreted by adipose tissue although label incorporation could not be confirmed
(i.e. leukemia inhibitory factor (LIF), pentraxin-related protein PTX3, SPARC). Finally, the
label could not be detected in a group of 12 proteins, even though their peptides showed a
lysine in the C-terminus. This fact may be due to a low incorporation rate or because the
protein was derived from serum, e.g. albumin is part of this latter group (table 2).
In supplemental data, table A, non-secreted proteins (intracellular) or proteins predicted to be
secreted via a non-classical pathway are shown (151 proteins in total). Non-secreted proteins,
such as actin, histones, catalase and proteasome subunits, are intracellular proteins that may
Chapter 2
83
be derived from damaged cells in culture or from cleaved fragments of membrane proteins.
This, together with the fact that albumin was still detected, indicates that although with the
improved tissue culture set-up sample quality has improved considerably, it was not
completely free of (high-abundance) serum and intracellular proteins. Nevertheless, the
dynamic range of the sample improved considerably allowing a much more sensitive
secretome identification. The level of sensitivity that was reached with the described
procedure was in the low ng/ml range since adiponectin, which was identified with ≥99%
confidence, reached concentrations of around 10-20 ng/ml at the end of the culture period, as
determined by ELISA in similar experiments.
Three studies have been published thus far on the adipocyte cell secretome. We compared the
results of those studies with our results on the human visceral adipose tissue secretome. Chen
et al. (19) identified 84 proteins secreted by isolated rat adipocytes cells using 2DLC-
MS/MS. Of these proteins 29 were also identified in the present study (as indicated in tables
1 and 2). Wang et al. (18) identified 27 proteins that were secreted during mouse 3T3-L1
adipocyte cell differentiation by 2DE-MS(/MS). 16 proteins were also identified in our study
(as indicated in tables 1 and 2). Out of 20 secreted proteins identified by Kratchmarova et al.
during differentiation of 3T3-L1 preadipocytes by SDS-PAGE and LC-MS/MS (17), 13 were
also found in this study (as indicated in tables 1 and 2). Only five proteins are shared between
the three adipocyte cell studies and the present study on adipose tissue. These proteins are
adiponectin, SPARC, gelsolin, adipsin, and MMP-2. 68 proteins (classically secreted)
identified in the present study were not found in the other three studies. This may be
explained by the different starting materials (cells or tissue), the different proteomics
approaches that were followed and the different origins of the material (rat, mouse, human).
As mentioned before, although the adipocyte is the major cell type in adipose tissue, this
study shows that a substantial number of secreted proteins by the tissue are released by other
cell types such as macrophages and endothelial cells. In particular, some of the secreted
proteins identified in the present study were also identified in human macrophages by a 2D
gel approach (30). That is the case for endoplasmin, gelsolin, protein disulfide isomerase,
protein disulfide isomerase A3, calreticulin, cathepsin D and peroxiredoxin 4, among others.
In the same way, pentraxin-related protein 3 (PTX3) mRNA is expressed in the stromal-
vascular fraction of adipose tissue but not in fully differentiated adipocytes; it plays a role in
the regulation of resistance to pathogens and inflammatory reactions and the PTX3 gene can
be induced in adipocytes by TNF-α (31). Taken into account what has been reported
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84
previously (1, 7, 32-35) on proteins secreted by adipose tissue, we estimate that in this study
we identified 48 new proteins as being secreted by adipose tissue.
The functional classification of the identified secreted proteins, as shown in table 1, indicates
that 39% of the proteins are involved in the modulation of the extra-cellular matrix. An
example from this group is perlecan. It is a secreted proteoglycan, widely distributed as part
of the basement membrane that may bind lipoprotein lipase (LPL) and is localized close to
the cell surface, so that it can participate in triacylglycerol hydrolysis (36). Another example
from this group is versican core protein which plays a role in intercellular signaling and in
connecting cells with the extra-cellular matrix. It may also take part in the regulation of cell
motility, growth and differentiation (37). The second largest group (27%) consists of proteins
involved in signaling and regulation. Macrophage colony stimulating factor (MCSF) is part
of this group. It promotes human adipose tissue hyperplasia, it is up-regulated under
conditions favouring adipose tissue growth (obesity) and it is down-regulated by TNF-α (38).
Another example is neuropilin-1, which is expressed by adipocytes and is involved in
regulation of angiogenesis (39). Proteins classified as being involved in degradation (14%)
are e.g. cathepsin B, and D. These are lysosomal proteases but they can also be secreted. The
fact that a large part of the identified proteins is involved in the modulation of extra-cellular
matrix, protein degradation and regulation of cellular processes indicates that adipose tissue
is a very actively dividing tissue which is probably related to the demand to store energy in
the form of triglycerides. Therefore, the tissue has to be flexible to increase or decrease
storage capacity.
In conclusion, adipose tissue culture set-up has strong influence on the quality of the sample
of detection of secreted proteins. We show here that proteins secreted from adipose tissue can
be unequivocally identified by a qualitative labeling approach which allows distinguishing
the source of relevant proteins. For the future it will be interesting to compare adipose tissue
secretomes from lean and obese people to determine differences in protein expression which
may lead to the discovery of mechanisms involved in insulin resistance and type 2 diabetes.
For this, a quantitative labeling approach should be developed. We are currently working on
this topic.
Acknowledgements
This work was supported by the Netherlands Proteomic Centre (project 6.3.)
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85
References
1. Gimeno, R.E., and Klaman, L.D. (2005) Adipose tissue as an active endocrine organ: recent advances. Curr. Opin. Pharmacol. 5, 122-128.
2. Hutley, L., and Prins, J.B. (2005) Fat as an endocrine organ: relationship to the metabolic syndrome. Am. J. Med. Sci. 330, 280-289.
3. Rajala, M.W., and Scherer, P.E. (2003) Minireview: The adipocyte--at the crossroads of energy homeostasis, inflammation, and atherosclerosis. Endocrinology 144, 3765-3773.
4. Matsuzawa, Y. (2006) The metabolic syndrome and adipocytokines. FEBS Lett. 580, 2917-2921. 5. Permana, P.A., Menge, C., and Reaven, P.D. (2006) Macrophage-secreted factors induce adipocyte
inflammation and insulin resistance. Biochem. Biophys. Res. Commun. 341, 507-514. 6. Wellen, K.E., and Hotamisligil, G.S. (2003) Obesity-induced inflammatory changes in adipose tissue. J.
Clin. Invest. 112, 1785-1788. 7. Wellen, K.E., and Hotamisligil, G.S. (2005) Inflammation, stress, and diabetes. J. Clin. Invest. 115, 1111-
1119. 8. Xu, H., Barnes, G.T., Yang, Q., Tan,G., Yang, D., Chou, C.J., Sole, J., Nichols, A., Ross, J.S., Tartaglia,
L.A., and Chen, H. (2003) Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J. Clin. Invest. 112, 1821-1830.
9. Scherer, P.E. (2006) Adipose tissue. From lipid storage compartment to endocrine organ. Diabetes 55, 1537-1545.
10. Corton, M., Villuendas, G., Botella, J.I., San Millan, J.L., Escobar-Morreale, H.F., and Peral, B. (2004) Improved resolution of the human adipose tissue proteome at alkaline and wide range pH by the addition of hydroxyethyl disulfide. Proteomics 4, 438-441.
11. Lanne, B., Potthast, F., Hoglund, A., Brockenhuus von Lowenhielm, H., Nystrom, A.C., Nilsson, F., and Dahllof, B. (2001) Thiourea enhances mapping of the proteome from murine white adipose tissue. Proteomics 1, 819-828.
12. Schmid, G.M., Converset, V., Walter, N., Sennitt, M.V., Leung, K.Y., Byers, H., Ward, M., Hochstrasser, D.F., Cawthorne, M.A., and Sanchez, J.C. (2004) Effect of high-fat diet on the expression of proteins in muscle, adipose tissues, and liver of C57BL/6 mice. Proteomics 4, 2270-2282.
13. Renes, J., Bouwman, F., Noben, J.-P., Evelo, C., Robben, J., and Mariman, E. (2005) Protein profiling of 3T3-L1 adipocyte differentiation and (tumor necrosis factor α-mediated) starvation. Cell. Mol. Life Sci. 62, 492-503.
14. Hansson, O., Strom, K., Guner, N., Wierup, N., Sundler, F., Hoglund, P., and Holm, C. (2006) Inflammatory response in white adipose tissue in the non-obese hormone-sensitive lipase null mouse model. J. Proteome Res. 5, 1701-1710.
15. Barcelo-Batllori, S., Corominola, H., Claret, M., Canals, I., Guinovart, J., and Gomis, R. (2005) Target identification of the novel antiobesity agent tungstate in adipose tissue from obese rats. Proteomics 5, 4927-4935.
16. Celis, J.E., Moreira, J.M., Cabezon, T., Gromov, P., Friis, E., Rank, F., and Gromova, I. (2005) Identification of extracellular and intracellular signaling components of the mammary adipose tissue and its interstitial fluid in high risk breast cancer patients: toward dissecting the molecular circuitry of epithelial-adipocyte stromal cell interactions. Mol. Cell. Proteomics 4, 492-522.
17. Kratchmarova, I., Kalume, D.E., Blagoev, B., Scherer, P.E., Podtelejnikov, A.V., Molina, H., Bickel, P.E., Andersen, J.S., Fernandez, M.M., Bunkenborg, J., Roepstorff, P., Kristiansen, K., Lodish, H.F., Mann, M., and Pandey, A. (2002) A proteomic approach for identification of secreted proteins during the differentiation of 3T3-L1 preadipocytes to adipocytes. Mol. Cell. Proteomics 1, 213-222.
18. Wang, P., Mariman, E., Keijer, J., Bouwman, F., Noben, J.P., Robben, J., and Renes, J. (2004) Profiling of the secreted proteins during 3T3-L1 adipocyte differentiation leads to the identification of novel adipokines. Cell. Mol. Life Sci. 61, 2405-2417.
19. Chen, X., Cushman, S.W., Pannell, L.K., and Hess, S. (2005) Quantitative proteomic analysis of the secretory proteins from rat adipose cells using a 2D liquid chromatography-MS/MS approach. J. Proteome Res. 4, 570-577.
Chapter 2
86
20. Halleux, C.M., Takahashi, M., Delporte, M.L., Detry, R., Funahashi, T., Matsuzawa, Y., and Brichard, S.M. (2001) Secretion of adiponectin and regulation of apM1 gene expression in human visceral adipose tissue. Biochem. Biophys. Res. Commun. 288, 1102-1107.
21. Chertov, O., Simpson, J.T., Biragyn, A., Conrads, T.P., Veenstra, T.D., and Fisher, R.J. (2005) Enrichment of low-molecular-weight proteins from biofluids for biomarker discovery. Expert Rev. Proteomics 2, 139-145.
22. Fried, S.K., and Moustaid-Moussa, N. (2001) Culture of adipose tissue and isolated adipocytes. Methods Mol. Biol. 155,197-212.
23. Tang, W.H., Halpern, B.R., Shilov, I.V., Seymour, S.L., Keating, S.P., Loboda, A., Patel, A.A., Schaeffer, D.A., and Nuwaysir, L.M. (2005) Discovering known and unanticipated protein modifications using MS/MS database searching. Analytical Chemistry 77, 3931-3946.
24. Bendtsen, J.D., Jensen, L.J., Blom, N., von Heijne, G., and Brunak, S. (2004) Feature based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel. 17, 349-356.
25. Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E.L.L. (2001) Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. Journal of Molecular Biology 305, 567-580.
26. Peri, S., Navarro, J.D., Amanchy, R., Kristiansen, T.Z., Jonnalagadda, C.K.,Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T.K., Gronborg, M., Ibarrola, N., Deshpande, N., Shanker, K., Shivashankar, H.N., Rashmi, B.P., Ramya, M.A., Zhao, Z., Chandrika, K.N., Padma, N., Harsha, H.C., Yatish, A.J., Kavitha, M.P., Menezes, M., Choudhury, D.R., Suresh, S., Ghosh, N., Saravana, R., Chandran, S., Krishna, S., Joy, M., Anand, S.K., Madavan, V., Joseph, A., Wong, G.W., Schiemann, W.P., Constantinescu, S.N., Huang, L., Khosravi-Far, R., Steen, H., Tewari, M., Ghaffari, S., Blobe, G.C., Dang, C.V., Garcia, J.G., Pevsner, J., Jensen, O.N., Roepstorff, P., Deshpande, K.S., Chinnaiyan, A.M., Hamosh, A., Chakravarti, A., and Pandey, A. (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Research 13, 2363-2371.
27. Kratzer, R., Eckerskorn, C., Karas, M., and Lottspeich, F. (1998) Suppression effects in enzymatic peptide ladder sequencing using ultraviolet – matrix assisted laser desorption/ionization – mass spectrometry. Electrophoresis 19, 1910-1919.
28. Fain, J.N., Madan, A.K., Hiler, M.L., Cheema, P., and Bahouth, S.W. (2004) Comparison of the release of adipokines by adipose tissue, adipose tissue matrix, and adipocytes from visceral and subcutaneous abdominal adipose tissues of obese humans. Endocrinology 145, 2273-2282.
29. Xu, A., Wang, Y., Xu, J.Y., Stejskal, D., Tam, S., Zhang, J., Wat, N.M.S., Wong, W.K., and Lam, K.S.L. (2006) Adipocyte fatty acid-binding protein is a plasma biomarker closely associated with obesity and metabolic syndrome. Clinical Chemistry 52, 405-413.
30. Dupont, A., Tokarski, C., Dekeyzer, O., Guihot, A., Amouyel, P., Rolando, C., and Pinet, F. (2004) Two-dimensional maps and databases of the human macrophage proteome and secretome. Proteomics 4, 1761-1778.
31. Abderrahim-Ferkoune, A., Bezy, O., Chiellini, C., Maffei, M., Grimaldi, P., Bonino, F., Moustaid-Moussa, N., Pasqualini, F., Mantovani, A., Ailhaud, G., and Amri, E. (2003) Characterization of the long pentraxin PTX3 as a TNF-α-induced secreted protein of adipose cells. J. Lipid Res. 44, 994-1000.
32. Kershaw, E.E., and Flier, J.S. (2004) Adipose tissue as an endocrine organ. J. Clin. Endocrinol. Metab. 89, 2548-2556.
33. Krug, A.W., and Ehrhart-Bornstein, M. (2005) Newly discovered endocrine functions of white adipose tissue: possible relevance in obesity-related diseases. Cell. Mol. Life Sci. 62, 1359-1362.
34. Hauner, H. (2005) Secretory factors from human adipose tissue and their functional role. Proceedings of the Nutrition Society 64, 163-169.
35. Trayhurn, P. (2005) Endocrine and signalling role of adipose tissue: new perspectives on fat. Acta Physiol Scand 184, 285-293.
36. Wilsie, L.C., Chanchani, S., Navaratna, D., and Orlando, R.A. (2005) Cell surface heparan sulfate proteoglycans contribute to intracellular lipid accumulation in adipocytes. Lipids in Health and Disease 4, 1-15.
Chapter 2
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37. Rahmani, M., Wong, B.W., Ang, L., Cheung, C.C., Carthy, J.M., Walinski, H., and McManus, B.M. (2006) Versican: signaling to transcriptional control pathways. Can. J. Physiol. Pharmacol. 84, 77-92.
38. Levine, J.A., Jensen, M.D., Eberhardt, N.L., and O’Brien, T. (1998) Adipocyte macrophage colony-stimulating factor is a mediator of adipose tissue growth. J. Clin. Invest. 101, 1557-1564.
39. Belaid, Z., Hubint, F., Humblet, C., Boniver, J., Nusgens, B., and Defresne, M. (2003) Differential expression of vascular endothelial growth factor and its receptors in hematopoietic and fatty bone marrow: evidence that neuropilin-1 is produced by fat cells. Haematologica 90, 400-401.
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Section II
Role of adipose tissue in the development of
insulin resistance
Chapter 3
89
Chapter 3
Sub-chronic administration of stable GIP
analogue in mice decreases serum LPL activity
and body weight
Ewa Szalowska
Kees Meijer
Niels Kloosterhuis
Farhad Razaee
Marion Priebe
Roel J. Vonk
Peptides 2011
Chapter 3
90
Abstract
GIP receptor knockout mice were shown to be protected from the development of obesity on
a high fat diet, suggesting a role of GIP in the development of obesity.
Our aims were to test the hypothesis if excess of GIP could accelerate development of obesity
and to identify GIP gene targets in adipose tissue.
Mice were kept on a chow or a high fat diet. During the last 2 weeks of the experiment mice
were injected with D-Ala2-GIP or PBS. Serum LPL activity and several biochemical
parameters (TG, FFA, cholesterol, glucose, insulin, resistin, IL-6, IL-1b, TNFa, GIP) were
measured. Fat tissue was isolated and QPCR was performed for a set of genes involved in
energy metabolism (LPL, GLUT4, FAS, SREBP1c) and inflammation (IL-6, IL-1b, TNFa).
A DNA Microarray was used to identify GIP gene targets in adipose tissue of the chow diet
group.
D-Ala2-GIP injection caused a significant decrease in both body weight and LPL activity
compared to PBS-injected animals. Serum biochemical parameters were not affected by D-
Ala2-GIP, with an exception for resistin and insulin. The set of inflammatory genes were
significantly decreased in adipose tissue in the D-Ala2-GIP injected animals on a chow diet.
A DNA microarray revealed that APO-genes and CYP- genes were affected by GIP treatment
in adipose tissue.
D-Ala2-GIP injections caused body weight loss related to decreased serum insulin level and
LPL activity. The identified GIP candidate gene targets in adipose tissue link GIP action to
lipid metabolism exerted by APO and CYP genes.
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91
Introduction
Glucose-dependent insulinotropic polypeptide (GIP) is one of gastrointestinal hormones
involved in the regulation of postprandial nutrient homeostasis 3. In response to glucose or
fat, GIP is secreted from K-cells in the proximal small intestine (duodenum and jejunum);
however only in the presence of glucose GIP stimulates insulin secretion and acts as
incretine. GIP acts via G-coupled GIP receptors (GIPR) which are expressed in pancreas,
adipose tissue, gastrointestinal tract, lung, brain, and bone. GIP is recognized as an anti-
diabetic hormone, since it acts on pancreatic beta cells and stimulates beta-cells proliferation,
growth, differentiation, and protects from apoptosis. Additionally GIP was shown to enhance
bone formation via stimulation of osteoblast proliferation and inhibition of apoptosis and it
may play a role in the central nervous system 2. Recent evidence indicates that GIP has an
emerging role in the development of obesity. The GIP receptor (GIPR) knockout mice on a
high fat diet were protected on a high fat diet from obesity, suggesting that GIP might
promote energy storage 20. The proposed mechanism behind the GIP-dependent fat deposition
in adipose tissue is an increase in lipoprotein lipase (LPL) activity leading to triglyceride
(TG) accumulation, shown in 3T3-L1 cells and human adipocytes 14. LPL is known to be
produced by many tissues such as adipose tissue, cardiac and skeletal muscles, macrophages,
and islets. Insulin is known to stimulate LPL activity, leading to accumulation of LPL-
catalyzed reaction products, fatty acids, and monoacylglycerol and partially taken up by the
tissues locally and stored as neutral lipids in adipose tissue, oxidized, or stored in skeletal and
cardiac muscles or as cholesteryl ester and TG in macrophages 29. Despite the evidence that
GIP activates LPL activity in vitro resulting in accumulation of TG in adipocytes, it was not
shown that infusion of the DPPIV-resistant GIP would lead to increased fat mass in vivo 13.
In addition, to the emerging role of GIP in the development in obesity, there is an interest to
use GIP for treatment of type 2 diabetes. However, one of factors limiting the use of native
GIP peptide is its short half-time in blood caused by rapid degradation by dipeptidyl
peptidase IV (DPPIV) and renal filtration. The use of GIP DPPIV stable analogues can
overcome these obstacles. One of these analogues is D-Ala2-GIP, which is equipotent to the
native GIP peptide and was shown to improve glucose tolerance in normal and obese diabetic
rats 11. In another study the D-Ala2-GIP was shown to increase serum resistin and leptin
levels in C57BL/6 mice fed a high fat diet, however no changes in body fat mass were
observed 18.
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In our studies we aimed to test the hypothesis, if excess GIP could accelerate development of
obesity on both chow and high-fat diets. Mice were intraperitoneally injected with D-Ala2-
GIP sub-chronically for 2 weeks and subsequently analyzed for body weight, various
biochemical serum parameters and subsets of metabolic and proinflammatory genes in
adipose tissue. Additionally we aimed to identify GIP gene targets in adipose tissue by means
of DNA microarray analysis in order to better understand how adipose tissue under GIP
action could influence the total body energy metabolism.
Materials and Methods
Animals and diets
Male C57BL/6 mice, age 8 weeks, were housed in a light- and temperature-controlled facility
(light on 7AM-7PM, 21ºC). The mice were fed standard laboratory chow ad libitum, n=20
(Harlan, The Netherlands or the high fat diet (HFD) ad libitum, n=20 (Harlan, The
Netherlands) and had free access to water for 11 weeks. In the last 2 weeks of the
experiments half of the animals from each diet group were intraperitoneally (ip) injected
with D-Ala2-GIP (0.12mg/kg) or PBS daily at 11AM. All experiments were approved by the
Ethics Committee for Animal Experiments of the University of Groningen.
GIP synthesis and sequencing
D-Ala2-GIP was synthetized by Solid phase Fmoc (N-(9-fluorenyl)methoxycarbonyl)
chemistry using a MilliGen 9050 peptide synthesizer (MilliGen/Biosearch, Bedford, MA), as
described previously 21. The peptide was purified by reversed-phase HPLC on a JASCO
HPLC System (Tokyo, Japan). The peptide was dissolved on 0.1% trifluoroacetic acid and
applied on a VYDAC C18-column (218TP, 1.0x25 cm, 10-µm particles, Vydac, Hesperia,
CA) equilibrated in 0.1% trifluoroacetic acid in 20 min at a flow rate of 4 ml/min. The
peptide sequence was confirmed by Orbitrap mass spectrometry analysis.
Serum biochemical measurements
Plasma insulin, IL-6, TNFa, and resistin concentrations were determined using a multiplex
assay (MILLIPLEX MAP Mouse Serum Adipokine Panel assay, Millipore, Amsterdam, The
Netherlands). Plasma GIP level was determined using ELISA (Rat/Mouse GIP (total) ELISA
Kit, Millipore, Amsterdam, The Netherlands). For the GIP measurements, plasma was
supplemented directly after collection with DPP IV protease inhibitor according to the
manufacturer’s description (Millipore, Amsterdam, The Netherlands). Plasma NEFA, TG,
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and cholesterol concentrations were determined using commercially available kits (Roche
Diagnostics, Mannheim, Germany and Wako Chemicals, Neuss, Germany).
LPL serum measurement
LPL in serum was measured using a LPL activity assay kit (Roar Biomedical Inc.NY, USA)
according to the manufacturer’s protocols.
RNA isolation and cDNA synthesis
RNA was extracted from adipose tissue using RNeasy Lipid Tissue Mini Kit (Qiagen, Venlo,
The Netherlands) according to the manufacturer’s instructions. RNA extraction from livers
was performed using Tri reagent (Sigma-Aldrich, St. Louis, MO). The RNA concentration
was determined by Nano Drop ND-1000 Spectrophotometer (Isogen IJsselstein, The
Netherlands). The quality of total RNA from adipose tissue was evaluated by capillary
electrophoresis using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, Calif.).
cDNA synthesis was performed from total RNA with QuantiTect Reverse Transcription Kit
(Qiagen, Venlo, The Netherlands) according to the manufacturer’s instructions.
Quantitative Real-Time PCR (QPCR)
Expression of the genes of interest was quantified by QPCR on an ABI prism 7900 HT
(Applied Biosystems) with the following cycling conditions: 15 min 95°C followed by 40
cycles of 15s 95 ºC and 1 min 60 ºC. Reactions were performed in 10 µl and contained 20 ng
cDNA, 1x TaqMan PCR Master Mix (Applied Biosystems, Foster City, CA), 250 nM of each
probe and 900 nM of each primer. For each gene, a standard curve was generated and
efficiency of the primers was determined as described previously 22. For each primer pair the
efficiency was about 95 %. Specific primer sets for actin β (ACTB), IL-1β, IL-6, TNFα,
lipoprotein lipase (LPL), solute carrier family 2 (facilitated glucose transporter) member 4
(GLUT4), sterol regulatory element binding protein 1c (SREBP1C), and fatty acid synthase
(FAS) were developed with Primer Express 1.5 (Applied Biosystems). The sequences for
forward (F), reverse (R) primers and the probe (P) were (5’-3’): ACTB (F) AGC CAT GTA
CGT AGC CAT CCA; (R) TCT CCG GAG TCC ATC ACA ATG; (P) TGT CCC TGT ATG
CCT CTG GTC GTA CCA C; IL-1β (F) ACC CTG CAG CTG GAG AGT GT ; (R) TTG
ACT TCT ATC TTG TTG AAG ACA AAC C; (P) CCC AAG CAA TAC CCA AAG AAG
AAG ATG GAA ; IL-6 (F) CCG GAG AGG AGA CTT CAC AGA ; (R) AGA ATT GCC
ATT GCA CAA CTC TT; (P) ACC ACT TCA CAA GTC GGA GGC TTA ATT ACA;
TNFα (F); (R); (P); LPL (F) AAG GTC AGA GCC AAG AGA AGC A; (R) CCA GAA
AAG TGA ATC TTG ACT TGG T; (P) CCT GAA GAC TCG CTC TCA GAT GCC CTA
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CA; GLUT4 (F) CTC ATG GGC CTA GCC AAT G; (R) GGG CGA TTT CTC CCA CAT
AC; (P) CAT TGG CGC CTA CTC AGG GCT AAC ATC;
SREBP1c (F) GGA GCC ATG GAT TGC ACA TT; (R) CCT GTC TCA CCC CCA GCA
TA; (P) CAG CTC ATC AAC AAC CAA GAC AGT GAC TTC C;
FAS (F) GGC ATC ATT GGG CAC TCC TT; (R) GCT GCA AGC ACA GCC TCT CT; (P)
CCA TCT GCA TAG CCA CAG GCA ACC TC.
Data were analyzed with SDS 2.0 software (Applied Biosystems). For each sample, the
QPCR reaction was performed twice in triplicate and the averages of the obtained threshold
cycle values (CT) were processed for further calculations. For normalization ACTB was
used. Relative expression was calculated with ∆(∆(CT))-method 19.
Statistical analysis
QPCR experiments were performed with n=10 adipose tissue. Insulin, IL-6, TNFα, resistin,
and GIP multiplex or ELISA measurements in plasma were in technical duplicates for n=10
animals of each experimental groups. Biochemical measurements were performed in
technical duplicates for n=10 mice in each of the experimental groups. Kruskal Wallis (KW)
test was applied to compare the different experimental groups and p-value <0.05 was
considered significant.
Illumina Human WG6-v2 Microarray Analysis
The Illumina platform was used for the gene expression analysis in adipose tissue. Adipose
tissue obtained form 8 mice (4 mice fed chow diet, injected with PBS and 4 mice fed HFD
injected with D-Ala2-GIP) were used in the DNA microarray experiment. Biotin- labeled
cRNA was generated from high-quality total RNA with the Illumina TotalPrep RNA
amplification kit (Ambion). Briefly, 50 ng of total RNA was reversely transcribed with an
oligo(dT) primer containing a T7 promoter. The first- strand cDNA was used to make the
second strand. The purified second-strand cDNA, along with biotin UTPs, was subsequently
used to generate biotinylated, antisense RNA of each mRNA in an in vitro transcription
reaction. The size distribution profile for the labeled cRNA samples was evaluated by
Bioanalyzer. After RNA labeling, 1.5ug of purified, labeled cRNA from each sample was
hybridized at 55ºC overnight with a Human-8 v2 expression Illumina Beadchip targeting
22000 transcripts. The beadchip was washed the following day. Streptavidin-Cy3 was used to
develop a signal, and each chip was scanned with an Illumina Bead Array Reader.
The preprocessing of Illumina data was performed using the BeadStudio package with default
settings. The background was subtracted and quantile normalization performed. Probes with
“absent” signals in all samples (lower than or near to background levels) were removed from
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further analysis. To select the candidate targets of GIP in adipose tissue we concentrated on
genes which change at least more than 5-fold in abundance in the D-Ala2-GIP injected
animals compared to the placebo injected mice.
Results
Effect of D-Ala2-GIP on body weight during chow and high fat diet
In order to study the effect of GIP on the body mass, mice were fed with chow diet and HFD
for 11 weeks. During the first 9 weeks animals fed with the HF diet increased body weight
faster compared to the chow diet group, as expected (Figure 1).
24
26
28
30
32
34
36
38
0 2 4 6 8 10 12
bo
dy
ma
ss
(g
)
weeks
HFD GIP
HFD PBS
CHD GIP
CHD PBS
Figure 1. Mice were fed with chow and high fat diet for 11 weeks. During the last 2 weeks of the dietary interventions D-Ala2-GIP or PBS were daily, intraperitoneally injected. X axis shows time frame of the experiment in weeks. Y axis shows mice body weight in grams. Indicated p values are calculated for body weight loss between PBS and D-Ala2-GIP injected animals.
In the last two weeks (week 10 and 11) of dietary interventions D-Ala2-GIP or PBS were
injected, what resulted in body weight reduction in animals fed with both chow and HFD.
After 2 weeks of injections there was a significant difference in body weight loss between D-
Ala2-GIP and PBS injected animals both in the chow and HFD groups (p=0.008 and p=0.01
respectively) (Figure 2).
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0.0
1.0
2.0
3.0
4.0
5.0
6.0
CHD PBS CHD GIP HFD PBS HFD GIP
we
igh
t re
du
cti
on
(g
)
Figure 2. Columns in the figure represent body weight loss after 2 weeks of D-Ala2-GIP or PBS i.p. injections. On the X axis the analyzed groups of animals are depicted. The Y axis represents body weight loss in grams in the D-Ala2-GIP injected animals compared to PBS injected mice. The difference between CHD PBS and CHD GIP and HFD PBS and HFD GIP were significant (p=0.008 and p=0.01 respectively). LPL activity in serum
With the aim to identify factors which could be implicated in the body weight reduction we
analyzed serum LPL activity. The serum LPL activity was measured in both chow and HF
diet groups after 2 weeks of administration of D-Ala2-GIP or PBS. Animals injected with D-
Ala2-GIP had significantly lower LPL activity compared to the PBS injected animals in both
chow and HF diet groups (Figure 3).
0
20
40
60
80
100
120
140
CHD PBS CHD GIP HFD PBS HFD GIP
rela
tiv
e L
PL
se
rum
ac
tiv
ity
(%
)
Figure 3. The chart represents relative LPL activity (Y axis) in mice on a chow diet injected with PBS or D-Ala2-GIP, depicted on the chart as CH PBS and CH GIP respectively; and mice fed with a high fat diet injected with PBS or D-Ala2-GIP depicted on the chart as F PBS and F GIP respectively. The difference between CHD PBS and CHD GIP and HFD PBS and HFD GIP were significant (p=0.001 and p=0.009 respectively).
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Biochemical serum parameters. In order to find systemic effects of the D-Ala2-GIP we measured several biochemical serum parameters such as triglyceride (TG), cholesterol, free fatty acids (FFA), resistin, insulin, IL-6, TNFα , and GIP serum levels. We did not detect any significant effect of the D-Ala2-GIP administration on most of the indicated parameters, except for a significantly lower resistin level in the D-Ala2-GIP injected animals on the HF diet, (p=0.04),( Figure 4A); significantly lower insulin level of the D-Ala2-GIP injected animals in the chow diet group ( p=0.016 ),( Figure 4B) ; and higher GIP serum level in the D-Ala2-GIP-injected animals on the HF diet,( p=0.002),( Figure 4C). Data for parameters which did not change significantly are not shown.
0
500
1000
1500
2000
2500
3000
3500
CHD PBS CHD GIP HFD PBS HFD GIP
resi
stin
(p
g/
ml)
0
100
200
300
400
500
600
700
800
900
CHD PBS CHD GIP HFD PBS HFD GIP
insu
lin
(p
g/
ml)
0
20
40
60
80
100
120
140
160
CHD PBS CHD GIP HFD PBS HFD GIP
GIP
(p
g/
ml)
Figure 4. (A) Resistin serum levels in the animal experimental groups. The difference CHD PBS vs. HFD PBS and HFD PBS vs. HFD GIP were significant (p=0.001 and p=0.04 respectively). (B) Insulin serum levels in the animal experimental groups. The difference CHD PBS vs. CHD GIP and CHD GIP vs. HFD GIP were significant (p=0.02 and p=0.005 respectively). (C) GIP serum levels in the animal experimental groups. The difference between HFD PBS vs. HFD GIP and CHD PBS vs. HFD PBS were significant (p=0.002 and p=0.005 respectively).
Metabolic and pro-inflammatory genes expression in adipose tissue
Adipose tissue was collected after D-Ala2-GIP or PBS injections and gene expression was
analyzed by QPCR. As expected there was a significant difference between chow and HF
diets in respect to the metabolic and the pro-inflammatory gene expression; the
proinflammatory genes (IL-1β, IL-6, and TNFα) were significantly upregulated in obese
animals (Figures 5 A-C).
4A 4B
4C
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0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f IL
1b
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f TN
Fa
Figure 5. Relative gene expression of IL-1b (A), IL-6 (B), and TNFa (C) in adipose tissue (Y axis) in the experimental animals groups (x axis). The differences in RGE for IL-1b, IL-6, and TNFa between CHD and HFD groups were significant p<0.05.
The energy metabolism genes (GLUT4, LPL, SREBP1c) were significantly downregulated in
the animals fed with the HF diet (Figures 6 A-C), except for expression of FAS, which was
significantly higher in animals fed with HF diet compared to animals fed with chow diet
(Figure 6D).
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f G
LU
T4
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f L
PL
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f S
RE
BP
1c
0
0.5
1
1.5
2
2.5
3
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f F
AS
Figure 6. Relative gene expression of selected energy metabolism genes (GLUT4 (A), LPL (B), SREBP1c (C),
FAS (D) in adipose tissue (Y axis) in the experimental animals groups (x axis). The differences between the
CHD and HFD were significant (p<0.05).
The only significant effect of GIP was found for the proinflammatory genes expression (IL-
1β, IL-6, and TNFα), which had significantly decreased expression in adipose tissue in the
chow diet group, (Figures 7 A-C).
0
1
2
3
4
5
6
7
CHD PBS CHD GIP HFD PBS HFD GIP
RG
E o
f IL
6
5A 5B 5C
6A 6B
6D 6C
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99
0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
0.00035
CHD PBS CHD GIP
RG
E I
L1
b
0
0.00001
0.00002
0.00003
0.00004
0.00005
0.00006
CHD PBS CHD GIP
RG
E I
L6
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0.001
CHD PBS CHD GIP
RG
E T
NFa
Figure 7. Relative gene expression (RGE) of IL-1b (A), IL-6 (B), and TNFa (C) in adipose tissue (Y axis) in the chow diet animals (x axis), the difference is significant p=0.05, p=0.02, p=0.02 for of IL-1b, IL-6, and TNFa respectively.
Identification of GIP gene targets in adipose tissue
In order to identify candidate GIP gene targets in adipose tissue, a DNA microarray was
performed in the chow diet group. The DNA microarry analysis revealed that the most
changed (mostly upregulated) genes in adipose tissue belonged to Apo family such as
APOA1, APOA2, APON (…) and cytochrome P450 supergene family of enzymes such as
CYP1A2, CYP2A5, CYP2C37, CYP2D10 (…). Additionally, we observed upregulation of
ABC transporters, SERPINA gene family and other genes encoding for transporters:
SLC22A1, SLC27A5. The results are summarized in Table1 and Table 2.
7C
7B 7A
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Table 1. The most differentially affected genes affected by D-Ala2-GIP injections in adipose tissue of the chow diet group. The DNA microarray was performed for 4 mice injected with PBS (PBS 1-4) and compared with 4 mice injected with D-Ala2-GIP (GIP 1-4). In the table Avg. PBS and Avg. GIP stand for average signal from the DNA microarray in both groups, FC stands for fold change.
Target gene ID
PBS 1 PBS 2 PBS 3 PBS 4 Avg. PBS
GIP 1 GIP 2 GIP 3 GIP 4 Avg. GIP
FC GIP vs.
PBS
ABCG5 3.3 1.9 2.6 17 6.2 39.2 172.8 205.1 13.7 107.7 17.4
ABCG8 -2.5 -2.6 -0.2 2.8 -0.625 7.2 100.3 116.3 2 56.5 90.3
APOA1 390.8 349.2 4.5 30.1 193.65 964.5 4533.8 3378 929.9 2451.6 12.7
APOA2 343.2 261 3.6 29.9 159.43 1512.1 10053.1 7442.8 1145.4 5038.4 31.6
APOC3 85 90 10.7 20.3 51.5 388.3 3711.8 2355.5 359.6 1703.8 33.1
APOC4 93.3 79.8 92.7 23.5 72.325 227.2 1447.8 1246.3 206.8 782 10.8
APOF 16.5 7.1 3.1 0.3 6.75 60.5 452.9 207.9 28.6 187.5 27.8
APOH -6.9 9.4 7.2 -16.9 -1.8 26.3 197.6 95.2 40.5 89.9 49.9
APON -2.6 3.3 1.4 -1.7 0.1 14.5 40.4 22.7 -1.9 18.9 189.3
C9 -5.5 6.7 3.4 -3.7 0.225 29.7 104.2 71.2 8.2 53.3 237
CPS1 5.3 -1.8 -10.1 1.1 -1.375 31.6 289.3 73.9 2.8 99.4 72.3
CRP 71.1 23.5 8.4 19.9 30.725 134.3 625.1 444.1 74.3 319.5 10.4
CYP1A2 -2.7 17.2 -8 -4.9 0.4 37.7 132.3 101.3 46.3 79.4 198.5
CYP24A1 45.4 19.9 6.2 11.8 20.825 0.6 -2.5 6.5 7.2 3 -10
CYP2A5 -0.3 1.7 2.3 5.5 2.3 8.8 99.6 48.1 8.3 41.2 17.9
CYP2A5 -7.2 -1.9 -0.8 10 0.025 72.2 712.4 454 16.2 313.7 12548
CYP2C37 -0.2 1.3 -7.2 -8.2 -3.575 67.3 593.1 416.2 31.4 277 77.5
CYP2C50 -4.3 -7.9 -0.1 -13.7 -6.5 53.4 541.9 341.2 22 239.6 36.9
CYP2C70 -5.5 2.8 -7.4 -0.9 -2.75 74.4 467.2 387.6 24 238.3 86.7
CYP2D10 8.2 7.7 -8.1 -1.2 1.65 90.9 582.8 459.2 54.7 296.9 179.9
CYP2D9 2.6 11.5 6.5 8 7.15 167.7 992.5 993.8 121.5 568.9 79.6
CYP4F14 0.1 -0.4 -7.4 -0.8 -2.125 32.9 165.7 107.1 6.8 78.1 36.8
SERPINA10 -7 -6.2 -0.7 5.5 -2.1 26 136.9 83.7 6.4 63.3 30.1
SERPINA12 4.9 7.6 -4.8 -1.7 1.5 25.1 102 103.6 7 59.4 39.6
SERPINA1A 2.7 36.4 1.4 8.9 12.35 79.2 536.5 229.5 77 230.6 18.7
SERPINA1B 280.9 169.4 -1.4 50.1 124.75 1167.4 8021.5 4171 907.1 3566.8 28.6
SERPINA1C 82.8 69.8 0.2 0.1 38.225 391.5 3197.6 1573.4 391.3 1388.5 36.3
SERPINA1D 211 167.9 -3.4 50.9 106.6 1015.6 6938 3646.7 916.5 3129.2 29.4
SERPINA3M 1.9 17.6 7.2 15.6 10.575 41 216.5 109.3 19.3 96.5 9.1
SERPINA6 7.5 3.2 -8.6 0.5 0.65 13.5 109.6 54.8 4.5 45.6 70.2
SLC22A1 10 2.1 27.7 4.8 11.15 84.7 343.2 600.6 22.2 262.7 23.6
SLC27A5 1.2 0.5 -13.6 -6.6 -4.625 34.7 338.6 203.4 10.3 146.8 31.7
SLC38A3 4.7 -3.2 6.7 -7.4 0.2 19.9 144.6 77.5 5.6 61.9 309.5
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Table 2. GIP candidate target genes and their functions
Target ID Gene functional description
Full name
ABCG5
The protein encoded by this gene is a member of the superfamily of ATP-binding cassette (ABC) transporters. ABC proteins transport various molecules across extra- and intra-cellular membranes. ABC genes are divided into seven distinct subfamilies (ABC1, MDR/TAP, MRP, ALD, OABP, GCN20, and White). This protein is a member of the White subfamily. In humans, this protein functions as a half-transporter to limit intestinal absorption and promote biliary excretion of sterols; however, the function of the mouse gene has not been determined. Mutations in the human gene have been associated with sterol accumulation, atherosclerosis, and sitosterolemia.
ATP-binding cassette, sub-family G (WHITE), member 5
ABCG8 Mutations in ABCG8 cause sitosterolemia, an inborn error in metabolism characterized by high plasma sterol concentrations (16
ATP-binding cassette, sub-family G (WHITE), member 8
APOA1 APOA1 is a major component of the high-density lipoprotein complex and has anti-inflammatory effects 31 apolipoprotein A-I
APOA2 In mice, amyloidogenic type C apolipoprotein A-II (apoA-II) forms amyloid fibrils n age associated-amyloidosis 15,24 apolipoprotein A-II
APOC3
ApoC3 inhibits LPL activity. High levels of plasma ApoC3 cause hypertriglyceridemia, while the absence of ApoC3 leads to the reduced plasma TG levels, and resistance to diet induced obesity. The gene encoding for ApoC3 in liver is regulated by insulin, bile acids, retinoids, statins, and fibrates. In adipose tissue little is known about functions of ApoC3, but recently it was shown that apoC3 gene expression increases during adipogenesis and is augmented by retinoid X receptor (RXR) agonists 27 apolipoprotein A-III
APOC4
ApoC-IV overexpression may perturb lipid metabolism leading to lipid accumulation. HCV core protein may modulate ApoC-IV expression through Ku antigen and PPARgamma/RXRalpha complex in human. In mouse the role is unknown 12 apolipoprotein A-IV
APOF Overexpression of murine ApoF significantly reduced total cholesterol levels by 28%, high density lipoproteins by 27% and phospholipid levels by 19%.17 apolipoprotein F
APOH These results are compatible with a role for apolipoproteins in lipid metabolism and transport in the developing lung in association with the sex difference in surfactant lipid synthesis23 apolipoprotein H
APON Unknown apolipoprotein N
C8G Complement and coagulation cascades complement component 8, gamma polypeptide
C9 Complement and coagulation cascades complement component 9
CPS1 a ligase enzyme located in the mitochondria involved in the production of urea carbamoyl-phosphate synthetase 1
CRP CRP plays a crucial role in the induction, amplification, and prolongation of inflammatory processes, including atherosclerotic lesions, c-reactive protein C-reactive protein
CYP1A2 The major P450 enzyme involved in metabolism of drugs and exogenous toxins, drug and steroid (especially estrogen) metabolism (CYP1A2),
cytochrome P450, family 1, subfamily a, polypeptide 2
CYP24A1 Xenobiotics, vit D, drugs and steroid metabolism
cytochrome P450, family 24, subfamily a, polypeptide 1
CYP2A5 Oxidative stress and xenobiotics metabolism cytochrome P450, family 2, subfamily a, polypeptide 5
CYP2C37, CYP2C50 xenobiotics metabolism
cytochrome P450, family 2. subfamily c, polypeptide 37 and polypeptide 50 resp.
CYP4F14 arachidonic acid and fatty acid metabolism cytochrome P450, family 4, subfamily f, polypeptide 14
SERPINA10, 12, 1A, 1B, 1C, 1D, 3M, 6
Serpins belong to a group of proteins with similar structures that were primarily identified as a set of proteins inhibiting proteases. Most of serpins control proteolytic cascades in processes such as coagulation and inflammation, and other serpins were shown to have diverse functions such as storage (ovalbumin), hormone carriage proteins and tumor suppressor genes. Recently it was shown that vaspin (SERPINA 12) has insulin sensitizing properties and it is expressed in human adipose tissue 28
serine (or cysteine) peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 10, 12, 1A, 1B, 1C, 1D, 3M, 6 resp
SLC22A1\ OCT1
SLC22A1 plays a role in the hepatic uptake of metformin which is one of the most widely prescribed drugs for the treatment of type 2 diabetes 25
solute carrier family 22 (organic cation transporter), member 1\organic cation transporter 1
SLC27A5\ FATP5
Protein encoded by this gene has been shown to be a multifunctional protein that in vitro increases both uptake of fluorescently labeled long-chain fatty acid (LCFA) analogues and bile acid/coenzyme A ligase activity on overexpression. In FATP5 knockout mice it was shown that in livers there were alternations in lipid homeostasis coupled with a decreased in uptake of dietary LCFAs. The role of FATP5 in adipose tissue is not known 6
solute carrier family 27 (fatty acid transporter), member 5/fatty acid transport protein 5
SLC38A3 Protein encoded by SLC38A3 it is an amino acid transporter. Insulin decreases expression of SLC38A3 , however the dietary restricted mice have increased expression of SLC38A3 9
solute carrier family 38, member 3
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Discussion
In the present study we aimed to investigate the role of GIP in the development of obesity in
mice. We tested if excess of GIP applied subchronically by intraperitoneal injections might
contribute to the accelerated development of obesity. We injected mice on chow and HFD
daily with a stable, DPPIV-resistant analogue of GIP (D-Ala2-GIP) and measured selected
serum biochemical parameters and expression of sets of metabolic and pro-inflammatory
genes in adipose tissue. Additionally, we aimed to identify GIP gene targets in adipose tissue
in order to deepen our understanding of the role of GIP in adipose tissue metabolism.
As expected, animals which were fed for 9 weeks with a chow diet were lean and mice fed a
HFD became obese. In weeks 10 and 11 animals were fed the same diets as during the 9
weeks and were daily injected with D-Ala2-GIP or PBS. During this period (week 10 and 11)
mice lost body weight, however the body weight loss was significantly higher in mice
injected with the stable GIP analogue compared to placebo injected animals, in both chow
and HFD groups. These findings are in contradiction to other experiments performed in
C57BL/6 mice fed a HFD and injected subcutaneously (twice-daily) for 8 weeks with D-Ala-
GIP where no significant change in body mass was observed 18. It is difficult to explain this
difference, especially because the applied concentrations of the drug were identical (24
nM/kg) and other experimental factors such as gender, age, housing conditions were very
similar. In the experiment of Lemont et al. the D-Ala2-GIP was applied twice daily and for 8
weeks, but it is unlikely that these factors explain the difference. Here we can only speculate,
that the difference in genetic variation of the animals used in our and Lemont’s study plays a
significant role. In our experiments, the decrease in body mass in both D-Ala2-GIP and PBS
injected animals could be partly explained by stress caused by daily injections; however, the
significant higher body weight loss in GIP injected animals indicates that D-Ala2-GIP has an
effect on body weight. The observed significant higher body weight reduction in GIP injected
animals was coupled with significantly lower LPL activity in serum. This suggests that the
stable GIP analogue effect might be mediated by a decrease of LPL serum activity, resulting
in lower TG accumulation in adipose and/or other tissues and eventually decreased body
mass. Moreover to support this hypothesis, we observed that insulin level was significantly
reduced in the D-Ala2-GIP injected animals fed chow diet (p=0.016) and a similar trend,
however not significant, was observed in animals fed HF diet. Insulin is known to increase
LPL activity; thereby the combined decreased insulin serum level and the decreased LPL
Chapter 3
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activity observed in our study can be involved in the mechanism responsible for the
decreased body weight reduction upon D-Ala2-GIP action. These findings might be in
contradiction to earlier in vitro studies demonstrating that native GIP increases LPL activity
and triglyceride (TG) accumulation in differentiated 3T3-L1 cells and human subcutaneous
adipocytes 14. However, these and our results can not be compared directly since native and
modified GIP analogue were used respectively, and in vitro studies can not be directly
extrapolated to the in vivo situation. Moreover, recently, it was reported that in clinical trials,
application of GIP agonists led to weight loss, blood pressure reduction, and, as expected,
beta-cell function improvements 8 thereby suggesting an anti-diabetic role for the
overstimulation of GIP signaling in vivo.
Besides LPL activity we measured several serum biochemical parameters in order to analyze
systemic effects of D-Ala2-GIP. We did not find any effect on TG, cholesterol, and FFA
levels in D-Ala2-GIP injected animals. We also could not confirm previous observations that
GIP injections lead to increased resistin serum levels 10. However, in agreement with a
previous report 26, we observed that resistin serum levels were significantly elevated in obese
mice. This effect, though, was not seen in the GIP injected animals on a HFD, suggesting
that GIP might reverse the effect of a HFD on resistin levels. In contradiction to findings of
Miyawaki et al. 20, the obese animals did not have elevated GIP serum levels. Thereby, this
observation does not support the hypothesis that GIP acts as a direct link between
overnutrition and obesity.
As anticipated, the selected metabolic and proinflammatory gene expression analysis showed
that obese animals had significantly upregulated proinflammatory- and significantly
downregulated energy metabolism genes expression in adipose tissue compared to mice fed
chow diet. These findings are indicative for local inflammation and insulin resistance in
adipose tissue of obese animals. However the D-Ala2- GIP significantly decreased
proinflammatory gene expression in adipose tissue of lean animals. These data suggest that
GIP might have indirect anti-inflammatory actions in lean animals which are absent in obese
mice. The absence of this effect in obese animals could be due a very strong pro-
inflammatory action of the HFD which can not be compensated by GIP. We also anticipated
that in fat tissue of obese animals angiogenic processes, requiring upregulation of
“proinflammatory” cytokines, were activated, in order to support expansion of fat tissue
necessary for storage of TGs, and associated with these processes angiogenesis 5.
Subsequently, we wondered if the local inflammation in adipose tissue of animals fed a HFD
would result in increased serum concentration of proinflammatory cytokines. We did not
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detect any differences in serum levels for theses cytokines between lean and obese animals.
These findings are in line with the hypothesis that the local inflammation in adipose tissue
might precede systemic inflammation and insulin resistance in obesity 1,4,7 or it could mean
that the observed upregulation of proinflammatory cytokines is characteristic for
angiogenesis necessary for the expansion of fat tissue related to overnutrition as stated above 5.
The DNA-microarray data analysis revealed novel GIP candidate targets. Within these genes
we identified several APO family genes known from their involvement in lipid metabolism in
the liver, however, not reported yet in the same context in adipose tissue metabolism. For
example APOA1 was reported to be the major component of the high-density lipoprotein
complex and has anti-inflammatory effects 31. ApoC-IV overexpression may perturb lipid
metabolism leading to lipid accumulation resulting in steatosis in humans; in mice the role of
ApoCIV is largely unknown 12. Murine ApoF significantly reduced total cholesterol levels,
high density lipoproteins and phospholipid levels 17. Also other candidate GIP target genes
are known to be involved in lipid metabolism such as SLC27A5 6, ABCG5, ABCG8, 16
ApoH 23. The role of other genes such as SERPINA-group family is not established yet in
mice in relation to insulin resistance. However it was recently shown that human vaspin
(SERPINA 12) exhibits insulin sensitizing properties and its mRNA decreased with
worsening of diabetes and body weight loss. Vaspin mRNA increased upon treatment with
pioglitazone and improved glucose tolerance in obese ICG mice 28. Further research is needed
to elucidate the link between GIP, insulin sensitization and SRPINA group family of proteins.
The cytochrome P450 supergene family of enzymes is involved in metabolism of steroids,
fatty acids, vitamin D, but also the degradation of drugs and exogenous toxins 30. In our
studies we could not exclude that the upregulation of the CYP450 genes was due to
exogenous effect of D-Ala2-GIP, which had to be metabolized and excreted due to its
toxicity; however we did not observed any changes in animals indicative for obvious toxic
actions of D-Ala2-GIP.
In summary, our studies showed for the first time that in vivo application of D-Ala2-GIP
induces significant body weight reduction, which can be explained by decreased LPL serum
activity and decreased insulin serum levels. Moreover, the identified novel candidate GIP
target genes in adipose tissue linking GIP actions to lipid metabolism exerted by APO,
SERPINA or ABCG genes bring new insights into adipose tissue metabolism in relation to
GIP. The exact mechanism connecting GIP and its candidate target genes in lipid metabolism
has to be established.
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105
Acknowledgements
We would like to thank Henk van der Molen for excellent technical assistance in animal
handling.
References
1. Andersson, C. X.; Gustafson, B.; Hammarstedt, A.; Hedjazifar, S.; Smith, U. Inflamed adipose tissue,
insulin resistance and vascular injury. Diabetes Metab Res Rev 2008 Nov;24(8):595-603. 2. Asmar, M.; Holst, J. J. Glucagon-like peptide 1 and glucose-dependent insulinotropic polypeptide: new
advances. Curr Opin Endocrinol Diabetes Obes 2010 Feb;17(1):57-62. 3. Badman, M. K.; Flier, J. S. The gut and energy balance: visceral allies in the obesity wars. Science
2005 Mar;307(5717):1909-1914. 4. Bastard, J. P.; Maachi, M.; Lagathu, C.; Kim, M. J.; Caron, M.; Vidal, H.; Capeau, J.; Feve, B. Recent
advances in the relationship between obesity, inflammation, and insulin resistance. Eur Cytokine Netw 2006 Mar;17(1):4-12.
5. Cao, Y. Adipose tissue angiogenesis as a therapeutic target for obesity and metabolic diseases. Nat Rev Drug Discov 2010 Feb;9(2):107-115.
6. Doege, H.; Stahl, A. Protein-mediated fatty acid uptake: novel insights from in vivo models. Physiology (Bethesda ) 2006 Aug;21:259-268.
7. Flowers, J. B.; Oler, A. T.; Nadler, S. T.; Choi, Y.; Schueler, K. L.; Yandell, B. S.; Kendziorski, C. M.; Attie, A. D. Abdominal obesity in BTBR male mice is associated with peripheral but not hepatic insulin resistance. Am J Physiol Endocrinol Metab 2007 Mar;292(3):E936-E945.
8. Fonseca, V. A.; Zinman, B.; Nauck, M. A.; Goldfine, A. B.; Plutzky, J. Confronting the type 2 diabetes epidemic: the emerging role of incretin-based therapies. Am J Med 2010 Jul;123(7):S2-S10.
9. Gu, S.; Villegas, C. J.; Jiang, J. X. Differential regulation of amino acid transporter SNAT3 by insulin in hepatocytes. J Biol Chem 2005 Jul;280(28):26055-26062.
10. Hansotia, T.; Maida, A.; Flock, G.; Yamada, Y.; Tsukiyama, K.; Seino, Y.; Drucker, D. J. Extrapancreatic incretin receptors modulate glucose homeostasis, body weight, and energy expenditure. J Clin Invest 2007 Jan;117(1):143-152.
11. Hinke, S. A.; Gelling, R. W.; Pederson, R. A.; Manhart, S.; Nian, C.; Demuth, H. U.; McIntosh, C. H. Dipeptidyl peptidase IV-resistant [D-Ala(2)]glucose-dependent insulinotropic polypeptide (GIP) improves glucose tolerance in normal and obese diabetic rats. Diabetes 2002 Mar;51(3):652-661.
12. Kim, E.; Li, K.; Lieu, C.; Tong, S.; Kawai, S.; Fukutomi, T.; Zhou, Y.; Wands, J.; Li, J. Expression of apolipoprotein C-IV is regulated by Ku antigen/peroxisome proliferator-activated receptor gamma complex and correlates with liver steatosis. J Hepatol 2008 Nov;49(5):787-798.
13. Kim, S. J.; Nian, C.; McIntosh, C. H. Resistin is a key mediator of glucose-dependent insulinotropic polypeptide (GIP) stimulation of lipoprotein lipase (LPL) activity in adipocytes. J Biol Chem 2007 Nov;282(47):34139-34147.
14. Kim, S. J.; Nian, C.; McIntosh, C. H. Activation of lipoprotein lipase by glucose-dependent insulinotropic polypeptide in adipocytes. A role for a protein kinase B, LKB1, and AMP-activated protein kinase cascade. J Biol Chem 2007 Mar;282(12):8557-8567.
15. Korenaga, T.; Fu, X.; Xing, Y.; Matsusita, T.; Kuramoto, K.; Syumiya, S.; Hasegawa, K.; Naiki, H.; Ueno, M.; Ishihara, T.; Hosokawa, M.; Mori, M.; Higuchi, K. Tissue distribution, biochemical properties, and transmission of mouse type A AApoAII amyloid fibrils. Am J Pathol 2004 May;164(5):1597-1606.
Chapter 3
106
16. Kruit, J. K.; Drayer, A. L.; Bloks, V. W.; Blom, N.; Olthof, S. G.; Sauer, P. J.; de, H. G.; Kema, I. P.; Vellenga, E.; Kuipers, F. Plant sterols cause macrothrombocytopenia in a mouse model of sitosterolemia. J Biol Chem 2008 Mar;283(10):6281-6287.
17. Lagor, W. R.; Brown, R. J.; Toh, S. A.; Millar, J. S.; Fuki, I. V.; de, l. L.-M.; Yuen, T.; Rothblat, G.; Billheimer, J. T.; Rader, D. J. Overexpression of apolipoprotein F reduces HDL cholesterol levels in vivo. Arterioscler Thromb Vasc Biol 2009 Jan;29(1):40-46.
18. Lamont, B. J.; Drucker, D. J. Differential antidiabetic efficacy of incretin agonists versus DPP-4 inhibition in high fat fed mice. Diabetes 2008 Jan;57(1):190-198.
19. Livak, K. J.; Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001 Dec;25(4):402-408.
20. Miyawaki, K.; Yamada, Y.; Ban, N.; Ihara, Y.; Tsukiyama, K.; Zhou, H.; Fujimoto, S.; Oku, A.; Tsuda, K.; Toyokuni, S.; Hiai, H.; Mizunoya, W.; Fushiki, T.; Holst, J. J.; Makino, M.; Tashita, A.; Kobara, Y.; Tsubamoto, Y.; Jinnouchi, T.; Jomori, T.; Seino, Y. Inhibition of gastric inhibitory polypeptide signaling prevents obesity. Nat Med 2002 Jul;8(7):738-742.
21. Oudhoff, M. J.; Bolscher, J. G.; Nazmi, K.; Kalay, H.; van 't, H. W.; Amerongen, A. V.; Veerman, E. C. Histatins are the major wound-closure stimulating factors in human saliva as identified in a cell culture assay. FASEB J 2008 Nov;22(11):3805-3812.
22. Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 2001 May;29(9):e45.
23. Provost, P. R.; Boucher, E.; Tremblay, Y. Apolipoprotein A-I, A-II, C-II, and H expression in the developing lung and sex difference in surfactant lipids. J Endocrinol 2009 Mar;200(3):321-330.
24. Sawashita, J.; Kametani, F.; Hasegawa, K.; Tsutsumi-Yasuhara, S.; Zhang, B.; Yan, J.; Mori, M.; Naiki, H.; Higuchi, K. Amyloid fibrils formed by selective N-, C-terminal sequences of mouse apolipoprotein A-II. Biochim Biophys Acta 2009 Oct;1794(10):1517-1529.
25. Shu, Y.; Sheardown, S. A.; Brown, C.; Owen, R. P.; Zhang, S.; Castro, R. A.; Ianculescu, A. G.; Yue, L.; Lo, J. C.; Burchard, E. G.; Brett, C. M.; Giacomini, K. M. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest 2007 May;117(5):1422-1431.
26. Steppan, C. M.; Bailey, S. T.; Bhat, S.; Brown, E. J.; Banerjee, R. R.; Wright, C. M.; Patel, H. R.; Ahima, R. S.; Lazar, M. A. The hormone resistin links obesity to diabetes. Nature 2001 Jan;409(6818):307-312.
27. Takahashi, Y.; Inoue, J.; Kagechika, H.; Sato, R. ApoC-III gene expression is sharply increased during adipogenesis and is augmented by retinoid X receptor (RXR) agonists. FEBS Lett 2009 Jan;583(2):493-497.
28. Wada, J. Vaspin: a novel serpin with insulin-sensitizing effects. Expert Opin Investig Drugs 2008 Mar;17(3):327-333.
29. Wang, H.; Eckel, R. H. Lipoprotein lipase: from gene to obesity. Am J Physiol Endocrinol Metab 2009 Aug;297(2):E271-E288.
30. Yang, X.; Zhang, B.; Molony, C.; Chudin, E.; Hao, K.; Zhu, J.; Gaedigk, A.; Suver, C.; Zhong, H.; Leeder, J. S.; Guengerich, F. P.; Strom, S. C.; Schuetz, E.; Rushmore, T. H.; Ulrich, R. G.; Slatter, J. G.; Schadt, E. E.; Kasarskis, A.; Lum, P. Y. Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome Res 2010 Aug;20(8):1020-1036.
31. Yoo, K. H.; Kim, Y. N.; Lee, M. J.; Seong, J. K.; Park, J. H. Identification of apolipoproteinA1 reduction in the polycystic kidney by proteomics analysis of the Mxi1-deficient mouse. Proteomics 2009 Aug;9(15):3824-3832.
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Chapter 4
Adipokines and energy metabolism genes, but
not proinflammatory genes are deregulated in
patients with higher HOMA and lower HDL
Ewa Szalowska
Gerard J. te Meerman Annemieke Hoek
Roel J. Vonk
In preparation
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Abstract
Context: The adipose tissue (AT) gene expression changes related to obesity may lead to
alternations in the metabolism of the tissue itself and contribute to the development of
systemic insulin resistance (IR). In has been suggested that one of the pivotal events leading
to systemic IR is inflammation of AT and it was shown that in AT of obese subjects
expression of proinflammatory genes was upregulated, while the energy metabolism genes
were downregulated. These changes were predominant in visceral fat compared to
subcutaneous fat tissue (SAT).
Objective: We studied expression of subsets of proinflammatory and energy metabolism
genes in omentum and SAT in non diabetic women in order to test if: (1) AT displays
proinflammatory or/and metabolic alternations in early stages in the development of IR and
(2) if both kind of AT display differential expression for the selected genes indicating on their
divergent functions.
Methods: Relative gene expression in AT was determined by QPCR. The basic biochemical
serum parameters were measured by means of standard laboratory techniques.
Results: Adiponectin and metabolic genes expression was decreased in women characterized
by higher HOMA and low HDL level, compared to the counterpart groups; no difference in
expression of pro-inflammatory genes was observed in these groups. Proinflammatory genes
expression was increased in women with waist circumference above 88 cm. The SAT and
omentum displayed differential gene expression for some of the analyzed genes.
Conclusions: Based on the altered AT gene expression and assuming that high HOMA and
low HDL level are related to the developing IR, we postulate that metabolic perturbations
precede AT inflammation in early stages of the development of IR. Furthermore, the
differential gene expression between SAT and omentum indicates on different functions for
these fat depots.
KEYWORDS: insulin resistance, inflammation, energy metabolism and proinflammatory and
energy metabolism gene expression, subcutaneous adipose tissue, omentum
Chapter 4
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Introduction
Obesity is associated with an increased risk factor for the development of insulin resistance
(IR) and type 2 diabetes (T2D). Adipose tissue (AT) acts as an endocrine organ secreting a
variety of factors that exert paracrine and endocrine effects and plays an important role in the
maintenance of the whole-body energy homeostasis [1; 2]. In obesity AT releases increased
amounts of proinflammatory cytokines, and has perturbed secretion pattern of adipokines
what could contribute to the systemic IR [3-7]. It was shown in humans and rodents, that
overnutrition cause oxidative stress which contributes to the infiltration of macrophages into
AT, causing a shift in fat tissue physiology from an organ predominantly involved in the
energy metabolism towards a significant source of proinflammatory cytokines released to the
blood such as TNF-α, IL-6, and IL-1β [8; 9; 9-12]. Therefore, inflammation of AT is
regarded as one of the key events leading to the development of systemic IR and in its early
stages is manifested by upregulated expression of several proinflammatory genes in AT.
Moreover, IR patients are characterized by low serum level of high density lipoprotein (HDL)
and elevated levels of serum glucose, insulin, triglycerides (TG), cholesterol, low density
lipoprotein (LDL) and high values for homeostasis model assessment (HOMA), waist
circumference (WC) and body mass index (BMI) [13].
Several studies have shown that the distribution of body fat is a critical factor for the
determination of insulin sensitivity. Lean individuals with more subcutaneous adipose tissue
(SAT) are more insulin sensitive than lean individuals with more fat distributed centrally
(omentum and visceral fat). Furthermore, it was shown in most but not all studies, that intra-
abdominal adipose tissue expresses more genes encoding for secretory proteins and has a
more proinflammatory character than SAT [14; 15].
In our studies, we aimed to find out if during early stages of insulin resistance AT
inflammation precedes its metabolic perturbations. Therefore, we studied proinflammatory
and energy metabolism genes expression in non-diabetic women. In order to characterize
patients we determined serum biochemical parameters (glucose, low density lipoprotein
(LDL), high density lipoprotein (HDL), cholesterol, triglycerides (TG), and insulin) and
anthropometric factors such as body mass index (BMI) and waist circumference (WC).
Thereafter, we applied a specific cut off value for the analyzed parameters, in order to divide
patients into low or high group for a specific parameter, for example low or high HDL level
group. Then, we aimed to identify which biochemical or anthropometric parameters are
Chapter 4
110
related to altered AT gene expression. Moreover, we wanted to asses if the AT gene
expression changes were fat depot specific, therefore the analyzed genes were studied both in
SAT and omentum.
Materials and Methods
Study subjects
In the study 38 Caucasian women undergoing surgery because of benign gynecological
problems were included. The women were in general good health, as determined by medical
history and physical examination. All the individuals had no history or symptoms of T2D or
other inflammatory diseases. The research protocols were approved by the medical ethical
committee of the University of Groningen, University Medical Center Groningen.
Participants gave their written informed consent.
Anthropometric measurements, blood collection and adipose tissue sampling
Anthropometric examination of the subjects was performed 1 day before the surgical
intervention. The patients were weighted, and the body mass index was calculated. Blood
samples for biochemical and hormonal measurements were taken after overnight fasting, at
the day of operation, before start of anesthesia. Anthropometric and biochemical
characteristics of the subjects are summarized in Table 3.
The subcutaneous biopsies were taken at the place of the incision, midline lower abdomen
above the symfysis and under the umbilicus. The biopsies of the omentum were taken at the
lower edge of the omentum, both types of biopsies were taken by means of surgical scissor.
Human omental and subcutaneous surgical biopsies were placed in sterile transfer buffer
(TB) (PBS containing 5.5 mM glucose and 50 µg/ml Gentamycine) in the operating room ,
and transferred within 10 minutes to the laboratory where the biopsies were snap frozen in
liquid nitrogen and stored in -80ºC till further processing.
Serum adipokines, insulin and biochemical analysis
Total serum insulin, cholesterol, triglycerides, HDL-cholesterol, LDL cholesterol, and
glucose were determined in the Laboratory Centre of the University Medical Centre
Groningen (UMCG) with standard laboratory methods.
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Homeostasis model assessment (HOMA-IR) index was calculated as previously described
(Matthews et al 1985) using the following formula: fasting serum insulin (mIU/l) x fasting
serum glucose (mmol/l)/22.5.
RNA isolation and cDNA synthesis
RNA was extracted from adipose tissue using RNeasy Lipid Tissue Mini Kit (Qiagen, Venlo,
The Netherlands) according to the manufacturers’ instructions. The RNA concentration was
determined by Nano Drop ND-1000 Spectrophotometer (Isogen Ijsselstein, The Netherlands).
First-strand cDNA synthesis was performed from total RNA with QuantiTect Reverse
Transcription Kit (Qiagen, Venlo, the Netherlands) according to the manufacturers’
instructions.
Real-time quantitative PCR
Quantification of the expression of the genes of interest was done by QRPCR on ABI prism
7900 HT (Applied Biosystems) with the following cycling conditions: 15 min 95°C followed
by 40 cycles of 15s 95 ºC and 1 min 60 ºC. Reactions were performed in 10 µl volume and
contained 20 ng cDNA, 1x TaqMan PCR Master Mix (Applied Biosystems, Foster City, CA),
250 nM of each probe and 900 nM of each primer. For each gene, a standard curve was
generated and efficiency of the primers was determined as described previously [16]. For
each primer pair the efficiency was about 95 %. Specific primer sets were developed with
Primer Express 1.5 (Applied Biosystems) and the sequences are given in Table 1. Primers for
CRP were purchased from Applied Biosystems (the Netherlands). Data were analyzed with
SDS 2.0 software (Applied Biosystems). For each sample, the RT-PCR reaction was
performed twice in triplicate and the averages of the obtained threshold cycle values (CT)
were processed for further calculations. For normalization B2M was used. Relative
expression was calculated with ∆ (∆ (CT))-method [17]. The functional and nomenclature
summary of the studied genes is presented in Table 2.
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112
Table 1. Sequences of primers and probes used in Taq-Man Q-PCR.
Gene symbol
Forward primer (5’-3’) Reverse primer (5’-3’) Probe (5’-3’)
ADIPOQ AGG CCG TGA TGG CAG AGA T GTC TCC CTT AGG ACC AAT AAG ACC T ATC TCC TTT CTC ACC CTT CTC ACC AGG G
LEP TCA CCA GGA TCA ATG ACA TTT CAC AGC CCA GGA ATG AAG TCC AAA C CGC AGT CAG TCT CCT CCA AAC AGA AAG TCA
RETN GCC GGA TTT GGT TAG CTG AG GAG GAG GAG ACA GAG AGC TTT CAT CCA CCG AGA GGC GCC TGC AG
PPARγ GAT GTC TCA TAA TGC CAT CAG GTT GGA TTC AGC TGG TCG ATA TCA CT CCA ACA GCT TCT CCT TCT CGG CCT G
GLUT4 GCTGTGGCTGGTTTCTCCAA CCCATAGCCTCCGCAACATA CGAGCAACTTCATCATTGGCATGGGTT
LPL TGGAGATGTGGACCAGCTAGTG CAGAGAGTCGAT GAAGAGATGAATG CTCCCACGAGCGCT
SREBP1C GGA TTG CAC TTT CGA AGA CAT G AGC ATA GGG TGG GTC AAA TAG G CAG CTT ATC AAC AAC CAA GAC AGT GAC TTC CC
FASN GCAAATTCGACCTTTCTCAGAAC GGACCCCGTGGAATGTCA CCCGCTCGGCATGGCTATC
IR CAACGGGCAGTTTGTCGAA GCAGCCGTGTGACTTACAGATG ACTCATAGTCACTGCCAGAAAGTTTGCCCG
GLUR TGCACTGCACCCGCAAT GCACGGAGCTGGCTTTCA CGCGAATCTGTTTGCGTCCT
GIPR GGCCTTTCTGGACCAAAGG TGTGGCGAGAGACAGGGAGTA TTGGAGCGGTTGCAGGTCATGTACAC
GHRR CCTTCCACGTAGGGCGATATT TGATCTGAGCAATCTCCAAGGA TTTTCCAAATCCTTTGAGCCTG
GLP-1R CCCATTCTCTTTGCCATTGG GCACATGAGATTGGCCTTCA TGTTCGGGTCATCTGCATCGTGGT
CD 68 GCTTCTCTCATTCCCCTATGGA ATGTAGCTCAGGTAGACAACCTTCTG CAGCTTTGGATTCATGCAGGACCTCC
CD 163 TGCAGAAAACCCCACAAAAAG CAAGGATCCCGACTGCAATAA AACAGGTCGCTCATCCCGTCAGTCA
IL-1β CTGATGGCCCTAAACAGATGAAG GGTCGGAGATTCGTAGCAGCTGGAT TTCCAGGACCTGGACCTCTGCCCTC
IL-6 CCAGGAGCCCAGCTATGAAC CCCAGGGAGAAGGCAACTG CCTTCTCCACAAGCGCCTTCGGT
TNFα CTCGAACCCCGAGTGACA AGCTGCCCCTCAGCTTGA CCTGTAGCCCATGTTGTAGCAAACC
B2M TGACTTTGTCACAGCCCAAGATA AATCCAAATGCGGCATCTTC TGATGCTGCTTACATGTCTCGATCCCA
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113
Table 2. Functional and nomenclature summary of the studied genes.
Gene symbol
Gene name Function description
AD
IPO
KIN
ES
ADIPOQ adiponectin decreases glucose, FFA, TG, increases glucose uptake and fatty acid oxidation in muscle; increased adiponectin levels have many beneficial effects on insulin sensitivity and inflammation, plasma levels are decreased in diabetic compared to non diabetic people
LEP leptin antidiabetic, increases the whole body glucose metabolism, decreases glycogen content in liver and stimulates fatty acid oxidation in skeletal muscle; decreases during fasting and it is restored after feeding; plasma levels correlate with the level of adiposity;
RETN resistin correlates with markers of inflammation and CVD
GE
NE
S IN
VO
LVE
D IN
EN
ER
GY
ME
TA
BO
LIS
M
PPARγ Peroxisome proliferator- activated receptor gamma
regulator of adipocyte differentiation
GLUT4 Solute carrier family 2, member 4
insulin-regulated facilitative glucose transporter
LPL Lipoprotein lipase expressed in heart, muscle, adipose tissue, has the dual functions of triglyceride hydrolase and ligand/bridging factor for receptor-mediated lipoprotein uptake
SREBP1C Sterol regulatory element binding transcription factor 1c
transcription factor that binds to the sterol regulatory element-1 (SRE1), which is flanking the low density lipoprotein receptor gene and some genes involved in sterol biosynthesis
FASN Fatty acid synthase its main function is to catalyze the synthesis of palmitate from acetyl-CoA and malonyl-CoA, in the presence of NADPH, into long-chain saturated fatty acids
INSR Insulin receptor mediates insulin actions; there are two transcript variants encoding different isoforms of INSR
GLUR Glucagon receptor mediates glucagon actions, mostly expressed in liver and kidneys, but also adipose tissue, heart , spleen, and gastrointestinal tract
GIPR Glucose dependend insulinotropic peptide receptor
mediates effects of GIP, involved in the regulation of the lipid metabolism by for example activating LPL activity in adipocytes
GHRR Ghrelin receptor mediates ghrelin effects such as regulation of appetite
GLP-1R Glucagon like peptide-1 receptor
mediates effects of GLP-1 such as decreasing food intake by increasing satiety
PR
OIN
FLA
MM
AT
OR
Y
GE
NE
S
CD 68 CD 68 molecule macrophage marker
CD 163 CD 163 molecule marker for activated macrophages, increases during endotoxemia
CRP C-reactive protein expressed by human monocytes and tissue macrophages, involved in clearance of cellular debris, promotes phagocytosis, and mediates the recruitment and activation of macrophages.
IL-1β Interleukin 1 beta produced by activated macrophages, mediator of the inflammatory response, involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis.
IL-6 Interleukin 6 cytokine ,marker of many inflammatory diseases such as T2D, CVD, arthritis
TNFα Tumor necrosis factor alpha proinflammatory cytokine, secreted by macrophages, involved in cell proliferation, differentiation, apoptosis, lipid metabolism, and coagulation; implicated in diseases, including autoimmune diseases, IR, and cancer.
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Statistical analysis
The statistical analysis was performed using SPSS 12.0.1 for Windows (SPSS Inc., Chicago,
IL, USA). The obtained values for the relative gene expression were ranked, the lowest value
obtained the lowest rank (1), the next low value rank 2, etc, and the highest values obtained
the highest rank. To determine differences in changes of relative gene expression (dependent
variables) against anthropometric and biochemical parameters (independent variables)
Kruskal Wallis test was used. The cut off values for dependent variables were set as depicted
in Table 2. Statistical significance was assigned to p ≤0.05.
Results
1. Anthropometric and biochemical characteristics of the study group and applied cut-
off values for the studied parameters
The patients were classified in three groups for BMI (lean, overweight, obese), two groups
for waist circumference (WC), and lower and higher levels group for the following
biochemical and hormonal parameters: HOMA, glucose, insulin, triglycerides (TG), total
cholesterol, LDL, and HDL. The cut off values for the groups are given in Table 3. The
selected cut off values for the HOMA, insulin, cholesterol, LDL, group were set based on the
median value for the specific parameter within the group. The cut off values for HDL,
glucose, TG were set according to the WHO Clinical Criteria for Metabolic Syndrome [13].
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Table 3. Anthropometric and biochemical characteristics of the study group and applied cut-off values for the studied parameters.
2. Identification of differentially expressed genes in relation to anthropometric and
biochemical parameters
By means of Q-PCR gene expression in SAT and omentum was determined in relation to the
biochemical and anthropometric parameters. The statistical analysis revealed significantly
changed genes for the investigated parameters. Within the BMI-group the expression of
leptin in omentum and PPARγ, LPL, and IL1β in SAT was significantly different. For the
WC-group the omentum had different expression of leptin, LPL and IL-6 and the SAT,
within the same group, had different expression for resistin, IL-1β, and IL-6. The HOMA-
group had significantly changed expression in SAT for adipokines and several energy
metabolism genes while in omentum expression of resistin, LPL, GLUR and CD163 were
changed. The TG-group showed changes associated with adipokines genes in SAT and in
omentum only resistin expression was changed. The HDL group had changed expression in
SAT of adipokines and several metabolic genes, whereas in omentum leptin and LPL
expression were altered. For the LDL-group only the resistin expression in omentum was
significantly changed. Within the cholesterol group leptin in omentum and resistin in SAT
expression was changed. The results are summarized in Table 4.
INDEPENDENT VARIABLES CUT OFF FOR INDEPENDENT VARIA BLES AND NUMBER (N) OF SUBJECTS WITHIN THE GROUP
Total number of subjects N=38
BMI N=15 BMI<25 N=16 25<BMI<30
N=7 BMI>30
Waist circumference (WC) cm N=16 WC<88cm N=22 WC>88cm
Age (years) N=8 30-39 N=17 40-49 N=8 50-59 N=5 60-69
Fat tissue type N=38 SAT N=38 omentum
HOMA N=26 HOMA<2.6 N=12 HOMA>2.6
Glucose (mM) (G) N=19 G <5.5 N=19 G>5.5
Insulin (pg/ml) (I) N=17 I<250 N=21 I>250
TG(mM) N=16 TG <1 N=22 TG >1
Cholesterol (mM) (CH) N=23 CH<3 N=15 CH>3
LDL (mM) N=16 LDL<4 N=22 LDL>4
HDL (mM) N=27 HDL<1.3 N=11 HDL>1.3
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Table 4. The significantly changed gene expression in SAT and omentum (om.) for the analyzed biochemical (glucose, insulin, HOMA, HDL, LDL, total cholesterol, TG) and anthropometric parameters (BMI, WC). Kruskal-Wallis test was used to calculate p-value signifying the difference in a gene expression for a particular parameter, p value ≤0.05 was considered significant. None of the analyzed gene had changed expression for both “glucose” and “insulin” groups.
BMI WC HOMA TG HDL LDL Cholesterol
ADIPOQ SAT NS NS p=0.009 p=0.05 p=0.02 NS NS
LEPTIN SAT. NS NS p= 0.03 p=0.003 p=0.006 NS NS
LEPTIN om. p=0.001 p=0.002 NS NS p=0.02 NS p=0.03
RETN SAT. NS p=0.05 NS NS p=0.006 NS p=0.04
RETN om. NS NS p=0.05 p=0.01 NS p=0.04 NS
PPARg SAT p=0.05 NS p=0.03 NS NS NS NS
SREBP1c SAT NS NS p=0.05 NS p=0.01 NS NS
LPL SAT. p=0.03 NS p=0.04 NS p=0.03 NS NS
LPL om. NS p=0.04 p=0.05 NS p=0.05 NS NS
GLUT4 SAT. NS NS p=0.04 NS NS NS NS
INSR SAT NS NS p=0.05 NS NS NS NS
GLUR SAT NS NS p=0.02 NS p=0.05 NS NS
GLUR om. NS NS p=0.01 NS NS NS NS
GIPR SAT NS NS p=0.05 NS p=0.03 NS NS
GLP-1R SAT NS NS NS NS p=0.03 NS NS
GHRR SAT NS NS NS NS p=0.005 NS NS
CD68 SAT NS NS NS NS p=0.05 NS NS
CD163 om. NS NS p=0.05 NS NS NS NS
Il-1β SAT p=0.04 p=0.02 NS NS NS NS NS
IL-6 SAT NS p=0.03 NS NS NS NS NS
IL-6 om. NS p=0.005 NS NS NS NS NS
TNFα SAT NS NS NS NS p=0.03 NS NS
3. Direction of changes in gene expression in relation to the analyzed parameters
The relative gene expression values were ranked and the significant up- and downwards
trends of the gene expression for the analyzed parameters associated with IR such as
increased BMI, WC, HOMA and decreased HDL levels were determined. Expression of
leptin, LPL and proinflammatory genes (RETN, IL-1β, and IL-6) was increased in women
with WC above 88cm. The increased HOMA index was associated with decrease in
expression of adipokines (ADIPQ, leptin, and RETN) and several metabolic genes (PPARγ,
SREBP1c, LPL, GLUT4, INSR, GLUR and GIPR). The decreased HDL levels, similarly as
the HOMA, were associated with decreased expression of adipokines and energy metabolism
genes (SREBP1c, LPL, GLUR, GIPR, GHRR). The results are summarized in Table 5.
Table 5. The relative gene expression (RGE) mean rank value for the studied anthropometric and biochemical parameters. p-value ≤ 0.05 was considered significant and was calculated with Kruskal-Wallis test.
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Significantly, fat depot specific changed genes
Mean relative gene expression rank values for the studied biochemical and anthropometric parameters
BMI BMI<25 25<BMI<30 BMI>30 LEPTIN omentum 12.16 25.36 26.67
PPARg SAT 22.56 15.14 12 LPL SAT 25.83
19.25
12.57
14 IL1b SAT 7.3 14.5 Waist circumference (WC) cm WC<88cm WC>88cm
LEPTIN omentum 12.75 23.54 RETN SAT 15.5 22.25
LPL omentum 15.1 22.36 Il-1β SAT 6.78 12.95 IL-6 SAT 7.11 12.6
IL-6 omentum 9 13.97 HOMA HOMA<2.7 HOMA>2.7
ADIPOQ SAT 21.93 10.38 LEPTIN SAT s 24.24 14.6
RETN omentum 20.38 10.2 PPARg SAT s 21.79 8.88 SREBP1c SAT 14.06 7.83
LPL SAT 23.41 15.4 LPL omentum 23.24 14.3 GLUT4 SAT 12.4 7.5 INSR SAT 16.64 9.86 GLUR SAT 17.57 8.71
GLUR omentum 17.24 6.8 GIPR SAT 18.16
10.57
CD163 omentum 14.24 6.5 HDL (mM/L) HDL<1.3 HDL>1.3 ADIPOQ SAT 16.93 25.82 LEPTIN SAT 19.12 31.5
LEPTIN omentum 16.52 25.23 RETN SAT 17.63 28.47
SREBP1c SAT 9.5 16.7 LPL SAT 18.41 27.07
LPL omentum 18.54 25.44 GLUR SAT 13.4 19.7
GLUR omentum 12.2
21.8 GIPR SAT 13.65 21.25
GHRR SAT 14.1 24 CD68 SAT 9.36 14.29 TNFα SAT 16.72 24.85
4. Differential gene expression in SAT and omentum
Moreover, in order to explore the hypothesis about fat depot specific differences we analyzed
which genes displayed significant differential expression in SAT and omentum. The results
are summarized in Table 6.
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Table 6. Differential gene expression between SAT and omentum. The gene expression difference between SAT and omentum was significant p≤ 0.05.
Significantly, fat depot specific changed genes
Mean relative gene expression rank values for the studied biochemical and anthropometric parameters
TISSUE SAT Omentum leptin 50.77 29
PPARg 42.39 31.14 GIPR 35.64 28 GHRR 39.75 28.59 CD163 19.09 29.08
Discussion
We analyzed expression of several genes in both types of adipose tissue (SAT and omentum)
in relation to different anthropometric (BMI, WC) and biochemical (insulin, HOMA, glucose,
insulin, TG, HDL, LDL, total cholesterol) parameters in 38 women. The women used in our
study were in general good health and non-diabetic. The patients were divided into low and
high level groups in respect to each of the analyzed parameters, for example low and high
HDL level. We applied the cut off values for HDL, glucose, BMI, and WC according to the
WHO Clinical Criteria for Metabolic Syndrome [13]. The cut off values for HOMA, LDL,
total cholesterol, and insulin were based on the median value for the specific parameter
within the group because these values are not defined within criteria for metabolic syndrome
[13] and are not known for IR patients. We aimed to investigate if in our group of patients
inflammation or energy metabolism perturbations in AT were associated with early
symptoms of IR and if these changes were fat-depot specific. Therefore, we studied
expression of a subset of proinflammatory and energy metabolism genes in both SAT and
visceral adipose tissue in relation to factors indicative for IR, such as high HOMA index,
high LDL serum levels, low HDL levels, high WC, and BMI. Additionally, in order to
quantify accumulation of macrophages in AT-macrophages’ specific markers gene
expression was measured (CD68 and CD163).
The gene expression analysis revealed that patients with increased HOMA index and
decreased HDL serum levels had decreased expression of genes involved in energy
metabolism and adipokines, without changes in proinflammatory genes expression.
Assuming, that higher HOMA and lower HDL are indicative for IR, this observation
indicates that in very early stages of the development of IR a metabolic disarrangement, but
not a proinflammatory one occurs in AT. These data were in consistence with previously
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described findings; it was reported that several genes involved in insulin signaling pathway,
exampled by INSR, and other energy metabolism genes such as: SREBP1c, GLUT4, FASN
had decreased expression in obese patients compared with lean ones [15; 18; 19].
Furthermore, it was shown by Rudovich et al [20] that GIPR gene expression was decreased
in AT of IR women assessed by HOMA index. It has to be elucidated in future, if higher
HOMA and lower HDL are indeed representative for metabolic perturbations in AT. Based in
our results we speculated that HDL serum level can affect AT gene expression. To support
this hypothesis, it was recently shown that adiponectin gene expression level and adipocyte
metabolism was controlled by HDL levels [21].
Previously, it was reported that obesity and IR are associated with macrophage accumulation
responsible for upregulation of pro inflammatory cytokines [9; 22] [23]. However, in our
study we did not observe changes in AT proinflamamtory gene expression related to factors
indicating on IR such as higher HOMA, lower HDL levels or obesity. Moreover, we did not
observe increased infiltration of macrophages assessed by levels of CD68 and CD163 in
neither SAT and omentum. Thereby, this observation contradicted the hypothesis that
infiltration of macrophage into AT triggers inflammation and is the pivotal event leading to
IR in humans. However, the cytokines expression was increased in women with WC above
88 cm. The WC above 88cm is considered as one of indirect measures of IR and is one of the
criteria for the definition of metabolic syndrome, but at present a direct link between WC and
IR is unknown and WC remains a speculative measure of IR [24].
There is an ongoing scientific discussion if different fat depots have divergent physiological
functions reflected in for example differential gene expression profile. Some studies have
demonstrated that omentum exhibits more harmful profile by expressing more
proinflammatory genes than SAT [25]. However, other studies failed to confirm this
hypothesis [15], [26]. In our study, we could identify few genes such as leptin, PPARγ,
GIPR, and GHRR which were significantly higher expressed in SAT compared to omentum.
This observation can be indicative for functional differences between SAT and omentum.
Moreover, we observed that direction of the genes expression changes in SAT and omentum,
related to the investigated anthropometric and biochemical parameters, were similar, but
mostly genes in SAT and not in the omentum reached significant difference. This could mean
that SAT might be more prone to alternations of gene expression during early development of
IR, compared to omentum. This phenomenon implies that omentum and SAT have different
metabolic functions and play divergent roles in the development of IR. We also can not rule
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out the possibility, that the investigated group of patients had its unique characteristics which
can not be extrapolated to the general mechanism of the development of IR in humans.
In summary, this study demonstrates that high HOMA and low HDL are associated with
metabolic, but not proinflammatory disarrangements in the adipose tissue gene expression.
Assuming that these parameters can be used as indicators for IR, we postulate the early
events in IR include metabolic perturbations preceding inflammatory changes, thereby
excluding inflammation as key event leading to IR. How these finding can be extrapolated to
the mechanisms responsible for the development of IR in humans remains to be elucidated in
the future.
There are differences in the abundance for some genes expressed between SAT and
omentum, supporting the hypothesis that these two adipose tissue depots have different
functions. Moreover, based on the significant changes in the energy metabolism genes
expression in SAT and not in omentum, the SAT seems to be more prone to metabolic
alternations and thereby is an important player in early events associated with IR.
References
[1] R.H.Eckel, S.M.Grundy, P.Z.Zimmet, The metabolic syndrome Lancet 365, (2005) 1415-1428. [2] M.Fasshauer, S.Kralisch, M.Klier, U.Lossner, M.Bluher, A.M.Chambaut-Guerin, J.Klein, R.Paschke,
Interleukin-6 is a positive regulator of tumor necrosis factor alpha-induced adipose-related protein in 3T3-L1 adipocytes FEBS Lett. 560, (2004) 153-157.
[3] S.Schinner, W.A.Scherbaum, S.R.Bornstein, A.Barthel, Molecular mechanisms of insulin resistance Diabet.Med. 22, (2005) 674-682.
[4] M.Sowers, H.Zheng, K.Tomey, C.Karvonen-Gutierrez, M.Jannausch, X.Li, M.Yosef, J.Symons, Changes in body composition in women over six years at midlife: ovarian and chronological aging J.Clin.Endocrinol.Metab 92, (2007) 895-901.
[5] B.M.Spiegelman, J.S.Flier, Adipogenesis and obesity: rounding out the big picture Cell 87, (1996) 377-389.
[6] K.A.Virtanen, P.Iozzo, K.Hallsten, R.Huupponen, R.Parkkola, T.Janatuinen, F.Lonnqvist, T.Viljanen, T.Ronnemaa, P.Lonnroth, J.Knuuti, E.Ferrannini, P.Nuutila, Increased fat mass compensates for insulin resistance in abdominal obesity and type 2 diabetes: a positron-emitting tomography study Diabetes 54, (2005) 2720-2726.
[7] P.E.Scherer, Adipose tissue: from lipid storage compartment to endocrine organ Diabetes 55, (2006) 1537-1545.
[8] B.V.Howard, Insulin, insulin resistance, and dyslipidemia Ann.N.Y.Acad.Sci. 683, (1993) 1-8. [9] S.P.Weisberg, D.McCann, M.Desai, M.Rosenbaum, R.L.Leibel, A.W.Ferrante, Jr., Obesity is
associated with macrophage accumulation in adipose tissue J.Clin.Invest 112, (2003) 1796-1808.
[10] H.Xu, G.T.Barnes, Q.Yang, G.Tan, D.Yang, C.J.Chou, J.Sole, A.Nichols, J.S.Ross, L.A.Tartaglia, H.Chen, Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance J.Clin.Invest 112, (2003) 1821-1830.
Chapter 4
121
[11] S.Cinti, G.Mitchell, G.Barbatelli, I.Murano, E.Ceresi, E.Faloia, S.Wang, M.Fortier, A.S.Greenberg, M.S.Obin, Adipocyte death defines macrophage localization and function in adipose tissue of obese mice and humans J.Lipid Res. 46, (2005) 2347-2355.
[12] K.E.Wellen, G.S.Hotamisligil, Inflammation, stress, and diabetes J.Clin.Invest 115, (2005) 1111-1119. [13] S.M.Grundy, H.B.Brewer, Jr., J.I.Cleeman, S.C.Smith, Jr., C.Lenfant, Definition of metabolic
syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition Circulation 109, (2004) 433-438.
[14] K.Maeda, K.Okubo, I.Shimomura, K.Mizuno, Y.Matsuzawa, K.Matsubara, Analysis of an expression profile of genes in the human adipose tissue Gene 190, (1997) 227-235.
[15] M.Dolinkova, I.Dostalova, Z.Lacinova, D.Michalsky, D.Haluzikova, M.Mraz, M.Kasalicky, M.Haluzik, The endocrine profile of subcutaneous and visceral adipose tissue of obese patients Mol.Cell Endocrinol. 291, (2008) 63-70.
[16] M.W.Pfaffl, A new mathematical model for relative quantification in real-time RT-PCR Nucleic Acids Res. 29, (2001) e45.
[17] K.J.Livak, T.D.Schmittgen, Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method Methods 25, (2001) 402-408.
[18] T.E.Graham, B.B.Kahn, Tissue-specific alterations of glucose transport and molecular mechanisms of intertissue communication in obesity and type 2 diabetes Horm.Metab Res. 39, (2007) 717-721.
[19] O.Poulain-Godefroy, C.Lecoeur, F.Pattou, G.Fruhbeck, P.Froguel, Inflammation is associated with a decrease of lipogenic factors in omental fat in women Am.J.Physiol Regul.Integr.Comp Physiol 295, (2008) R1-R7.
[20] N.Rudovich, S.Kaiser, S.Engeli, M.Osterhoff, O.Gogebakan, M.Bluher, A.F.Pfeiffer, GIP receptor mRNA expression in different fat tissue depots in postmenopausal non-diabetic women Regul.Pept. 142, (2007) 138-145.
[21] L.S.Van, A.Foryst-Ludwig, F.Spillmann, J.Peng, Y.Feng, M.Meloni, C.E.Van, U.Kintscher, H.P.Schultheiss, G.B.De, C.Tschope, Impact of HDL on adipose tissue metabolism and adiponectin expression Atherosclerosis 210, (2010) 438-444.
[22] C.A.Curat, V.Wegner, C.Sengenes, A.Miranville, C.Tonus, R.Busse, A.Bouloumie, Macrophages in human visceral adipose tissue: increased accumulation in obesity and a source of resistin and visfatin Diabetologia 49, (2006) 744-747.
[23] K.Clement, D.Langin, Regulation of inflammation-related genes in human adipose tissue J.Intern.Med. 262, (2007) 422-430.
[24] S.Klein, D.B.Allison, S.B.Heymsfield, D.E.Kelley, R.L.Leibel, C.Nonas, R.Kahn, Waist circumference and cardiometabolic risk: a consensus statement from shaping America's health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association Diabetes Care 30, (2007) 1647-1652.
[25] J.N.Fain, Release of interleukins and other inflammatory cytokines by human adipose tissue is enhanced in obesity and primarily due to the nonfat cells Vitam.Horm. 74, (2006) 443-477.
[26] B.L.Wajchenberg, D.Giannella-Neto, M.E.da Silva, R.F.Santos, Depot-specific hormonal characteristics of subcutaneous and visceral adipose tissue and their relation to the metabolic syndrome Horm.Metab Res. 34, (2002) 616-621.
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Chapter 5
The “adipokine” resistin is more abundant in
human liver than in adipose tissue and it is not
upregulated by lipopolysaccharide
Ewa Szalowska
Marieke G.L. Elferink
Annemiek Hoek
Geny M.M. Groothuis
Roel J. Vonk
Journal of Clinical Endocrinology and Metabolism 2009
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Abstract
Context: Resistin is an adipokine correlated with inflammatory markers and is predictive for
cardiovascular diseases (CVD). There is evidence that serum resistin levels are elevated in
obese patients, however the role of resistin in insulin resistance (IR) and type 2 diabetes
(T2D) remains controversial.
Objective: We addressed the question whether inflammation may induce expression of
resistin in organs involved in regulation of total body energy metabolism, such as liver and
adipose tissue (AT).
Methods: Human liver tissue, subcutaneous adipose tissue (SAT) and omentum were
cultured in the absence/presence of lipopolysaccharide (LPS). The resistin and cytokine
mRNA and protein expression levels were determined by real-time PCR (RTPCR), ELISA
and Multiplex Technology respectively. The localization of resistin in human liver was
analyzed by immunohistochemistry.
Results: Resistin gene and protein expression was significantly higher in liver than in AT.
Exposure of human AT and liver tissue in culture to LPS did not alter resistin concentration;
however, concentrations of IL-1β, IL-6 and TNFα were significantly increased in these
tissues. In liver resistin colocalizes with markers for Kupffer cells, for a subset of endothelial
and fibroblasts like cells.
Conclusions: High level of resistin gene and protein expression in liver compared to AT
implicates that resistin should not be considered only as an adipokine in humans.
LPS induced-inflammation does not affect resistin protein synthesis in human liver and AT.
This suggests that elevated serum resistin levels are not indicative for inflammation of AT or
liver in a manner similar to known inflammatory markers such as IL-1β, IL-6 or TNFα.
Keywords: resistin, inflammation, insulin resistance, T2D, CVD, human liver and adipose
tissue
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Introduction
Human resistin (12.5-kD) is known as an adipokine and belongs to a family of small
cysteine-rich secretory proteins, named FIZZ (Found In Inflammatory Zone) or resistin-like
molecules (1). The human resistin gene (Retn) is a homolog of the mouse resistin gene. In
mice resistin is almost exclusively expressed in white adipose tissue and has been shown to
be involved in glucose intolerance and hepatic IR (1-3). These findings suggested that resistin
could be a link between obesity and T2D. However most of the human studies failed to show
this link (4-6) and only a few studies showed that resistin serum levels are elevated in obese
patients and could be related to IR and T2D (7,8).
IR and T2D are associated with obesity and low grade inflammation with upregulated
cytokines and chemokines (IL-1β, IL-6, IL-8 and TNFα). LPS is a compound of the cell wall
of Gram-negative bacteria that has been demonstrated to induce inflammatory reactions and
upregulate many cyto- and chemokines. Besides its role in inflammation, LPS is known to
trigger hyperglycemia and IR in rats and humans (9-11). Recently it was shown that mice
infused with LPS and on a chow diet gain body weight, develop liver IR and have a
dysregulated inflammatory tone which leads to T2D (12,13). Conflicting data are published
concerning the regulation of resistin mRNA by proinflammatory agents such as LPS, TNF-α,
IL-6 both for rodents and humans (2,14-20). In humans serum resistin level is correlated with
markers of inflammation and is predictive for CVD (2,21).
Recently, resistin was found to be expressed at a low level in human liver and to be
upregulated during severe fibrosis (19) and in hepatocytes where it induces IR (22). In
addition it was reported that adipose tissue of patients with nonalcoholic fatty liver disease
had significantly higher resistin expression than healthy individuals and obese people, and
resistin serum levels were positively correlated with severity of the liver disease (20). These
results suggest that resistin expression may be induced indirectly by LPS possibly as a result
of increased cytokine exposure.
In the present study we compared resistin mRNA and protein expression in organs involved
in regulation of total body energy metabolism: liver and adipose tissue (subcutaneous adipose
tissue= SAT and omentum=om.) and identified by immunohistochemistry in which liver cells
resistin was expressed. Furthermore we tested if resistin is upregulated during LPS-induced-
inflammation in human liver, SAT and omentum. We studied resistin and cytokine gene
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expression, protein synthesis and secretion upon stimulation with LPS in cultured human
liver slices, SAT and omentum.
Materials and Methods
Human liver tissue
Human liver tissue (n=20) was obtained and prepared as described previously (23,24). The
donors of livers for the quantification experiments and immunohistochemistry stainings were
females, aged 40-49 years, with body mass index (BMI) 18.3-27.7. The donors of livers used
in LPS experiments were males, aged 16-48, with BMI 23.1-27.7. The information about the
medical history was not available. The liver tissue used for quantification studies by RTPCR
or ELISA was snap frozen in liquid nitrogen and stored at -80°C. For immunohistochemistry,
tissue was frozen in isopentane at -80°C and stored at -80°C. The research protocols were
approved by the Medical Ethical Committee of the UMCG and the donors gave their
informed consent.
Preparation and incubation of liver slices
Human liver slices were prepared and incubated as described previously (24,25). Liver slices
were incubated at 37°C in Williams Medium E in the presence or absence of 100 µg/ml LPS.
At 5, 24, and 48 h after incubation, slices were frozen in liquid nitrogen and, as the media
samples, and stored at –80°C.
Human adipose tissue
In the study SAT and omentum biopsies were obtained from in total 21 Caucasian women
undergoing surgery because of benign gynecological problems. The women were in general
good health, had no history or symptoms of T2D or inflammatory diseases. The subjects were
aged between 30 and 45 years, with BMI ranging from 23 to 29. The SAT biopsies were
taken at the place of the incision, midline lower abdomen above the symfysis and under the
umbilicus. The omentum biopsies were taken at the lower edge of the omentum. Biopsies
were taken by means of scissors. The AT biopsies used for quantification studies by RTPCR
or ELISA were snap frozen in liquid nitrogen and stored in -80°C. The research protocols
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were approved by the Medical Ethical Committee of the UMCG and patients gave written
informed consent.
Adipose tissue culture
The human AT surgical biopsies were processed as described previously (26). The resulting
pieces of fat were cultured in six-well plates. Per well 0.5 g of fat tissue was cultured in 5ml
M199 medium (Gibco) supplemented with 50 µg/ml Gentamycine (Sigma) for 24 hrs at 37ºC
in 5% CO2 in the absence and presence of 100 µg/ml LPS. After the incubation the tissues
were frozen in liquid nitrogen and, as the media, stored in -80ºC.
Adipose tissue and liver tissue extracts
50 mg of liver tissue and 300 mg of fat tissue were homogenized in 500 µl lysis buffer
(Sigma) supplemented with inhibitors of proteases (Complete, Roche) in a homogenizer
(Precellys 24, Bertin Technology) with settings: 6500g, 2x 15 sec, 8 ºC. The tissue extracts
were centrifuged 5 min 12000g at 8°C and the resulting supernatant was aliquoted and stored
in -80ºC.
Protein assay
Protein concentration was determined with the Bradford assay according to the manufactures
instructions (Biorad).
Cytokine and resistin analysis
TNFα, IL-1β and IL-6 were analyzed by Bio-Plex Human Cytokine Assay manufactured by
BioRad (The Netherlands). The detection limits for IL-1β, IL-6, and TNFα were 0.8, 1.1, and
3 respectively. The intra- and inter-assay variation for IL-1β, IL-6, and TNFα were 2, 6, 5%
and 8.6, 7.2, and 5.3% respectively. Resistin was measured with Human Resistin Elisa Kit,
manufactured by LINCO (The Netherlands). The detection limit was 0.312 ng/ml and the
intra- and inter-assay variation was 4 and 18% respectively. The measurements were
performed according to the manufactures’ protocols.
RNA isolation and cDNA synthesis
RNA was extracted from AT using RNeasy Lipid Tissue Mini Kit (Qiagen, Venlo, the
Netherlands) according to the manufacturers’ instructions. RNA extraction from human liver
slices was performed as described previously (24). The RNA concentration was determined
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by Nano Drop ND-1000 Spectrophotometer (Isogen Ijsselstein, The Netherlands). cDNA
synthesis was performed from total RNA with QuantiTect Reverse Transcription Kit (Qiagen,
Venlo, the Netherlands) according to the manufacturers’ instructions.
Real-time quantitative PCR
Quantification of the expression of the genes of interest was done by QPCR on a ABI prism
7900 HT (Applied Biosystems) with the following cycling conditions: 15 min 95°C followed
by 40 cycles of 15s 95 ºC and 1 min 60 ºC. Reactions were performed in 10 µl volume and
contained 20 ng cDNA, 1x TaqMan PCR Master Mix (Applied Biosystems, Foster City, CA),
250 nM of each probe and 900 nM of each primer. For each gene, a standard curve was
generated and efficiency of the primers was determined as described previously (27). For
each primer pair the efficiency was about 95 %. Specific primer sets for β-2-microglobulin
(B2M), actinβ (ACTB), and ribosomal protein S18 (RPS18), IL-1β, IL-6, TNFα and resistin
were developed with Primer Express 1.5 (Applied Biosystems). Primers for CRP were
purchased from Applied Biosystems. The sequences for forward (F), reverse (R) primers and
the probe (P) were (5’-3’): RPS18 (F)CGGCTACCACATCCAAGGA;
(R)CCAATTACAGGGCCTCGAAA; (P)CGCGCAAATTACCCACTCCCGA; B2M
(F)TGACTTTGTCACAGCCCAAGATA; (R)AATCCAAATGCGGCATCTTC;
(P)TGATGCTGCTTACATGTCTCGATCCCA; ACTB (F)AGCGCGGCTACAGCTTCA;
(R)CGTAGCACAGCTTCTCCTTAATGTC; (P)ATTTCCCGCTCGGCCGTGGT; IL-1 β
(F)CTGATGGCCCTAAACAGATGAAG; (R)GGTCGGAGATTCGTAGC;
(P)TTCCAGGACCTGGACCTCTGCCCTC ; IL-6 (F)CCAGGAGCCCAGCTATGAAC ;
(R)CCCAGGGAGAAGGCAACTG; (P)CCTTCTCCACAAGCGCCTTCGGT; TNFα
(F)CTCGAACCCCGAGTGACAA; (R)AGCTGCCCCTCAGCTTGA;
(P)CCTGTAGCCCATGTTGTAGCAAACC; Resistin (F)GCCGGATTTGGTTAGCTGAG;
(R)GAGGAGGAGACAGAGAGCTTTCAT; (P) CCACCGAGAGGCGCCTGCAG;
Data were analyzed with SDS 2.0 software (Applied Biosystems). For each sample, the RT-
PCR reaction was performed twice in triplicate and the averages of the obtained threshold
cycle values (CT) were processed for further calculations. For normalization B2M, ACTB,
and RPS18 were used. Relative expression was calculated with ∆ (∆ (CT))-method (28).
Chapter 5
129
Cryosectioning and immunohistochemistry staining
Cryostat sections were prepared from human liver tissue as described previously (24). For
colocalization studies the rabbit anti-human-resistin 1:20 (Santa Cruz) was incubated with a
mouse anti-human- CD68 1:150 (DAKO ) or mouse anti-human CD31 1:50 DAKO) or
mouse anti-human actin 1:400 (Sigma). The antibodies were diluted in phosphate buffered
saline (PBS) supplemented with 5% normal human serum and applied 1 h in a humidity
chamber at RT. A single primary antibody for control staining was applied with the same
dilutions. After being rinsed with PBS endogenous peroxidase blocking was conducted with
0.037% H2O2 in PBS for 1 h. Afterwards sections were washed with PBS and sections
incubated with anti-resistin were incubated with secondary antibodies goat anti-rabbit IgG
conjugated with peroxidase (GARPO; DAKO) diluted 1:100 in PBS, supplemented with 5%
normal human serum for 30 minutes in a humidity chamber. After PBS washing the same
sections were incubated with tertiary antibodies rabbit anti-goat conjugated with peroxidase
(RAGPO, DAKO) diluted 1:100 in PBS with 5% normal human serum for 30 minutes in a
humidity chamber at RT. The peroxidase activity was visualized using 3, 3-
diamonobenzidine tetrahydrochloride (DAKO) for 10 min. Afterwards sections were washed
in PBS and incubated briefly with 0.1M Tris/HCl, 2mM MgCl2 pH 8.2. Sections stained with
anti-CD31, anti-CD68 and anti-actin were subsequently incubated with goat anti-mouse
conjugated with alkaline phosphatase (GAMAF, DAKO) 1:100 in PBS supplemented with
5% normal human serum for 30 minutes in a humidity chamber at RT. After washing in
PBS, the alkaline phosphatase reaction was conducted in a buffer containing 100ml 0.1M
Tris HCl pH 8.2 with 2 mM MgCl2, 20 mg Napthol AS-MX phosphate, 100 mg Fast Blue BB
and 48 mg levamisole for 30 minutes in a 37°C water bath. After the final PBS washing step,
the sections were covered with gelatin and a cover glass.
Light microscopy
Light microscopy images were taken with Olympus BX41 using Olympus Soft Imaging
System Cell^D at the magnification of X40.
Statistical analysis
QRTPCR experiments in Results 3 were performed with n=5 human livers and n=7 AT.
QRTPCR experiments in Results 3 were performed with n=10 livers and n=10 AT.
Immunostainings were performed in n=5 different livers. Cytokine analysis and resistin
Chapter 5
130
measurements in cultured tissues were done in duplicate for n=4 livers and n=4 AT. Results
were compared using Kruskal Wallis (KW) test or paired t-test; p value <0.05 was considered
significant.
Results
1. Relative gene expression and protein abundance of resistin in human liver and
AT
To determine resistin expression 5 human liver tissues and 7 AT biopsies (SAT and
omentum) were used. The housekeeping genes used in the normalization process were
selected according to the algorithm described in GeNorm (29). The best housekeeping genes
were B2M and ACTB. In addition we used the commonly applied RPS18 for normalization.
In human liver there was on average 8-fold (p= 0.011 KW), 23-fold (p=0.03 KW) and 3.5-
fold (p= 0.036 KW) higher expression of resistin then in SAT and omentum when normalized
with B2M (Fig. 1A), ACTB (Fig 1B), and RPS18 (Fig. 1C) respectively. There was no
significant difference between SAT and omentum in resistin mRNA expression. Resistin
protein levels were analyzed in both types of AT and liver extracts. The resistin
concentrations were on average 18-fold higher in liver than in SAT and omentum (p=0.0009
and p=0.008 respectively KW) while normalized per mg of tissue (Fig 1D). The resistin
concentration normalized on 1 µg of total protein was on average 2.6-times higher in liver
then in SAT and omentum (p=0.038, and p=0.05 resp. KW, Fig. 1E). There was no
significant difference in resistin protein abundance between SAT and omentum while
normalized on mg of tissue or µg of total protein (Fig 1D and 1E respectively).
Chapter 5
131
*#
0.00
1.00
2.00
3.00
4.00
SAT om. liver
resi
stin
ng
pe
r m
g o
f
tiss
ue
snap frozen biopsies
*#
0.0
10.0
20.0
30.0
SAT om. liver
resi
stin
ng
pe
r μ
g o
f
pro
tein
snap frozen biopsies
Figure 1.The relative gene expression of resistin in human adipose tissue (snap frozen biopsies) and liver (snap
frozen biopsies) normalized with B2M (1A), ACTB (1B) and RPS18 (1C). The expression in liver is expressed
relative to that in adipose tissue, which was set to 1. Resistin concentration in tissue extracts of adipose tissue
and liver tissue was normalized per mg of tissue weight (1D) and per µg of protein (1E). Kruskal-Wallis test was
used to calculate p-value signifying the difference in resistin gene expression between adipose tissue (n=7) and
liver (n=5) and resistin concentration between fat tissue (n=7) and liver extracts (n=5); p-value below 0.05 was
considered significant and indicated with * (liver vs. SAT) and with # (liver tissue vs. omentum).
2. Localisation of resistin in human liver
Immunohistochemical staining shows that resistin is present in Kupffer cells (colocalization
with CD68 a marker for the liver residual macrophages) (Fig. 2A), in a subset of endothelial
cells stained with CD31 (Fig. 2B) and occasionally in actin positive fibroblast like cells
colocalizing with actin beta (Fig 2 C).
snap frozen biopsies
*#
0
2
4
6
8
10
SAT om. liver
Re
lati
ve
ge
ne
ex
pre
ssio
n
vs
B2
M
snap frozen biopsies
*#
0
10
20
30
40
SAT om. liverR
ela
tiv
e e
xp
ress
ion
vs
AC
TB
snap frozen biopsies
*#
0
1
2
3
4
5
SAT om. liver
Re
lati
ve
ex
pre
ssio
n v
s
18
S
Fig. 1E
Fig. 1A
Fig. 1D
Fig. 1C Fig. 1B
Chapter 5
132
Figure 2. Light microscopy pictures of double immunostaining for resistin (red) with (A) Kupffer cells marker CD68 (blue); (B) endothelial cells marker CD31 (blue); (C) actin positive fibroblasts like cells (blue) in untreated liver tissue (isopentane frozen biopsies). The arrows indicate selected examples of double staining of resistin with one of the liver tissue cells markers.
3. Influence of LPS on cytokine and C-reactive protein (CRP) gene expression in
human liver and adipose tissue culture
After 24 h of incubation with LPS IL-1β and IL-6 were significantly upregulated in liver (Fig
3A and 3B resp.). TNFα was significantly upregulated after 5 h in liver (Fig 3C) and after 24
h the TNFα expression returned to the normal level (Fig 3D). SAT and omentum responded
to LPS in significant upregulation of IL-1β, IL-6, and TNFα, Figures 3 E-J. CRP was not
significantly changed in liver, SAT and omentum at any of the tested time points (data not
shown).
Chapter 5
133
Figure 3. The effect of LPS on the mRNA level of IL-1β (3A), IL-6 (3B), TNFα 5h (3C), and TNFα 24h (3D) in human liver slices. In SAT the effect of LPS on the mRNA level of IL-1β, IL-6, and TNFα is depicted in figures 3E-3G respectively. In omentum the effect of LPS on the mRNA level of IL-1β, IL-6, and TNFα is depicted in figures 3H-3J respectively. A paired t-test was used to calculate p-value signifying the difference between untreated and treated tissues; p-value below 0.05 was considered significant and indicated with *.
liver *
0
10
20
30
40
50
60
control LPS
Fo
ld i
nd
uc
tio
n I
L-1
β 2
4h
liver*
-1
0
1
2
3
4
5
6
7
control LPS
Fo
ld i
nd
uc
tio
n I
L-6
24
h
liver *
0
1
2
3
4
5
6
7
control LPS
Fo
ld i
nd
uc
tio
n T
NF
α 5
h
liver
0
0.5
1
1.5
2
2.5
control LPS
Fo
ld i
nd
uc
tio
n T
NF
α 2
4h
SAT *
0
50
100
150
200
cont. LPS
Fo
ld i
nd
uc
tio
n I
L-1
β
SAT*
0
2
4
6
8
10
12
14
16
cont. LPS
Fo
ld i
nd
uc
tio
n I
L-6
SAT*
0
1
2
3
4
5
6
cont. LPS
Fo
ld i
nd
uc
tio
n T
NF
α
omentum*
0
5
10
15
20
25
30
35
40
45
cont. LPS
Fo
ld i
nd
uc
tio
n I
L-1
β
omentum*
0
2
4
6
8
10
12
14
16
cont. LPS
Fo
ld i
nd
uc
tio
n I
L-6
omentum*
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
cont. LPS
Fo
ld i
nd
uc
tio
n T
NF
α
Fig. 3J Fig. 3I Fig. 3H
Fig. 3G Fig. 3F Fig. 3E
Fig. 3D Fig. 3C Fig. 3B Fig. 3A
Chapter 5
134
4. Influence of LPS on cytokine protein synthesis and secretion in human liver and
adipose tissue culture
After 24 h of LPS treatment IL-1β (4A), IL-6 (4B), and TNFα (4C) were significantly
upregulated in liver slices. The secretion of IL-1β, IL-6 and TNFα in liver culture media was
also significantly increased upon LPS treatment in comparison to untreated culture media
(Fig 4D, 4E, and 4F respectively). The LPS treatment in SAT and omentum cultures
significantly increased concentration of IL-1β, IL-6, and TNFα in tissues extracts (5A, 5C, 5E
resp.), and media (Fig 5B, 5D, and 5F resp.).
Figure 4. The effect of 24h LPS treatment on the IL-1β (A, D), IL-6 (B, E), and TNFα (C, F) protein
concentration in liver slices (A, B, C) and liver culture media (D, E, F). The liver slices were cultured for 24 h
in the absence or presence of LPS (100µg/ml). The paired t-test was used to calculate p-value signifying the
difference between untreated and treated media or slices; p-value equal or below 0.05 was considered significant
and indicated with *.
*
0
200
400
600
800
control LPS
IL-1
βp
g/m
g
liver slices
Fig. 4D
Fig. 4CFig. 4BFig. 4A
*
0
500
1000
1500
2000
control LPS
IL-6
pg
/mg
liver slices
*
0
100
200
300
400
control LPS
TN
Fα
pg
/mg
liver slices
*
0
100
200
300
400
control LPS
IL-6
pg
/mg
liver media*
0
1
2
3
4
5
6
control LPS
IL-1
βp
g/m
l
liver media
Fig. 4FFig. 4E
*
0
10
20
30
40
50
control LPS
TN
Fα
pg
/mg
liver media
Chapter 5
135
*
#
0
50
100
150
200
250
300
cont. LPS
IL-1
β p
g/m
lAT extracts
SAT
omentum*
#
0
5
10
15
20
cont. LPS
IL-1
β p
g/m
l
AT media
SAT
omentum
*
#
0
100
200
300
400
cont. LPS
IL-6
pg
/ml
AT extracts
SAT
omentum *
#
0
50
100
150
200
cont. LPS
IL-6
pg
/ml
AT media
SAT
omentum
*
#
0
10
20
30
cont. LPS
TN
Fα
pg
/ml
AT extracts
SAT
omentum
*
#
-5
0
5
10
15
cont. LPS
TN
Fα
pg
/ml
AT media
SAT
omentum
Figure 5. The effect of LPS on the IL-1β (5A, 5B), IL-6 (5C, 5D), and TNFα (5E, 5F) protein concentration in AT extracts and AT culture media. Human SAT and omentum explants (5A, 5C, 5E), and SAT and omentum culture media (5B, 5D, 5F) were cultured for 24 h in the absence or presence of LPS (100µg/ml). The paired t-test was used to calculate p-value signifying the difference between untreated and treated tissues or media; p-value equal or below 0.05 was considered significant and indicated with * (control SAT vs. LPS treated SAT) and with # (control omentum vs. LPS treated omentum).
Fig. 5F Fig. 5E
Fig. 5D Fig. 5C
Fig. 5B Fig. 5A
Chapter 5
136
5. Influence of LPS on resistin gene expression and protein level in adipose tissue
and liver
Resistin gene expression was measured in SAT and omentum after 24 h of LPS treatment and
in liver slices after 5, 24 and 48 h of LPS treatment. In SAT and omentum resistin mRNA
expression was significantly upregulated 16-fold, p=0.009 and 7-fold, p=0.005 respectively
(Fig 6 B). In liver slices there was no significant effect of LPS on resistin gene expression
after 5, 24 and 48 h (Fig 6A) although in 2 out of 10 livers an upregulation of 2.5- till 7- fold
at 24 and 48 hrs was observed. Resistin protein level in liver and AT extracts (Fig 6C, 6D
resp.) and concentration in liver and AT culture media was not significantly altered by the 24
h LPS treatment (Fig 6E and F respectively).
Chapter 5
137
0
1
2
3
4
cont. LPS
Fold
in
du
ctio
n r
esi
stin
liver slices
5h
24h
48h
0
0.2
0.4
0.6
0.8
1
1.2
1.4
cont. LPS
res
isti
n n
g/m
l
liver media
Figure 6. The effect of LPS on the resistin mRNA expression in liver (6A), SAT and omentum (6B). The liver slices were treated without/with LPS for 5, 24, and 48 hrs. The SAT and omentum were treated without/with LPS for 24hrs. The resistin protein level in liver slice extracts (6C), SAT and omentum (6D) extracts upon 24 hrs of incubation in absence or presence of LPS. The resistin protein level in liver culture media (6E), SAT and omentum culture media (6F) upon 24 hrs of incubation in absence or presence of LPS. The paired t-test was used to calculate p-value signifying the difference between untreated and treated tissues; p-value equal or below 0.05 was considered significant and indicated with * (SAT control vs. SAT LPS treated) and # (omentum control vs. omentum LPS treatment).
*
#
0
5
10
15
20
25
con. 24h LPS 24hFo
ld i
nd
uc
tio
n r
esi
stin
AT
SAT
omentum
0
1
2
3
4
5
cont. 24h LPS 24h
resi
stin
ng
/mg
liver slices
Fig. 6D Fig. 6C
Fig. 6B Fig. 6A
0
0.02
0.04
0.06
0.08
0.1
cont. 24h LPS 24h
resi
stin
ng
/ml
AT extracts
SAT
omentum
0
0.001
0.002
0.003
0.004
cont. LPS
res
isti
n n
g/m
l
AT media
SAT
Fig. 6F Fig. 6E
Chapter 5
138
Discussion
In the present study we aimed to (1) quantify resistin mRNA and protein abundance in human
liver, SAT and omentum; (2) identify in which cell types in the liver resistin protein is
localized, and (3) study resistin gene and protein regulation during LPS-induced
inflammation, in liver, SAT and omentum.
Resistin mRNA showed significantly higher relative gene expression in liver compared to
SAT and omentum. The resistin gene mRNA expression in SAT and omentum was not
significantly different. We realize that relative quantification in two different organs (liver
and AT) is problematic because (1) it is very difficult to find a housekeeping gene that is
equally expressed in the two different tissues, and (2) the selection of the commonly used
housekeeping genes is based on assumptions and observations made in different biological
systems (30). In order to diminish this problem we decided to use 3 different housekeeping
genes. Moreover the unnormalized Ct values obtained for the liver (avg. Ct = 29, n=5) were
lower than in AT (avg. Ct=34, n=7), when the same amount of cDNA is used, which supports
our normalized gene expression data and indicatives higher resistin expression in liver than in
AT. We also compared the CD68 mRNA level in AT (both SAT and omentum) and liver and
there was no significant difference between any of these tissues (data not shown), indicating
that the difference in resistin expression between AT and liver is not caused by a difference in
the number of macrophages/Kupffer cells coexpressing resistin.
The protein data obtained for liver and AT extracts are in consistence with the gene
expression data and demonstrate that resistin is more abundant in the liver. The resistin level
in liver tissue extracts normalized per mg of tissue is 18- fold higher than in SAT and
omentum and the resistin level in liver tissue extracts normalized per total protein is 2.5-fold
higher in liver than in both types of AT. As the amount of protein per mg tissue differs
widely between fat and liver tissue it seems more relevant to normalize on weight of tissue
instead of normalizing on total protein level for a proper estimation of the physiological
levels of resistin in these tissues.
The localization studies showed presence of resistin in Kupffer cells (90% of the cells were
positive). This finding was not unexpected because resistin is also present in human
circulating macrophages and macrophages of AT (6,31). Previously it was suggested that
hepatocytes could produce resistin (19,22), but we could not confirm this observation.
Furthermore, we detected resistin in a subset of endothelial cells (5%). This finding is
Chapter 5
139
consistent with data linking resistin with inflammation and a profound role of endothelial
cells in inflammatory processes. In addition it was previously shown that resistin induces
human endothelial cell proliferation and migration, promotes capillary-like tube formation
thereby implying a role in angiogenesis-associated vascular disorders (21,32). The presence
of resistin in actin positive fibroblasts cells, which can become activated stellate cells during
inflammation, is in agreement with former findings showing that resistin expression is linked
to areas of inflammatory cell accumulation (19).
Our next aim was to challenge SAT, omentum and liver with LPS in order to induce an
inflammatory reaction and analyze if resistin expression both on mRNA and protein level is
altered. We previously validated the human liver slice model for study of inflammatory
reactions (24,33,34). The liver slices cultured with LPS for 24 h significantly upregulated IL-
1β and IL-6. TNFα was significantly upregulated after 5 h but after 24 h the level returned to
basal, confirming earlier data (24,34). The cytokines mRNA expression data were consistent
with the protein data. Upon LPS treatment we detected significant upregulation of IL-1β, IL-
6 and TNFα in both liver slices and culture media. In our study CRP, the classical acute phase
protein and the most extensively studied systemic marker of inflammation (35,36) was not
upregulated by LPS treatment at any of the tested time points on mRNA level either. In
contrast, it was shown previously that CRP was upregulated by LPS in peripheral blood
mononuclear cells (PBMC) both on mRNA and protein level in vivo (37). Previously we
found that CRP is upregulated in human liver slices by incubation with high concentration of
TNFα (15 ng/ml) (33) . Therefore we speculate that in the present study, the level of TNFα in
the liver tissue does not reach the level necessary for CRP induction.
The LPS-treatment of SAT and omentum for 24 hrs resulted in upregulation of mRNA for
IL-1β, IL-6 and TNFα. Also in AT the expression of CRP was not affected by LPS. The
mRNA data for IL-1β, IL-6 and TNFα were consistent with the protein data obtained for
cultured SAT and omentum and culture media. Based on these results we concluded that
treatment of AT in vitro with LPS is sufficient to induce an inflammatory reaction and
mimics inflammatory processes in vivo with upregulated cytokines. Furthermore LPS-treated
omentum synthesized and secreted significantly higher levels of cytokines than SAT what is
in line with the literature data pointing out towards more proinflammatory character of
omentum than SAT (38-40).
SAT and omentum treated for 24h with LPS responded with a significant upregulation of
resistin mRNA. However the resistin protein level in SAT and omentum tissue extracts and
Chapter 5
140
media was not affected by LPS. In the liver we did not detect a significant effect of LPS on
resistin gene expression and protein level in tissue or media at any of the tested time points.
Based on these results we could not find evidence that resistin is significantly upregulated in
human liver during 24hrs LPS-induced inflammation despite upregulation of IL-1β, IL-6 or
TNFα.
On the other hand we observed 2 livers responded to LPS by increased resistin protein
secretion after 48 hrs (5- and 3-fold), but not after 24h. The resistin mRNA expression was
increased in the same livers after 24 and 48 hrs (on average 2.5- and 7-fold respectively).
Although these results were obtained in only 2 out of 10 livers and were not significant for
n=10, this observation suggests that resistin could indeed be an inflammatory marker but a
conditional one, because the increase in resistin mRNA expression/synthesis occurred in
some livers only. The finding that resistin mRNA was significantly upregulated by LPS in
SAT and omentum without any protein changes might be explained by assuming that also in
AT resistin protein production occurs at 48hrs. We also could not exclude a proteolytic
resistin degradation, but this would imply increased proteolytic activity after the LPS
treatment compared to untreated tissues/media.
In summary, the remarkable high resistin mRNA expression and protein content in fresh liver
tissue compared with SAT and omentum suggests that resistin should not be considered only
as an adipokine and that the resistin-related research should be more committed towards its
role in the liver physiology. Furthermore higher resistin secretion in liver slices during
incubation compared to AT indicates that liver may contribute more to the resistin blood level
than AT. In the liver resistin is present in Kupffer cells (~90%), a subset of endothelial cells
(~10%) and actin positive fibroblasts like cells (~5%). During LPS induced inflammation
omentum and in a minor extend SAT, in addition to the liver, contribute to the total blood
concentrations of IL-1β, IL-6 and TNFα which are indicative for inflammation in vitro and in
vivo. Resistin mRNA expression was significantly upregulated during inflammatory reactions
in SAT and omentum, but not liver tissue. Further research is needed to reveal the exact
mechanism of the resistin regulation. Therefore the application of resistin as an inflammatory
marker in T2D and CVD should be considered critically.
Chapter 5
141
Acknowledgements
We would like to thank to Anne-miek van Loenen-Weemaes and Alie de Jager-Krikken for
excellent technical assistance and Prof Klaas Poelstra discussing the light microscopy images.
References
1. Steppan CM, Bailey ST, Bhat S, Brown EJ, Banerjee RR, Wright CM, Patel HR, Ahima RS, Lazar MA
2001 The hormone resistin links obesity to diabetes. Nature 409:307-312. 2. Pang SS, Le YY 2006 Role of resistin in inflammation and inflammation-related diseases. Cell Mol
Immunol 3:29-34. 3. Muse ED, Lam TK, Scherer PE, Rossetti L 2007 Hypothalamic resistin induces hepatic insulin
resistance. J Clin Invest 117:1670-1678. 4. Savage DB, Sewter CP, Klenk ES, Segal DG, Vidal-Puig A, Considine RV, O'Rahilly S 2001 Resistin
/ Fizz3 expression in relation to obesity and peroxisome proliferator-activated receptor-gamma action in humans. Diabetes 50:2199-2202.
5. Nagaev I, Smith U 2001 Insulin resistance and type 2 diabetes are not related to resistin expression in human fat cells or skeletal muscle. Biochem Biophys Res Commun 285:561-564.
6. Patel L, Buckels AC, Kinghorn IJ, Murdock PR, Holbrook JD, Plumpton C, Macphee CH, Smith SA 2003 Resistin is expressed in human macrophages and directly regulated by PPAR gamma activators. Biochem Biophys Res Commun 300:472-476.
7. McTernan PG, McTernan CL, Chetty R, Jenner K, Fisher FM, Lauer MN, Crocker J, Barnett AH, Kumar S 2002 Increased resistin gene and protein expression in human abdominal adipose tissue. J Clin Endocrinol Metab 87:2407.
8. McTernan CL, McTernan PG, Harte AL, Levick PL, Barnett AH, Kumar S 2002 Resistin, central obesity, and type 2 diabetes. Lancet 359:46-47.
9. Virkamaki A, Yki-Jarvinen H 1994 Mechanisms of insulin resistance during acute endotoxemia. Endocrinology 134:2072-2078.
10. Sugita H, Kaneki M, Tokunaga E, Sugita M, Koike C, Yasuhara S, Tompkins RG, Martyn JA 2002 Inducible nitric oxide synthase plays a role in LPS-induced hyperglycemia and insulin resistance. Am J Physiol Endocrinol Metab 282:E386-E394.
11. Agwunobi AO, Reid C, Maycock P, Little RA, Carlson GL 2000 Insulin resistance and substrate utilization in human endotoxemia. J Clin Endocrinol Metab 85:3770-3778.
12. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, Neyrinck AM, Fava F, Tuohy KM, Chabo C, Waget A, Delmee E, Cousin B, Sulpice T, Chamontin B, Ferrieres J, Tanti JF, Gibson GR, Casteilla L, Delzenne NM, Alessi MC, Burcelin R 2007 Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56:1761-1772.
13. Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM, Burcelin R 2008 Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice. Diabetes 57:1470-1481.
14. Kaser S, Kaser A, Sandhofer A, Ebenbichler CF, Tilg H, Patsch JR 2003 Resistin messenger-RNA expression is increased by proinflammatory cytokines in vitro. Biochem Biophys Res Commun 309:286-290.
Chapter 5
142
15. Lu SC, Shieh WY, Chen CY, Hsu SC, Chen HL 2002 Lipopolysaccharide increases resistin gene expression in vivo and in vitro. FEBS Lett 530:158-162.
16. Lehrke M, Reilly MP, Millington SC, Iqbal N, Rader DJ, Lazar MA 2004 An inflammatory cascade leading to hyperresistinemia in humans. PLoS Med 1:e45.
17. Rajala MW, Lin Y, Ranalletta M, Yang XM, Qian H, Gingerich R, Barzilai N, Scherer PE 2002 Cell type-specific expression and coregulation of murine resistin and resistin-like molecule-alpha in adipose tissue. Mol Endocrinol 16:1920-1930.
18. Fasshauer M, Klein J, Neumann S, Eszlinger M, Paschke R 2001 Tumor necrosis factor alpha is a negative regulator of resistin gene expression and secretion in 3T3-L1 adipocytes. Biochem Biophys Res Commun 288:1027-1031.
19. Bertolani C, Sancho-Bru P, Failli P, Bataller R, Aleffi S, DeFranco R, Mazzinghi B, Romagnani P, Milani S, Gines P, Colmenero J, Parola M, Gelmini S, Tarquini R, Laffi G, Pinzani M, Marra F 2006 Resistin as an intrahepatic cytokine: overexpression during chronic injury and induction of proinflammatory actions in hepatic stellate cells. Am J Pathol 169:2042-2053.
20. Pagano C, Soardo G, Pilon C, Milocco C, Basan L, Milan G, Donnini D, Faggian D, Mussap M, Plebani M, Avellini C, Federspil G, Sechi LA, Vettor R 2006 Increased serum resistin in nonalcoholic fatty liver disease is related to liver disease severity and not to insulin resistance. J Clin Endocrinol Metab 91:1081-1086.
21. Reilly MP, Lehrke M, Wolfe ML, Rohatgi A, Lazar MA, Rader DJ 2005 Resistin is an inflammatory marker of atherosclerosis in humans. Circulation 111:932-939.
22. Sheng CH, Di J, Jin Y, Zhang YC, Wu M, Sun Y, Zhang GZ 2008 Resistin is expressed in human hepatocytes and induces insulin resistance. Endocrine 33:135-143.
23. Brouwers MA, Peeters PM, de Jong KP, Haagsma EB, Klompmaker IJ, Bijleveld CM, Zwaveling JH, Slooff MJ 1997 Surgical treatment of giant haemangioma of the liver. Br J Surg 84:314-316.
24. Elferink MG, Olinga P, Draaisma AL, Merema MT, Faber KN, Slooff MJ, Meijer DK, Groothuis GM 2004 LPS-induced downregulation of MRP2 and BSEP in human liver is due to a posttranscriptional process. Am J Physiol Gastrointest Liver Physiol 287:G1008-G1016.
25. Olinga P, Merema M, Hof IH, de Jong KP, Slooff MJ, Meijer DK, Groothuis GM 1998 Effect of human liver source on the functionality of isolated hepatocytes and liver slices. Drug Metab Dispos 26:5-11.
26. Alvarez-Llamas G, Szalowska E, de Vries MP, Weening D, Landman K, Hoek A, Wolffenbuttel BH, Roelofsen H, Vonk RJ 2007 Characterization of the human visceral adipose tissue secretome. Mol Cell Proteomics 6:589-600.
27. Pfaffl MW 2001 A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:e45.
28. Livak KJ, Schmittgen TD 2001 Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25:402-408.
29. Vandesompele J, De PK, Pattyn F, Poppe B, Van RN, De PA, Speleman F 2002 Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:RESEARCH0034.
30. de Jonge HJ, Fehrmann RS, de Bont ES, Hofstra RM, Gerbens F, Kamps WA, de Vries EG, van der Zee AG, te Meerman GJ, ter EA 2007 Evidence based selection of housekeeping genes. PLoS ONE 2:e898.
31. Curat CA, Wegner V, Sengenes C, Miranville A, Tonus C, Busse R, Bouloumie A 2006 Macrophages in human visceral adipose tissue: increased accumulation in obesity and a source of resistin and visfatin. Diabetologia 49:744-747.
32. Mu H, Ohashi R, Yan S, Chai H, Yang H, Lin P, Yao Q, Chen C 2006 Adipokine resistin promotes in vitro angiogenesis of human endothelial cells. Cardiovasc Res 70:146-157.
33. Nijsten MW, Olinga P, The TH, de Vries EG, Koops HS, Groothuis GM, Limburg PC, ten Duis HJ, Moshage H, Hoekstra HJ, Bijzet J, Zwaveling JH 2000 Procalcitonin behaves as a fast responding acute phase protein in vivo and in vitro. Crit Care Med 28:458-461.
Chapter 5
143
34. Elferink MG, Olinga P, Draaisma AL, Merema MT, Bauerschmidt S, Polman J, Schoonen WG, Groothuis GM 2008 Microarray analysis in rat liver slices correctly predicts in vivo hepatotoxicity. Toxicol Appl Pharmacol 229:300-309.
35. Casas JP, Shah T, Hingorani AD, Danesh J, Pepys MB 2008 C-reactive protein and coronary heart disease: a critical review. J Intern Med 264:295-314.
36. Pepys MB, Hirschfield GM, Tennent GA, Gallimore JR, Kahan MC, Bellotti V, Hawkins PN, Myers RM, Smith MD, Polara A, Cobb AJ, Ley SV, Aquilina JA, Robinson CV, Sharif I, Gray GA, Sabin CA, Jenvey MC, Kolstoe SE, Thompson D, Wood SP 2006 Targeting C-reactive protein for the treatment of cardiovascular disease. Nature 440:1217-1221.
37. Haider DG, Leuchten N, Schaller G, Gouya G, Kolodjaschna J, Schmetterer L, Kapiotis S, Wolzt M 2006 C-reactive protein is expressed and secreted by peripheral blood mononuclear cells. Clin Exp Immunol 146:533-539.
38. Wajchenberg BL 2000 Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 21:697-738.
39. Wajchenberg BL, Giannella-Neto D, da Silva ME, Santos RF 2002 Depot-specific hormonal characteristics of subcutaneous and visceral adipose tissue and their relation to the metabolic syndrome. Horm Metab Res 34:616-621.
40. Poulain-Godefroy O, Lecoeur C, Pattou F, Fruhbeck G, Froguel P 2008 Inflammation is associated with a decrease of lipogenic factors in omental fat in women. Am J Physiol Regul Integr Comp Physiol 295:R1-R7.
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Chapter 6
Comparative analysis of the human hepatic and
adipose tissue transcriptomes during LPS-
induced inflammation leads to the
identification of differential biological
pathways and candidate biomarkers
Ewa Szalowska
Martijn Dijkstra
Marieke G.L. Elferink
Desiree Weening
Marcel de Vries
Marcel Bruinenberg
Annemieke Hoek
Han Roelofsen
Geny M.M. Groothuis
Roel J. Vonk
Submitted
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Abstract
Background
Insulin resistance (IR) is accompanied by chronic low grade systemic inflammation, obesity,
and deregulation of total body energy homeostasis. We induced inflammation in adipose and
liver tissues in vitro in order to mimic inflammation in vivo with the aim to identify tissue-
specific processes implicated in IR and to find biomarkers indicative for tissue-specific IR.
Methods
Human adipose and liver tissues were cultured in the absence or presence of LPS and DNA
Microarray Technology was applied for their transcriptome analysis. Gene Ontology (GO),
gene functional analysis, and prediction of genes encoding for secretome were performed
using publicly available bioinformatics tools (DAVID, STRING, SecretomeP). The
transcriptome data were validated by proteomics analysis of the inflamed adipose tissue
secretome.
Results
LPS treatment significantly affected 667 and 484 genes in adipose and liver tissues
respectively. The GO analysis revealed that during inflammation adipose tissue, compared to
liver tissue, had more significantly upregulated genes, GO terms, and functional clusters
related to inflammation and angiogenesis. The secretome prediction led to identification of
399 and 236 genes in adipose and liver tissue respectively. The secretomes of both tissues
shared 66 genes and the remaining genes were the differential candidate biomarkers
indicative for inflamed adipose or liver tissue. The transcriptome data of the inflamed adipose
tissue secretome showed excellent correlation with the proteomics data.
Conclusions
The higher number of altered proinflammatory genes, GO processes, and genes encoding for
secretome during inflammation in adipose tissue compared to liver tissue, suggests that
adipose tissue is the major organ contributing to the development of systemic inflammation
observed in IR. The identified tissue specific functional clusters and biomarkers might be
used in a strategy for the development of tissue-targeted treatment of insulin resistant
patients.
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Background
Adipose tissue is an important metabolic and endocrine organ that secretes numerous
biologically active proteins (adipokines) such as leptin, adiponectin, many cytokines, and
chemokines [1]. During the development of obesity, adipose tissue undergoes a switch from
being mainly a metabolic organ towards an organ that shows substantial pro-inflammatory
activity, associated with decreased insulin sensitivity, declined expression of adiponectin and
enhanced production of pro-inflammatory cytokines and chemokines. These processes are
believed to lead to low-grade inflammation and eventually systemic insulin resistance (IR)
and type 2 diabetes (T2D) [2]. However, it is not yet understood how the change in the
inflamed adipose tissue transcriptome and secretome leads to the development of IR. In
addition to adipose tissue, the liver as an important metabolic and endocrine organ secreting
many hormones, chemokines and cytokines, is also affected in obesity [3,4]. In a fatty liver,
inflammation with activated NF-κB signaling and upregulated cytokines (IL-6, TNFα, and
IL-1β) seems to be a pivotal event leading to the development of liver insulin resistance and
non-alcoholic fatty liver disease (NAFLD) which both strongly predispose to the
development of systemic IR and T2D. Except for the few proteins known to be produced and
secreted by the liver during inflammation little is known about other protein factors which
alone or by interacting with the secretome of inflamed adipose tissue could contribute to the
development of systemic inflammation and insulin resistance in humans [5-8].
Lipopolysachcaride (LPS) is a compound of the cell wall of Gram-negative bacteria which
induces inflammatory reactions and upregulates many cyto- and chemokines via TLRs.
Besides its role in inflammation LPS triggers hyperglycemia and IR in rats and humans [9-
12] and induces weight gain and liver IR in mice [13,14].
In our studies, we aimed to identify molecular processes affected during inflammation in
human AT and LT in order to better understand their roles in the inflammation- related
development of IR/T2D in vivo. Therefore we challenged human adipose tissue (omentum)
and liver tissue slices with LPS and analyzed gene expression changes by DNA microarray
technology and performed Gene Ontology (GO), gene functional classification/clustering
analysis by means of publicly available bioinformatics tools Database for Annotation,
Visualization, and Integrated Discovery (DAVID) and Search Tool for the Retrieval of
Interacting Genes/Proteins (STRING).
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Additionally, we aimed to compare the secretomes of adipose and liver tissues during
inflammation in order to better understand how these two organs can contribute to the
development of systemic inflammation and IR. The transcriptome data were used to predict
genes encoding for secreted proteins, by means of SecretomeP. The comparative analysis of
the predicted secretomes led to the identification of differential candidate biomarkers for the
inflamed adipose tissue and the inflamed liver tissue. Significantly changed genes detected in
the adipose tissue secretome, but not in the inflamed liver tissue secretome were considered
as the top candidate biomarkers related to inflammation of adipose tissue and these
transcriptome data were confirmed by proteomics analysis of the inflamed adipose tissue
culture medium.
The identified biological processes and biomarkers indicative for the inflamed adipose tissue
or the inflamed liver tissue might be used for tissue-specific diagnosis of insulin resistance
related to inflammation and thereby facilitate more targeted treatment of insulin resistant
patients.
Methods
Human liver tissue
Human liver tissue (n=5) was obtained and prepared as described previously [15]. The donors
of livers were healthy males aged 16–34 years, with BMI 23.1–27.7. The information about
the medical history was not available. The research protocols conformed the Helsinki
Declaration, were approved by the local Medical Ethical Committee of the UMCG, and
patients gave written informed consent to participate in the study.
Preparation and incubation of liver slices
Human liver slices were prepared and incubated as described previously [15]. Liver slices
were incubated at 37°C in Williams Medium E in the presence or absence of 100 µg/ml LPS.
24 h after incubation, slices were frozen in liquid nitrogen and stored at –80°C.
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Human adipose tissue
Omentum AT biopsies were obtained from 7 Caucasian women undergoing surgery because
of benign gynecological problems. The women were in general good health, had no history or
symptoms of T2D or inflammatory diseases. The subjects were aged between 30 and 45
years, with BMI ranging from 23 to 29.The omentum biopsies were taken at the lower edge
of the omentum using scissors. The research protocols conformed the Helsinki Declaration,
were approved by the local Medical Ethical Committee of the UMCG, and patients gave
written informed consent to participate in the study.
Preparation and incubation of adipose tissue biopsies
The human AT surgical biopsies were processed as described previously [16,17]. In our
studies AT was cultured in the absence/presence of LPS (100µg/ml) for 24 hours. After the
culture time the fat tissue was snap-frozen in liquid nitrogen and stored in -80ºC until further
processing.
RNA isolation
RNA was extracted from adipose tissue using RNeasy Lipid Tissue Mini Kit (Qiagen, Venlo,
The Netherlands) according to the manufacturer’s instructions. RNA extraction from human
liver slices was performed as described previously [15]. The RNA concentration was
determined by Nano Drop ND-1000 Spectrophotometer (Isogen Ijsselstein, The Netherlands).
The quality of total RNA was evaluated by capillary electrophoresis using an Agilent 2100
Bioanalyzer (Agilent Technologies, Palo Alto, Calif.).
Illumina Human WG8-v2 Microarray Analysis
The Illumina platform was used for the gene expression analysis in adipose tissue. Biotin-
labeled cRNA was generated from high-quality total RNA with the Illumina TotalPrep RNA
amplification kit (Ambion). Briefly, 50 ng of total RNA was reversely transcribed with an
oligo(dT) primer containing a T7 promoter. The first- strand cDNA was used to make the
second strand. The purified second-strand cDNA, along with biotin UTPs, was subsequently
used to generate biotinylated, antisense RNA of each mRNA in an in vitro transcription
reaction. The size distribution profile for the labeled cRNA samples was evaluated by
Bioanalyzer. After RNA labeling, 1.5ug of purified, labeled cRNA from each sample was
hybridized at 55ºC overnight with a Human-8 v2 expression Illumina Beadchip targeting
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22000 transcripts. The beadchip was washed the following day. A signal was developed
during incubation with Streptavidin-Cy3, and each chip was scanned with an Illumina Bead
Array Reader.
The preprocessing of Illumina data was performed using the BeadStudio package with default
settings. The background was subtracted and quantile normalization performed. Probes with
“absent” signals in all samples (lower than or near to background levels) were removed from
further analysis. To identify the differentially expressed genes in LPS treated samples versus
controls an eBayes test was performed and Benjamini Hochberg corrected false discovery
rate (FDR) ≤0.05. Probes with fold change ≥2 were used for further analysis. The
calculations were performed in R, a language for statistical computing and graphics (www.R-
project.org).
Affymetrix Human Genome U133 Plus 2.0 Array Analysis
The Affymetrix platform (55000 transcripts) was used for the liver tissue gene expression
analysis. Double-stranded cDNA was synthesized from 1.5 µg total RNA using the One-
Cycle Target Labeling Kit (Affymetrix Santa Clara, CA), and used as a template for the
preparation of biotin-labeled cRNA using the GeneChip IVT Labeling Kit (Affymetrix Santa
Clara, CA). Biotin-labeled cRNA was fragmented at 1 µg/µl following the manufacturer's
protocol. After fragmentation, cRNA (10µg) was hybridized at 45°C for 16 hours to the
Human Genome U133 Plus 2.0 array (Affymetrix, Santa Clara, CA). Following
hybridization, the arrays were washed, stained with phycoerythrin-streptavidin conjugate
(Molecular Probes, Eugene, OR), and the signals were amplified by staining the array with
biotin-labeled anti-streptavidin antibody (Vector Laboratories, Burlingame, CA) followed by
phycoerythrin-streptavidin. The arrays were laser scanned with a GeneChip Scanner 3000 7G
(Affymetrix, Santa Clara, CA) according to the manufacturer's instructions. Data was saved
as raw image file and quantified using GCOS (Affymetrix).
Probe set summarization was performed using the RMA algorithm. Subsequently, baseline
subtraction was performed setting the baseline to the median of all samples. To identify the
differentially expressed genes in LPS treated samples versus controls an eBayes test was
performed and Benjamini Hochberg corrected false discovery rate (FDR) ≤0.05. Probes with
fold change ≥2 were used for further analysis. The calculations were performed in R, a
language for statistical computing and graphics (www.R-project.org).
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Gene Functional Classification Analysis
The significant transcriptomes of AT and LT were uploaded to Database for Annotation,
Visualization, and Integrated Discovery (DAVID) Bioinformatics Resource where the Gene
Functional Classification tool was applied to generate clusters of functionally related genes.
Additionally the Functional Annotation Clustering tool was used to generate clusters of
overrepresented Gene Ontology (GO) terms [18,19]. The HG-U133 Plus 2 and
HUMANREF-8 V2 0 R3 11223162A were used as a background for the GO analysis of liver
tissue and adipose tissue respectively. The GO terms after correction for FDR at p≤0.05
(Benjamini Hochberg) were selected for further analysis and interpretation.
Gene networks and pathways identification
The significant transcriptomes of adipose and liver tissues were uploaded to Search Tool for
the Retrieval of Interacting Genes/Proteins 8.2 (STRING) where networks based on known
and predicted protein-protein interactions were built and clustered into functional categories
[20].
Secretome prediction
From the significant transcriptome data obtained for adipose and liver tissues, the secretome
prediction was performed with in-house developed software, which retrieved the
information about the predicted secretomes from SecretomeP [21]. Genes were considered to
belong to the secretome when they encoded for proteins with a predicted signal peptide
(present in proteins that are secreted via the classical endoplasmic reticulum/Golgi-dependent
pathway) or when their Neuronal Network (NN) score exceeded the value of 0.5, which
classifies them as secreted via the non-classical pathway. Genes encoding for proteins which
did not have a signal peptide nor had the NN-score below 0.5 were considered as genes
encoding for intracellular proteins and were discarded from the final secretome analysis.
Adipose tissue culture for the quantitative proteomics analysis
Quantitative secretome analysis was performed by Isotope-labeled Amino Acid Incorporation
Rates (CILAIR) as described previously [22]. Briefly, 6g of fat tissue was used from one
patient and divided into six Petri dishes containing 10 ml of lysine-free M199 medium
(reference number 22340 Lys-free, Invitrogen) to deplete lysine from other sources (blood in
the tissue) and supplemented with 50 µg/ml gentamicin. The tissue was incubated for 24 h.
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After this period, fresh M199 containing 70 mg/liter 13C-labeled lysine (L-
[13C6,14N2]lysine (Invitrogen) was added to all dishes for the next 24 hours to allow
incorporation of the label into newly synthesized proteins, in the absence (3 dishes) or
presence (3 dishes) of LPS (100 µg/ml). CILAIR is based on the incorporation rate of 13C-
labeled lysine in newly synthesized secreted proteins. If this rate is different between two
conditions for a specific protein the change in expression of this protein can be calculated by
comparing the heavy/light ratios for the two conditions. After the 24 h incubation, media
were collected and stored at –80ºC until further processing. The sample preparation and
protein identification by liquid chromatography coupled to mass spectrometry was performed
as described previously [22]. ProteinPilot 2.0 software (Applied Biosystems) was used to
analyze the mass spectra using the UniprotKB/Swiss-Prot database (release 54, January 2008,
276,256 entries). The settings used in the analysis were the same as described previously
[22].
CILAIR data analysis
The statistical analysis to detect differences in the secretome of LPS-treated vs. control
adipose tissue cultures was performed with in-house generated software that was developed
using the open source MOLGENIS toolbox [23]. A two-sided unpaired Student’s t-test was
applied, and multiple testing correction was performed to control the false discovery rate
(FDR) at FDR < 0.05.
The applied criteria for the proteins predicted to be secreted were the same as described
above for the transcriptome data.
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Results
Functional gene annotation analysis
The transcriptome data analysis revealed that in adipose tissue 667 genes were significantly
affected (322 were upregulated and 345 were downregulated) after exposure to LPS. In liver
tissue we detected 483 significantly changed genes (283 were upregulated and 200 were
downregulated). The overlapping significant transcriptome shared by both tissues consisted
of 82 transcripts. The significantly changed genes found in adipose tissue and liver tissue
which were not present on both platforms were discarded from further analysis (47 and 42
respectively). Functional gene annotation analysis of significantly upregulated genes in
adipose tissue (including the overlapping genes with the liver tissue significant
transcriptome) led to the identification of functional groups such as: chemokines; growth and
differentiation of hematopoietic precursors; (anti)apoptosis; modulation of immune response;
T-, B-, leukocytes, and NK-cells activation, suppression of cytokine signaling (SOCS),
extracellular matrix remodeling, and upregulation of numerous transporters, (supplementary
Table 1A). Within the downregulated gene functional groups we identified:
lysosomal/endosomal system activity, basement membrane components, extracellular matrix
components, cell adhesion and migration, deoxy-ribonucleases activity, and detoxification,
(supplementary Table 1B). A similar analysis was performed for liver tissue and within the
upregulated gene functional groups we identified : chemokines; matrix remodeling;
(anti)apoptosis; cell adhesion and migration; T- and NK- cell activity; and breakdown of
extracellular matrix/tissue remodeling, (supplementary Table 1C). The functional
classification of the downregulated genes led to identification of groups such as: amino acid
metabolism, membrane activity, redox/detoxification reactions, cell adhesion and
mitochondrial functions, (supplementary Table 1D). Additionally, in order to better visualize
the similarities and differences between the adipose tissue and liver tissue transcriptomes
during inflammation we performed gene functional network reconstruction in STRING,
Figures 1-5.
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Figure 1. The gene functional clusters identified for the significant, overlapping adipose and liver tissue transcriptomes. The overlapping (shared) upregulated adipose tissue and liver tissue significantly changed transcriptome. Within the overlapping network we identified functional clusters related to : T and B cell activation and functioning (pink); matrix remodeling (green); interleukin 7 receptor activity (yellow); mobilization of T-lymphocytes and monocytes (red); (anti)apoptosis/inflammation(blue).
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Figure 2. The gene functional clusters identified for the significant, upregulated adipose tissue transcriptome. The upregulated adipose tissue network contained 8 functional clusters: regulation of cytokine signaling (grey); glucocorticoid receptor signaling (red); acute phase response (pink); growth and differentiation of hematopoietic cells (dark blue); plasminogen activation system (bright blue); IL-10 signaling (light blue); apoptosis (green); cell adhesion (yellow).
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Figure 3. The gene functional clusters identified for the significant, downregulated adipose tissue transcriptome. The downregulated adipose tissue network had 6 functional clusters: PPARγ signaling (red); cellular defense against toxic compounds (green); redox reactions (dark blue); innate immune system (bright blue); G-receptor signaling (black); Wnt-signaling (pink).
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Figure 4. The gene functional clusters identified for the significant, upregulated liver tissue transcriptome. The liver tissue upregulated network consisted of 6 clusters: JAK-STAT signaling (light blue); NFκB signaling (blue); extracellular matrix remodeling (bright blue); chemo-attraction of T- and NK-cells (black); innate immune system (red); ROS production (pink).
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Figure 5. The gene functional clusters identified for the significant, downregulated liver tissue transcriptome. The downregulated liver tissue network contained 7 functional clusters: redox reactions (blue); cytochrome P450 (red); cell-cell adhesion (black); amino acids metabolism (light blue); metabolism (pink and blue); leukocytes functioning (green); immune reactions (bright blue).
Gene Ontology analysis
Additionally we performed GO ontology analysis. In adipose tissue we identified more
upregulated GO terms compared to liver tissue (106 vs. 36) and for the down-regulated GO
terms we detected 2 and 19 in adipose tissue and liver tissue respectively. The significantly
upregulated GO terms were divided into broad categories such as “inflammation”,
“development”, “signaling”, “metal ion homeostasis”, ‘secretion’ and “angiogenesis” and
within the downregulated GO categories we distinguished: “extracellular region”, “amino
acid metabolism”, and “polysaccharide binding”. The GO terms identities within the GO
categories are presented in the supplementary Tables 2A-D. Adipose tissue had more
upregulated GO terms belonging to “inflammation”, “development” and “angiogenesis”
compared to liver tissue and had additional terms such as: “signaling”, “metal ion
homeostasis” and “secretion”, (Figure 6).
Within the downregulated GO categories in adipose tissue we detected “extracellular region
while in liver tissue- “amino acid metabolism” and “polysaccharide binding”, (Figure 6).
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0 10 20 30 40 50
inflammation
development
signaling
metal ion homeostasis
secretion
angiogenesis
Number of significantly affected GO terms
GO analysis-upregulation (GO terms)
LT
AT
0 2 4 6 8 10
extracellular region
aa metabolism
inflammation/binding
Number of significantly affected GO terms
GO analysis-downregulation (GO terms)
LT
AT
Figure 6. GO analysis of the significant adipose and liver tissues transcriptomes. The number of significantly enriched upregulated and downregulated GO terms in adipose tissue (AT) and liver tissue (LT) upon LPS treatment. The GO terms were categorized into broader GO categories such as: angiogenesis, secretion, metal ion homeostasis, signaling, development, inflammation, amino acid (aa) metabolism, and extracellular region.
When analyzing individual genes within the GO categories, a similar picture emerged -in
general the larger number of genes belonging to the identified GO categories was altered in
adipose tissue compared to liver tissue Figure 7. The names and Entrez IDs of genes up- and
down- regulated in both tissues for each GO category are given in supplementary Tables 3A–
B.
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0 50 100 150 200
inflammation
development
signaling
metal ion homeostasis
secretion
angiogenesis
Number of significanltly affected genes
GO analysis-upregulation (genes)
LT
AT
0 50 100 150 200
extracellular region
aa metabolism
inflammation/binding
Number of significantly affcted genes
GO analysis-downregulation (genes)
LT
AT
Figure 7. Gene count analysis for the identified GO categories. Number of genes significantly upregulated and
downregulated in adipose tissue (AT) and liver tissue (LT) within GO categories (angiogenesis, secretion, metal
ion homeostasis, signaling, development, inflammation, amino acid (aa) metabolism, and extracellular region).
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The differentially expressed genes and secretome prediction
Subsequent analysis of the significant transcriptome data was performed in order to select
genes predicted to encode for secreted proteins (the predicted secretome). The analysis
revealed that adipose tissue and liver tissue share 66 genes predicted to encode for secreted
proteins (46 were upregulated and 20 were downregulated). In the adipose tissue predicted
secretome we identified additional 333 significantly changed genes encoding for secreted
proteins (138 transcripts were upregulated and 195 -were downregulated) and within the liver
tissue predicted secretome we identified 170 different genes encoding for secreted proteins
(80 were upregulated and 90 were downregulated).
In our studies we were mostly interested in the upregulated genes as they could be the best
candidate biomarkers measurable in human serum. The information about gene expression of
the highest upregulated genes in adipose and liver tissues is summarized in Table 1. The
presented genes were subdivided in three categories: the first category contained genes which
were significantly upregulated in both tissues (p≤0.05, FC≥2) as the best candidate
biomarkers for the inflamed adipose and liver tissues. The second category contained genes
significantly upregulated in adipose tissue (p≤0.05, FC≥2), but not changed in liver tissue, as
the best candidate biomarkers for the inflamed adipose tissue. The third category contained
genes significantly upregulated in liver tissue (p≤0.05, FC≥2) and unchanged in adipose
tissue (p>0.05) as the best source of candidate biomarkers for the inflamed liver tissue. The
entire list of genes encoding for the predicted inflammatory secretomes of adipose and liver
tissues is given in supplementary material in Tables 4A-C.
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Table 1. The most differential predicted secretome of adipose and liver tissues. The highest upregulated genes (p≤0.05) upon LPS treatment in adipose tissue (AT) and liver tissue (LT) are indicated in bold and are underlined and unchanged genes (p>0.05) are in italics. In the last two columns of the table fold change (FC) of gene expression in AT and LT is given.
ACCESSION NAME4 GENE SYMBOL AT FC LT FC
P01584 INTERLEUKIN 1, BETA IL1B 20 100
P10147 CHEMOKINE (C-C MOTIF) LIGAND 3 CCL3 19.8 10.7
Q96DR8 SMALL BREAST EPITHELIAL MUCIN MUCL1 17.4 5.8
P18510 INTERLEUKIN 1 RECEPTOR ANTAGONIST IL1RN 12.5 6.5
P78556 CHEMOKINE (C-C MOTIF) LIGAND 20 CCL20 11.4 7.5
P16619 CHEMOKINE (C-C MOTIF) LIGAND 3-LIKE 1 CCL3L1 10.7 34.54
P42830 CHEMOKINE (C-X-C MOTIF) LIGAND 5 CXCL5 7.8 30.3
P35354 PROSTAGLANDIN-ENDOPEROXIDE SYNTHASE 2 PTGS2 6.8 7.9
P13501 CHEMOKINE (C-C MOTIF) LIGAND 5/RANTES CCL5 6.3 68
P05120 SERPIN PEPTIDASE INHIBITOR, CLADE B (OVALBUMIN), MEMBER 2 SERPINB2 6 8
P01583 INTERLEUKIN 1, ALPHA IL1A 5.8 4.3
O14625 CHEMOKINE (C-X-C MOTIF) LIGAND 11 CXCL11 5.4 20.4
P05231 INTERLEUKIN 6 (INTERFERON, BETA 2) IL6 4.5 17.8
P08254 MATRIX METALLOPEPTIDASE 3 (STROMELYSIN 1, PROGELATINASE) MMP3 4 20.6
P09038 FIBROBLAST GROWTH FACTOR 2 (BASIC) FGF2 3.7 3.5
P39900 MATRIX METALLOPEPTIDASE 12 (MACROPHAGE ELASTASE) MMP12 3.7 5.8
P09341 CHEMOKINE (C-X-C MOTIF) LIGAND 1 CXCL1 3 23.5
P02778 CHEMOKINE (C-X-C MOTIF) LIGAND 10 CXCL10 2.9 17
P80162 CHEMOKINE (C-X-C MOTIF) LIGAND 6 (GRANULOCYTE CHEMOTACTIC PROTEIN 2) CXCL6 2.8 22
P10144 GRANZYME B (GRANZYME 2, CYTOTOXIC T-LYMPHOCYTE-ASSOCIATED SERINE ESTERASE 1) GZMB 2.8 6.1
O60462 NEUROPILIN 2 NRP2 2.6 2.6
P10145 INTERLEUKIN 8 IL8 2.5 6
P28845 HYDROXYSTEROID (11-BETA) DEHYDROGENASE 1 HSD11B1 2.4 2.5
P13500 CHEMOKINE (C-C MOTIF) LIGAND 2 CCL2 2.3 3.7
P16581 SELECTIN E (ENDOTHELIAL ADHESION MOLECULE 1) SELE 105.1 -2.5
P04141 COLONY STIMULATING FACTOR 2 (GRANULOCYTE-MACROPHAGE) CSF2 82.5 1
Q92629 SARCOGLYCAN, DELTA (35KDA DYSTROPHIN-ASSOCIATED GLYCOPROTEIN) SGCD 58.8 -2.5
Q9BYE3 LATE CORNIFIED ENVELOPE 3D LCE3D 46 -1.4
P02763 OROSOMUCOID 1 ORM1 26.6 1
O14944 EPIREGULIN EREG 25 -1.2
P22894 MATRIX METALLOPEPTIDASE 8 (NEUTROPHIL COLLAGENASE) MMP8 22.2 1.3
Q00604 NORRIE DISEASE (PSEUDOGLIOMA) NDP 19.5 -1.1
P07357 COMPLEMENT COMPONENT 8, ALPHA POLYPEPTIDE C8A 18.6 1
P09919 COLONY STIMULATING FACTOR 3 (GRANULOCYTE) CSF3 16.3 -1.1
P78423 FRACTALCINE CX3CL1 6.4 1.6
P01375 TUMOR NECROSIS FACTOR (TNF SUPERFAMILY, MEMBER 2) TNF 6 1.7
Q9UHD0 INTERLEUKIN 19 IL19 5.6 1.2
O14896 INTERFERON REGULATORY FACTOR 6 IRF6 5.3 -1.4
P26022 PENTRAXIN-RELATED GENE, RAPIDLY INDUCED BY IL-1 BETA PTX3 4 1.5
P03956 MATRIX METALLOPEPTIDASE 1 (INTERSTITIAL COLLAGENASE) MMP1 3.4 -1.1
P03950 ANGIOGENIN, RIBONUCLEASE, RNASE A FAMILY, 5 ANG 3.2 -2
Q9BY76 ANGIOPOIETIN-LIKE 4 ANGPTL4 3.2 1
P19875 CHEMOKINE (C-X-C MOTIF) LIGAND 2 CXCL2 3.1 1.6
P05121 SERPIN PEPTIDASE INHIBITOR/ PLASMINOGEN ACTIVATOR INHIBITOR TYPE 1) MEMBER 1 SERPINE1/pai1 3 1.6
P10124 PROTEOGLYCAN 1, SECRETORY GRANULE SRGN 2.7 1
Q96RQ9 INTERLEUKIN 4 INDUCED 1 IL4I1 2.4 1
Q9Y5U4 INSULIN INDUCED GENE 2 INSIG2 2.3 -1.2
P12643 BONE MORPHOGENETIC PROTEIN 2 BMP2 2.2 1.5
P02735 SERUM AMYLOID A1 SAA1 2.2 1.1
Q07325 CHEMOKINE (C-X-C MOTIF) LIGAND 9 CXCL9 1.8 69.3
P19876 CHEMOKINE (C-X-C MOTIF) LIGAND 3 CXCL3 3.6 20.6
O95633 FOLLISTATIN-LIKE 3 (SECRETED GLYCOPROTEIN) FSTL3 1 15.1
Q13113 PDZK1 INTERACTING PROTEIN 1 PDZK1IP1 1.9 12.9
Q9NRD8 DUAL OXIDASE 2 DUOX2 -2 5.5
Q8WWX9 SELENOPROTEIN M SELM 1.6 3.9
O94808 GLUTAMINE-FRUCTOSE-6-PHOSPHATE TRANSAMINASE 2 GFPT2 1.7 3.6
P13164 INTERFERON INDUCED TRANSMEMBRANE PROTEIN 1 (9-27) IFITM1 -1.1 3.5
P09603 COLONY STIMULATING FACTOR 1 (MACROPHAGE) CSF1 -1.6 3.3
P12544 GRANZYME A (GRANZYME 1, CYTOTOXIC T-LYMPHOCYTE-ASSOCIATED SERINE ESTERASE 3) GZMA -1.4 3.3
P25774 CATHEPSIN S CTSS 1.4 3.2
P24001 INTERLEUKIN 32 IL32 1.6 3
P31431 SYNDECAN 4 (AMPHIGLYCAN, RYUDOCAN) SDC4 1.9 2.7
P03973 SECRETORY LEUKOCYTE PEPTIDASE INHIBITOR SLPI -1.4 2.7
P09237 MATRIX METALLOPEPTIDASE 7 (MATRILYSIN, UTERINE) MMP7 -1.6 2.6
Q5VY09 IMMEDIATE EARLY RESPONSE 5 IER5 1.6 2.5
O75976 CARBOXYPEPTIDASE D CPD 1.3 2.2
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Transcriptomics and proteomics data comparison and candidate biomarkers
identification
In order to validate biomarkers related to inflamed adipose tissue, we performed a similar
experiment using a quantitative proteomics approach (CILAIR), and analyzed the secreted
proteins in the adipose tissue culture media (secretome). In the CILAIR experiment we
identified 192 proteins with incorporated label in medium of LPS treated tissue and 209 in
medium of untreated adipose tissue. 178 proteins had incorporated label in both conditions
and could thus be compared quantitatively. The statistical analysis revealed that 23 proteins
were significantly changed in abundance in the secretome by LPS treatment. Comparison
with the gene expression data for adipose tissue showed excellent correlation between
proteomics and transcriptomics data (Pearson’s correlation r2 = 0.78; Table 2). Within the 23
significantly affected proteins we selected those which were significantly affected by LPS in
adipose tissue, on both gene and protein level, but not changed in the liver tissue
transcriptome, and those proteins were considered as the best candidate biomarkers for
inflamed adipose tissue. We propose: PTX3, MMP1, SERPINE1, and CX3CL1 as the top
candidate biomarkers related to the inflamed adipose tissue. The results are summarized in
Table 2.
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Table 2. Significantly changed secreted proteins in adipose tissue culture media and the corresponding identified genes in adipose tissue and liver tissue upon LPS-treatment. The significantly changed proteins (p≤0.05, FC>1.2) and the significantly changed genes (p≤0.05, FC>2) are represented in bold. The insignificantly affected genes are depicted in italics. The top candidate biomarkers related to inflamed adipose tissue are depicted as underlined. FC stands for fold change.
NAME SYMBOL ADIPOSE TISSUE
FC-protein
ADIPOSE TISSUE FC-
transcriptome
LIVER TISSUE-FC
transcriptome
Granulocyte colony-stimulating factor CSF3 10.3 16 14
Leukemia inhibitory factor LIF 2.3 7.1 2.4
Fractalkine CX3CL1 4.3 6.4 1.6
Tumor necrosis factor TNF 3.8 6 1.7
Plasminogen activator inhibitor 2 SERPINB2 3.1 6 8
Interleukin-6 IL6 1.6 4.5 17.8
Pentraxin-related protein PTX3 1.9 4 1.6
Interstitial collagenase MMP1 1.7 3.4 -1.1
Tumor necrosis factor-inducible gene 6 protein TNFAIP6 5.4 3.1 17.9
Plasminogen activator inhibitor 1 SERPINE1 1.7 3 1.6
C-C motif chemokine 2 CCL2 6.9 2.3 3.7
CD44 antigen CD44 2.4 1.7 5.8
Insulin-like growth factor-binding protein 4 IGFBP4 -2.5 -1.1 1.2
Adipocyte enhancer-binding protein 1 AEBP1 -1.4 -1.2 1.2
Cystatin-C CST3 -3.1 -1.2 -1.1
Versican core protein VCAN -2 -1.6 -1.4
Collagen alpha-1(VI) chain COL6A1 -3.3 -1.6 -1.4
Transforming growth factor-beta-induced protein ig-h3 TGFBI -2.5 -1.6 1
Legumain LGMN -3.1 -2 1.2
Gelsolin GSN -2.5 -2 1.1
Cathepsin B CTSB -1.2 -2 -1.2
Lysozyme C LYZ -3.3 -2.5 -3.3
Alpha-2-macroglobulin A2M -3.3 -3.3 -1.6
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Discussion
In the present study we evoked LPS induced inflammation in adipose and liver tissues in
vitro in order to mimic IR caused by inflammation in vivo. We aimed to compare the changes
in the inflamed transcriptomes and secretomes of both tissues in order to (1) better understand
contribution of the inflamed adipose and liver tissues to the development of insulin resistance
and (2) to identify candidate biomarkers indicative for tissue specific inflammation/IR.
The gene functional classification analysis revealed that both adipose and liver tissue share
common response mechanisms that are activated during inflammation (chemokine signaling,
(anti)apoptosis, extracellular matrix remodeling, adhesion and migration of different immune
cells involved in inflammatory reactions). Although functional clustering led to identification
of the same functional groups, both tissues had a different set of genes within one functional
group, suggesting tissue-specific inflammatory signaling. The significantly upregulated
adipose tissue transcriptome contained additional gene functional categories belonging to
SOCS and several transporters (supplementary Table 1A). The SOCS signaling was shown
previously to be involved in induction of insulin resistance during acute inflammation in
human adipose tissue [24] and our ex vivo data are in line with these in vivo findings. The
analysis of the down regulated functional groups pointed out towards redox/detoxification
processes affected in both tissues and mitochondrial functions observed in liver tissue. These
processes could contribute to the enhanced reactive oxygen species (ROS) production
recognized as one of the mechanisms implicated in the development of IR/T2D [13].
Furthermore, adipose tissue had downregulated genes involved in the extracellular matrix
activity which is involved in multiple processes including modulation of immune response. In
liver tissue downregulation of genes involved in amino acid metabolism and polysaccharide
binding were observed. There are reports about changed amino acids concentrations in
animal models of obesity and obese humans [25,26], however interpretation of this ex vivo
finding in relation to these reports is not unequivocal.
The additional network identification for the common (overlapping) and differential
adipose and liver tissue transcriptomes was in line with the data obtained from the gene
functional analysis and distinguished the common and differential networks. Several of these
networks were described previously in the literature for their role in induction of IR thereby
supporting our model system to study the inflammation related insulin resistance in vivo. For
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example chemokine signaling and matrix remodeling were found for both tissues [27,28];
SOCS and PPARγ were changed in adipose tissue [29,30]; upregulated Jak-STAT signaling
and NFκB were identified previously in IR liver [31,32].
The GO analysis and gene count revealed that adipose tissue had more LPS-induced
upregulated GO terms and genes related primarily to “inflammation”, “angiogenesis”, and
“development”. Moreover, the predicted secretome studies showed that the adipose tissue
predicted inflammatory secretome is more abundant compared to the liver tissue secretome.
This observation indicates that adipose tissue is more active during inflammation, compared
to liver tissue, and supports the hypothesis that adipose tissue plays the major role in the
development of inflammation-related IR [2].
The predicted secretome analysis. The microarray data analysis of both tissues revealed
that adipose and liver tissues have numerous overlapping LPS-responsive genes which
protein products are predicted to be secreted. Among these genes we identified several known
markers associated with insulin resistance such as IL-6, IL-1β, IL-8, and PAI 1. Other
proteins known to be upregulated during insulin resistance by adipose tissue [33] such as
RANTES , MCP1, PLAUR, CXCL5, were found in our studies to be upregulated in both
adipose- and liver tissues. Additionally in both tissues we found genes, previously shown to
be regulated in adipose tissue in relation to insulin resistance: CXCL1, CXCL10, CXCL11,
ICAM1, TNFAIP6 [34], FGF2, IL6 [35], and ICAM1, IL-1 [36]. Although TNFα is known to
be involved in the development of insulin resistance in both adipose tissue and the liver, it
was only significantly upregulated in adipose tissue. However, we observed that 3 out of 5
livers had upregulated expression of TNFα and previously we showed that in liver tissue in
vitro, TNFα mRNA level was significantly upregulated after 5 hrs while after 24hrs the
TNFα mRNA level returned to basal values. [15,37].
Furthermore, the comparative analysis of adipose and liver tissues secretomes in vitro
provides a source of candidate biomarkers related to tissue specific inflammation/insulin
resistance. Similarly to Shah et al. [34], we identified in the inflamed adipose tissue
secretome genes such as: SELE, CD274, ORM1, PLA1A, SLAMF1, CX3CL1, OSM, TNF,
C19ORF59, PTX3, IER3, CCL8, CXCL2, SERPINE1, BMP2, FAM107A, GPX3. Moreover
we identified genes of yet unknown functions such as: C14ORF162, C20ORF59 or genes
implicated in other than insulin resistance inflammatory diseases: epiregulin, IL-19 or
sarcoglycan [38-40].
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The analysis of the predicted secretome of inflamed liver tissue revealed several significantly
changed genes with a known- and an unknown- relationship to insulin resistance.
Identification of biomarkers indicative for inflamed liver tissue could be a useful tool in a
diagnosis of NAFLD patients, where the only “golden standard” is an invasive liver biopsy
[41]. Biomarkers previously associated with liver diseases and identified in our samples were
among others: ANGPTL3, IGFBP2, SDC4, IL1RN [7,42]. Examples of other pro-
inflammatory proteins affiliated with inflammation but not liver insulin resistance were
cathepsin S [33] or granzyme A [43]. In future it has to be validated if the other most
differentially regulated genes between both tissues such as: SGCD, LCE3D, EREG, NDP and
CXCL9, FSTL3, PDZK1IP1 could be used as biomarkers related to insulin resistance of
adipose or liver tissues respectively.
Comparison of transcriptomics and proteomics data. Finally the transcriptome data
encoding for the adipose tissue inflammatory secretome was validated and compared with the
protein data of the inflamed adipose tissue culture medium. The analysis showed that the
transcriptome data were in line with the proteomics data, in respect to observed upwards and
downwards fold changes (FC) for genes and their corresponding protein products. However
the FC derived from the proteomics experiment cannot be directly compared with the FC of
the transcriptome experiment due to substantial technical differences between both
technologies. By combination of the comparative transcriptome analysis and proteomics
technology we identified matrix metalopeptidase-1(MMP-1), pentraxin related gene product
(PTX3), fractalkine (CX3CL1), and PAI 1 as the potential set of biomarkers for the inflamed
adipose tissue. We believe that such an approach could result in more specific diagnosis for a
tissue specific insulin resistance related to inflammation, than the use of single biomarkers.
One of the shortcomings of our study was the use of two different DNA microarray
platforms, since the data used here were generated in two different laboratories. However,
previous studies comparing human Affymetrix and Illumina platforms show that the obtained
results, using the same human material, are highly comparable, especially for genes which are
predicted to be differentially expressed [44]. Furthermore in our studies we compared only
genes which were significantly affected and present on both platforms; therefore genes which
were not present on both platforms were excluded from the analysis and we did not compare
intensities of corresponding genes since they would be different due to the platform specific
design.
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Conclusions
In summary, our in vitro approach showed that LPS-induced inflammation in adipose and
liver tissues, results in upregulation of inflammatory processes and downregulation of
metabolic pathways and redox/detoxification reactions, which could synergistically
contribute to the deregulation of energy homeostasis leading to insulin resistance.
Furthermore, our study implies that adipose tissue is more active during inflammation
compared to the liver, based on identification of higher number of GO terms and genes
involved in inflammation and angiogenesis, and a number of genes predicted to encode for
secreted proteins. It has to be validated in the future if the identified tissue specific molecular
pathways and the identified tissue specific candidate biomarkers can be used for tissue
specific diagnosis of insulin resistance in patients. We believe that such an approach might
facilitate more targeted treatment of insulin resistant patients.
Acknowledgements
We would like to thank Susanne Bauerschmidt and Jan Polman (Merck, Oss, The
Netherlands) for the performance and elaboration of the Affymetrix data, Prof. Rainer
Breitling for critical discussions and Heleen de Weerd for bioinformatical assistance.
References
1. Rasouli N, Kern PA: Adipocytokines and the metabolic complications of obesity. J Clin Endocrinol Metab 2008, 93:S64-S73.
2. Hotamisligil GS: Inflammation and metabolic disorders. Nature 2006, 444:860-867. 3. Shoelson SE, Lee J, Goldfine AB: Inflammation and insulin resistance. J Clin Invest 2006,
116:1793-1801. 4. Wellen KE, Hotamisligil GS: Inflammation, stress, and diabetes. J Clin Invest 2005, 115:1111-1119. 5. Breitling R: Robust signaling networks of the adipose secretome. Trends Endocrinol Metab 2009,
20:1-7. 6. Karalis KP, Giannogonas P, Kodela E, Koutmani Y, Zoumakis M, Teli T: Mechanisms of obesity and
related pathology: linking immune responses to metabolic stress. FEBS J 2009, 276:5747-5754. 7. Younossi ZM, Gorreta F, Ong JP, Schlauch K, Del GL, Elariny H, Van MA, Younoszai A, Goodman
Z, Baranova A et al.: Hepatic gene expression in patients with obesity-related non-alcoholic steatohepatitis. Liver Int 2005, 25:760-771.
8. Edens MA, Kuipers F, Stolk RP: Non-alcoholic fatty liver disease is associated with cardiovascular disease risk markers. Obes Rev 2009, 10:412-419.
9. Virkamaki A, Yki-Jarvinen H: Mechanisms of insulin resistance during acute endotoxemia. Endocrinology 1994, 134:2072-2078.
Chapter 6
169
10. Virkamaki A, Puhakainen I, Koivisto VA, Vuorinen-Markkola H, Yki-Jarvinen H: Mechanisms of hepatic and peripheral insulin resistance during acute infections in humans. J Clin Endocrinol Metab 1992, 74:673-679.
11. Sugita H, Kaneki M, Tokunaga E, Sugita M, Koike C, Yasuhara S, Tompkins RG, Martyn JA: Inducible nitric oxide synthase plays a role in LPS-induced hyperglycemia and insulin resistance. Am J Physiol Endocrinol Metab 2002, 282:E386-E394.
12. Agwunobi AO, Reid C, Maycock P, Little RA, Carlson GL: Insulin resistance and substrate utilization in human endotoxemia. J Clin Endocrinol Metab 2000, 85:3770-3778.
13. Bashan N, Kovsan J, Kachko I, Ovadia H, Rudich A: Positive and negative regulation of insulin signaling by reactive oxygen and nitrogen species. Physiol Rev 2009, 89:27-71.
14. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, Neyrinck AM, Fava F, Tuohy KM, Chabo C et al.: Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 2007, 56:1761-1772.
15. Elferink MG, Olinga P, Draaisma AL, Merema MT, Faber KN, Slooff MJ, Meijer DK, Groothuis GM: LPS-induced downregulation of MRP2 and BSEP in human liver is due to a posttranscriptional process. Am J Physiol Gastrointest Liver Physiol 2004, 287:G1008-G1016.
16. Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM, Burcelin R: Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice. Diabetes 2008, 57:1470-1481.
17. Alvarez-Llamas G, Szalowska E, de Vries MP, Weening D, Landman K, Hoek A, Wolffenbuttel BH, Roelofsen H, Vonk RJ: Characterization of the human visceral adipose tissue secretome. Mol Cell Proteomics 2007, 6:589-600.
18. Dennis G, Jr., Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003, 4:3.
19. Huang dW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009, 4:44-57.
20. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M et al.: STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009, 37:D412-D416.
21. Bendtsen JD, Jensen LJ, Blom N, Von HG, Brunak S: Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 2004, 17:349-356.
22. Roelofsen H, Dijkstra M, Weening D, de Vries MP, Hoek A, Vonk RJ: Comparison of isotope-labeled amino acid incorporation rates (CILAIR) provides a quantitative method to study tissue secretomes. Mol Cell Proteomics 2009, 8:316-324.
23. Swertz MA, Jansen RC: Beyond standardization: dynamic software infrastructures for systems biology. Nat Rev Genet 2007, 8:235-243.
24. Mehta NN, McGillicuddy FC, Anderson PD, Hinkle CC, Shah R, Pruscino L, Tabita-Martinez J, Sellers KF, Rickels MR, Reilly MP: Experimental Endotoxemia Induces Adipose Inflammation and Insulin Resistance in Humans. Diabetes 2009.
25. Wijekoon EP, Skinner C, Brosnan ME, Brosnan JT: Amino acid metabolism in the Zucker diabetic fatty rat: effects of insulin resistance and of type 2 diabetes. Can J Physiol Pharmacol 2004, 82:506-514.
26. She P, Van HC, Reid T, Hutson SM, Cooney RN, Lynch CJ: Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab 2007, 293:E1552-E1563.
27. Feuerer M, Herrero L, Cipolletta D, Naaz A, Wong J, Nayer A, Lee J, Goldfine AB, Benoist C, Shoelson S et al.: Lean, but not obese, fat is enriched for a unique population of regulatory T cells that affect metabolic parameters. Nat Med 2009, 15:930-939.
28. Nishimura S, Manabe I, Nagasaki M, Eto K, Yamashita H, Ohsugi M, Otsu M, Hara K, Ueki K, Sugiura S et al.: CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity. Nat Med 2009, 15:914-920.
Chapter 6
170
29. Baranova A, Collantes R, Gowder SJ, Elariny H, Schlauch K, Younoszai A, King S, Randhawa M, Pusulury S, Alsheddi T et al.: Obesity-related differential gene expression in the visceral adipose tissue. Obes Surg 2005, 15:758-765.
30. Gomez-Ambrosi J, Catalan V, ez-Caballero A, Martinez-Cruz LA, Gil MJ, Garcia-Foncillas J, Cienfuegos JA, Salvador J, Mato JM, Fruhbeck G: Gene expression profile of omental adipose tissue in human obesity. FASEB J 2004, 18:215-217.
31. Larsen L, Ropke C: Suppressors of cytokine signalling: SOCS. APMIS 2002, 110:833-844. 32. Lebrun P, Van OE: SOCS proteins causing trouble in insulin action. Acta Physiol (Oxf) 2008,
192:29-36. 33. Clement K, Langin D: Regulation of inflammation-related genes in human adipose tissue. J Intern
Med 2007, 262:422-430. 34. Shah R, Lu Y, Hinkle CC, McGillicuddy FC, Kim R, Hannenhalli S, Cappola TP, Heffron S, Wang X,
Mehta NN et al.: Gene profiling of human adipose tissue during evoked inflammation in vivo. Diabetes 2009, 58:2211-2219.
35. Gomez-Ambrosi J, Catalan V, ez-Caballero A, Martinez-Cruz LA, Gil MJ, Garcia-Foncillas J, Cienfuegos JA, Salvador J, Mato JM, Fruhbeck G: Gene expression profile of omental adipose tissue in human obesity. FASEB J 2004, 18:215-217.
36. Nair S, Lee YH, Rousseau E, Cam M, Tataranni PA, Baier LJ, Bogardus C, Permana PA: Increased expression of inflammation-related genes in cultured preadipocytes/stromal vascular cells from obese compared with non-obese Pima Indians. Diabetologia 2005, 48:1784-1788.
37. Szalowska E, Elferink MG, Hoek A, Groothuis GM, Vonk RJ: Resistin is more abundant in liver than adipose tissue and is not upregulated by lipopolysaccharide. J Clin Endocrinol Metab 2009.
38. McIntyre E, Blackburn E, Brown PJ, Johnson CG, Gullick WJ: The complete family of epidermal growth factor receptors and their ligands are co-ordinately expressed in breast cancer. Breast Cancer Res Treat 2009.
39. Commins S, Steinke JW, Borish L: The extended IL-10 superfamily: IL-10, IL-19, IL-20, IL-22, IL-24, IL-26, IL-28, and IL-29. J Allergy Clin Immunol 2008, 121:1108-1111.
40. Sandona D, Betto R: Sarcoglycanopathies: molecular pathogenesis and therapeutic prospects. Expert Rev Mol Med 2009, 11:e28.
41. Byrne CD, Olufadi R, Bruce KD, Cagampang FR, Ahmed MH: Metabolic disturbances in non-alcoholic fatty liver disease. Clin Sci (Lond) 2009, 116:539-564.
42. Yilmaz Y, Ulukaya E, Atug O, Dolar E: Serum concentrations of human angiopoietin-like protein 3 in patients with nonalcoholic fatty liver disease: association with insulin resistance. Eur J Gastroenterol Hepatol 2009.
43. Pardo J, Aguilo JI, Anel A, Martin P, Joeckel L, Borner C, Wallich R, Mullbacher A, Froelich CJ, Simon MM: The biology of cytotoxic cell granule exocytosis pathway: granzymes have evolved to induce cell death and inflammation. Microbes Infect 2009, 11:452-459.
44. Barnes M, Freudenberg J, Thompson S, Aronow B, Pavlidis P: Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms. Nucleic Acids Res 2005, 33:5914-5923.
171
Abbreviations
AdipoR1 adiponectin receptor 1 AMPK AMP-activated protein kinase ANOVA analysis of variance Apo apolipoprotein BMI body mass index CILAIR Comparison of Isotope Labeled Amino Acid Incorporation Rates CRP C-reactive protein CSF1 colony stimulating factor 1 CVD cardiovascular disease CX3CL1 chemokine (C-X3-C motif) ligand 1 CXCL10 C-X-C motif chemokine 10 CoA coenzyme A DAVID Database for Annotation, Visualization, and Integrated Discovery DIO diet induced obesity EAM energy absorbing molecule FC fold change FDR false discovery rate FFA free fatty acids FSIGT frequently sampled intravenous glucose tolerance testing GIP glucose dependent insulinotropic polypeptide GIPR glucose dependent insulinotropic polypeptide receptor GIPR KO glucose dependent insulinotropic polypeptide receptor knockout GLUT4 solute carrier family 2 (facilitated glucose transporter) member 4 GLUCR glucagon receptor GO gene ontology HDL high-density lipoprotein HOMA homeostasis model assessment IL-1b interleukin 1 b IL-6 interleukin -6 INSR insulin receptor IR insulin resistance Jak-STAT Janus kinase/signal transducers and activators of transcription LC liquid chromatography M-CS macrophage-colony stimulating factor MCP-1 monocyte chemotactic protein-1 MMP-1 matrix metalloproteinase-1 MS mass spectrometry m/z mass to charge ratio NFκB nuclear factor kappa-light-chain-enhancer of activated B cells LDL low-density lpoprotein LPL lipoprotein lipase LPS lipopolysachcaride PAI-I plasminogen activator inhibitor I PEDF pigment epithelium-derived factor PPARγ peroxisome proliferator-activated receptor gamma PTX3 pentraxin-related protein RBP4 retinol binding protein 4
172
ROS reactive oxygen species SAA serum amyloid A SAT subcutaneous adipose tissue SELDI surface-enhanced laser desorption/ionization SILAC stable isotope labeling by/with amino acids in cell culture SOCS suppressors of cytokine signaling SREBP1 sterol regulatory element-binding proteins STRING Search Tool for the Retrieval of Interacting Genes/Proteins T2D type 2 diabetes TG triglycerides TNFα tumor necrosis factor a TOF time of flight TZDs thiazolidinediones WC waist circumference VAT visceral adipose tissue
173
Acknowledgements
Dear Roel, starting from the beginning …as you may not remember anymore... I met you by coincident while having a meeting with someone else; at that time you had a PhD position which I decided to take. It was a pioneering project involving human adipose tissue and proteomics. With hardly any equipment except for a brand new SELDI machine and the only bench at the Pediatrics’ lab we were scrutinizing SELDI limits and searching for biomarkers in human adipose tissue culture media. And looking from this early moment to the present situation, it is actually hard to believe, how things changed and developed further.
I think working in your lab worked out for me very well, because of its pioneering character, such a situation stimulates to explore a lot of possibilities which you would not think of while working in a well established lab with well defined research questions. Dear Roel, I really enjoyed especially the last years of my PhD studies, when we gained a lot of experience, and most of techniques were established. I appreciate that you supported me in my choices and gave me a lot of freedom in doing what I thought was interesting to do. And your guidance helped me to keep focus. Thanks for the Keystone adventure; it was a pleasure to be there and I will never forget these beautiful mountains (and lectures of course) and the great time we spent there!
Dear Han and Annemieke, you both were involved in the adipose tissue project from the beginning. Han, I appreciate your efforts into the gradual development of proteomics in our lab, ending up with the CILAIR. Thanks a lot for your contribution and discussions within last years. And of course, I can not forget, you are a great skier and thanks for your tips which upgraded my skills and my first time on the black piste.
Dear Annemieke, I admire your enthusiasm and the passion you have for your work as a scientist and as medical doctor. Without your involvement we could not investigate the human adipose tissue and could not make this book.
Dear Marianne, you were also present from the beginning of “our” lab and for some while you were the only colleague struggling with the SELDI. Thank you for all the little things you helped me with, for the small talks and for being my paranymph.
Later we were joined shortly or permanently by other colleagues who worked also on the adipose tissue projects: Desiree (you developed into a multitasking technician impossible to substitute), Aldona, Suzette, Mariska, Jenneke, Karl-Loes, and Gloria-thank you all for your contributions and efforts.
Of course I can not omit the Pediatrics colleagues: Prof. Folkert Kuipers, Aldo, Marijke, Torsten, Fjodor, Dirk-Jan, Janine, Thierry, Meike, Jelske, Frans, Dirk-Jan, Renzee, Juul, Klary, Henk, Hilde, and co-members. I really enjoyed being part of your lab for a while and I learnt a lot from you guys!
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After we moved to the new location several members enriched our team whose joint work contributed to this thesis. Marcel B. we all appreciate your knowledge and professionalism in the way how you deal with the mass spectrophotometers. Dear Fahrad, since you joined our lab you brought a lot of energy and enthusiasm. Within a short period of time it looked like you knew half of the UMCG employees. I wish you can realize your scientific dream once! Dear Marion, you are a very kind person. I value your scientific opinion and passion you have for your work. I wish you success in your further career. Martijn, my great appreciation for your both bioinformatical and non-bioinformatical, very often ad hoc, input. Not to be forgotten are my (inter)national room mates. Dear Saed, we had a kind of a tough start, but I am really happy that after all things worked out for us both quite well-a lot of success in your “second” life…, well things can only get better ... Kees it was a pleasure that you could join the GIP project and we could work for a while together. I appreciate your practical and intellectual contributions & thank you for being my paranymph. Dear Nicolai, we did not really were roommates but it felt like this. Your arrival to the Medical Biomics lab filled the gap for a social evening’s organizer. Ich wunsche dir, dein Buch rechtzeitig zu liefern, obwohl der Weg darnach kein Honiglecken ist. Moreover thanks for nice time to roommates and other colleagues -Jouke, Andrea, Heleen, Ma-ye, Marcel, Coby, Tao, Hong -waai, Sulima, Diederick.
My very special thanks go to Dr. Marieke Elferink and Prof. Geny Groothuis from the department of Pharmacokinetics, Toxicology, and Targeting. Dear Marieke and dear Geny, from my perspective I think it is very special how our collaboration developed-the primary aim we met for did not really work out, but the others resulted in publication(s). I really enjoyed working with you both, and I am grateful for your involvement, warmness, and stimulating discussions. I hope our collaboration will be successfully continued.
My very first paper would not see light if I would not have met Gerard te Meerman during one of the courses for PhD students. After that we had contact more frequently and you were always willing to explain to me diverse statistical issues. Thank you Gerard for your time, enthusiasm, and patience.
From the Department of Genetics I would like to thank Prof. Cisca Wijmenga for setting up the Illumina facility which we could make use of. Of course, the help of Marcel Bruinenberg with practical Illumina issues is highly appreciated. I see myself lucky I could meet Martin Wapenaar, a great teacher, you Martin explained me a lot of topics related to DNA microarrays and help me to plan my very first arrays experiments. Thanks a lot for your time, openness and stimulating discussions!
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Our floor colleagues are also on the thank you list. Prof. Marten Hofker I would like to express my appreciation for your willingness to cooperate. Marcel thanks for your cooperating attitude as well, it is a pity there was not enough time to explore all the scientific possibilities. Dear Jana, yes still we have to finish the TUB project and it was nice to collaborate with you and to get to know you. Niels I am very happy that after all difficulties we were able to finish the animal experiment and publish the results. It was fun to do this animal experiment with you. Thanks for your enthusiasm and help, you are a great improviser and I really learnt from you a lot. I also would like to thank Henk van der Molen who was guiding us during beginning of animal experiments.
My words of appreciation go also to Prof. Ingrid Molema. We met shortly, but I learnt from you a bit about angiogenesis and of course in your lab I could practice stainings of adipose tissue. Henk thanks a lot for making the adipose tissue sections for me and your help in other experiments.
Dear Jolien, you made few times my life easier. While living in Arnhem and still working in Groningen you spent (too much) time getting all these signatures on the copyright forms… Thanks also for your help and time we spent together in our department!
Dearest Sacha, Filip, and Nadia ………………………buziaki, you guys make my days!
So this is briefly how we managed to get to the
HHHHAAAAPPPPPllll-EEEEND!
Ewa
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Curriculum Vitae
Ewa Szalowska was born on 14th of May 1975, in Wroclaw, Poland. She studied
Biotechnology/Biochemistry at the University of Wroclaw, Poland. As a student she visited
Laboratory of Microbial Physiology led by Prof. Lubbert Dijkhuizen where she performed
one of her Master Projects. After graduating in Poland she came back to the Netherlands and
was appointed as a PhD student in the Department of Developmental Genetics in Haren.
After 2 years she resigned this job and started a new PhD position under the supervision of
Prof. Roel Vonk in the Department of Pediatrics. The appointment was continued under the
same supervision in the Department of Medical Biomics in the UMCG/University of
Groningen.
Since 2010 she is appointed as a postdoc at RIKILT/University of Wageningen within the
Netherlands Toxicogenomics Center project. She is working on pharmacologically induced
liver pathologies (cholestasis, steatosis, and necrosis) in order to study mechanisms behind
these processes and to identify biomarkers of early liver injury. Moreover, she will continue
to study adipose tissue physiology and its interactions with the liver in relation to
environmental pollutants and type 2 diabetes.
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