The proteomic signature of insulin-resistant human skeletal muscle reveals increased glycolytic and decreased mitochondrial enzymes

ARTICLE
The proteomic signature of insulin-resistant human skeletal
muscle reveals increased glycolytic and decreased
mitochondrial enzymes
J. Giebelstein & G. Poschmann & K. Højlund &
W. Schechinger & J. W. Dietrich & K. Levin &
H. Beck-Nielsen & K. Podwojski & K. Stühler &
H. E. Meyer & H. H. Klein
# Springer-Verlag 2012
Abstract
Aims/hypothesis The molecular mechanisms underlying in-
sulin resistance in skeletal muscle are incompletely under-
stood. Here, we aimed to obtain a global picture of changes
in protein abundance in skeletal muscle in obesity and type
2 diabetes, and those associated with whole-body measures
of insulin action.
Methods Skeletal muscle biopsies were obtained from ten
healthy lean (LE), 11 obese non-diabetic (OB), and ten obese
type 2 diabetic participants before and after hyperinsulinae-
mic–euglycaemic clamps. Quantitative proteome analysis was
performed by two-dimensional differential-gel electrophoresis
and tandem-mass-spectrometry-based protein identification.
Results Forty-four protein spots displayed significant (p<
0.05) changes in abundance by at least a factor of 1.5
between groups. Several proteins were identified in multiple
spots, suggesting post-translational modifications. Multiple
spots containing glycolytic and fast-muscle proteins showed
increased abundance, whereas spots with mitochondrial and
slow-muscle proteins were downregulated in the OB and
obese type 2 diabetic groups compared with the LE group.
No differences in basal levels of myosin heavy chains were
observed. The abundance of multiple spots representing
glycolytic and fast-muscle proteins correlated negatively
with insulin action on glucose disposal, glucose oxidation
and lipid oxidation, while several spots with proteins
J. Giebelstein, G. Poschmann and K. Højlund contributed equally to
this work.
Electronic supplementary material The online version of this article
(doi:10.1007/s00125-012-2456-x) contains peer-reviewed but unedited
supplementary material, which is available to authorised users.
J. Giebelstein : W. Schechinger : J. W. Dietrich : H. H. Klein (*)
Medizinische Klinik I, Berufsgenossenschaftliches
Universitätsklinikum Bergmannsheil,
Klinikum der Ruhr Universität Bochum,
Bürkle-de-la-Camp-Platz 1,
44789 Bochum, Germany
e-mail: harald.klein@rub.de
G. Poschmann : K. Podwojski : K. Stühler : H. E. Meyer
Medizinisches Proteom-Center, Ruhr Universität Bochum,
Bochum, Germany
K. Højlund : K. Levin : H. Beck-Nielsen
Diabetes Research Center, Department of Endocrinology,
Odense University Hospital,
Odense, Denmark
W. Schechinger
Bioavid Diagnostics,
Dieburg, Germany
K. Podwojski
Department of Statistical Methods in Genetics and Chemometrics,
Technical University Dortmund,
Dortmund, Germany
K. Stühler
Molecular Proteomics Laboratory,
Heinrich Heine Universität Düsseldorf,
Düsseldorf, Germany
Diabetologia (2012) 55:1114–1127
DOI 10.1007/s00125-012-245 -6 x
Received: 8 October 2011 /Accepted: 19 December 2011 /Published online: 27 January 2012

involved in oxidative metabolism and mitochondrial
function correlated positively with these whole-body
measures of insulin action.
Conclusions/interpretation Our data suggest that increased
glycolytic and decreased mitochondrial protein abundance
together with a shift in muscle properties towards a fast-
twitch pattern in the absence of marked changes in fibre-
type distribution contribute to insulin resistance in obesity
with and without type 2 diabetes. The roles of several
differentially expressed or post-translationally modified
proteins remain to be elucidated.
Keywords Glycolysis . Insulin resistance .Mitochondria .
Obesity . Proteome analysis . Skeletal muscle . Type 2
diabetes mellitus
Abbreviations
2D-DIGE Two-dimensional differential gel
electrophoresis
ACO2 Aconitate hydratase
ACTA1 Actin, α skeletal muscle
ATP5A1 ATP synthase subunit-α
ATP5B ATP synthase subunit-β
ENO3 β-Enolase
ESI Electrospray ionisation
GAPDH Glyceraldehyde-3-phosphate dehydrogenase
GBAS NipSnap-homologue-2
GDR Glucose disposal rates
HES1 ES1 protein homologue
LE Healthy lean
MYH Myosin heavy chain
MYL Myosin light chain
MYLPF Myosin regulatory light chain 2, skeletal muscle
NOGM Non-oxidative glucose metabolism
OB Obese non-diabetic
PGAM2 Phosphoglycerate mutase-2
PKM2 Pyruvate kinase
PTM Post-translational modification
PYGM Glycogen phosphorylase, muscle form
TCA Tricarboxylic acid
TNNT3 Fast skeletal muscle troponin-T
Introduction
Insulin resistance of skeletal muscle is a major metabolic
feature in obesity and a key factor in the pathogenesis of
type 2 diabetes [1]. The underlying molecular mechanisms
are complex and still incompletely understood. Insulin sig-
nalling is impaired at several levels [1, 2], but whether these
changes are primary or secondary to the metabolic changes
remains unclear [2]. Inflammation and cytokine signalling
appear to be important, and recent studies have linked
insulin resistance with mitochondrial dysfunction. This
includes reduced mitochondrial content and in some [2–5],
but not all, studies, reduced mitochondrial functional capacity
[2, 4, 6–12].
While hypothesis-driven investigations have elucidated
important details of the complex mechanism leading to
insulin resistance, hypothesis-free global approaches such
as microarray-based transcriptional profiling or quantitative
proteome analysis have the advantage of investigating the
whole complexity. Thus, evidence for patterns of changes
can be provided and, potentially, new hypotheses generated.
The transcriptomic and proteomic approaches are comple-
mentary as changes in mRNA levels do not necessarily
mirror changes in the abundance of the proteins encoded
by these genes, and potentially important post-translational
modifications (PTMs) can only be detected by proteomics.
There have been several previous approaches to the study
of the human skeletal muscle proteome [13, 14] and its
alterations in insulin-resistant muscle [15–18]. Two studies
have used the two-dimensional gel approach [15, 16]. Al-
though these investigators described several differences in
protein abundance between insulin-sensitive and -
insensitive muscle, their studies were somewhat limited by
the proteomic technologies available at that time. Thus,
lower resolution of the spots, lower sensitivity and/or lower
dynamic range of the staining methods and protein identifica-
tion by matrix-assisted laser desorption/ionisation (MALDI)-
time of flight (TOF) MS rather than high-sensitivity HPLC-
electrospray ionisation (ESI)-MS/MS (tandem mass spec-
trometry) made it difficult to detect and identify changes in
protein abundance. Moreover, these earlier attempts involved
study groups that were less well matched or rather small.
Another more recent study investigated muscle from healthy
lean, obese non-diabetic and obese type 2 diabetic individuals
using one-dimensional gel separation with SDS-PAGE fol-
lowed by HPLC-ESI-MS/MS-based identification and quan-
tification [17], and reported significant changes in the
abundance of 15 proteins.
Within the last years, the combination of two-
dimensional differential gel electrophoresis (2D-DIGE)
and fluorescence staining of proteins followed by protein
identification using high-sensitivity MS/MS methods has
proven to be a suitable gel-based method for the quantifica-
tion of changes in abundance of proteins or patterns of
proteins in human tissues and has recently been used to
compare the human skeletal muscle mitochondrial proteome
before and after endurance exercise training [14]. Here, we
applied this technology to investigate alterations of the
skeletal muscle proteome in obesity and type-2-diabetes-
associated insulin resistance, respectively. Muscle biopsies
obtained from healthy lean (LE), obese non-diabetic (OB),
and matched obese type 2-diabetic individuals before and
after hyperinsulinaemic–euglycaemic clamps were
Diabetologia (2012) 55:1114–1127 1115

individually analysed. In addition to the comparison of the
three participant groups, associations of spot volumes with
rates of glucose disposal (GDR) and other variables were
explored.
Methods
Participants Ten LE and 11 OB individuals with normal
glucose tolerance and no family history of diabetes, and ten
obese individuals with type 2 diabetes were included in the
study (Table 1). In the obese type 2 diabetes group, diabetes
had been diagnosed for 2.8 ±0.9 years and was treated by
diet alone, sulfonylurea, metformin or human insulin (four,
five, one and three participants, respectively). Metformin,
sulfonylurea or NPH insulin were withdrawn 1 week before
the study, fast-acting human insulin was withdrawn after the
last meal the evening before. Patients were GAD-65-
antibody negative and without signs of diabetic retinopathy,
nephropathy, neuropathy and macrovascular complications.
Informed consent was obtained before participation, and the
study was approved by the local ethics committee and
performed in accordance with the Helsinki declaration.
Clamp experiments and biopsy procedures These were per-
formed as described [19]. Briefly, after an overnight fast the
participants underwent hyperinsulinaemic–euglycaemic
clamps (40 mU m−2 min−1 insulin for 4 h) combined with
whole-body indirect calorimetry; GDR, glucose oxidation,
lipid oxidation and non-oxidative glucose metabolism
(NOGM) were assessed as described [19, 20]. In the obese
type 2 diabetes group, plasma glucose was allowed to de-
cline to 5.5 mmol/l before glucose infusion was initiated.
Muscle biopsies were obtained from the vastus lateralis
muscle before and after the insulin infusion, and were frozen
in liquid nitrogen [19].
Sample preparation and protein labelling Samples were
homogenised in 2.4 μl (30 mmol/l Tris-base, 2 mol/l thio-
urea, 7 mol/l urea, 4% 3-[(3-cholamidopropyl)dimethylam-
monio]-1-propanesulfonate (CHAPS), pH 8.5) per mg
tissue, sonicated for 2 × 1 min and centrifuged for 2 ×
15 min (16,000g). After protein determination, supernatant
fractions were labelled by adding 1 μl of 400 pmol/μl
CyDye (Cy2, Cy3 or Cy5 ‘minimal dyes’; GE Healthcare,
Munich, Germany, in dimethylformamide (DMF)) to 50 μg
solubilised protein. Following 30 min at 4°C in the dark,
reactions were stopped by 1 μl 10 mmol/l lysine. After
10 min ampholine 2-4 (GE Healthcare) and dithiothreitol
(1.08 g/ml) were added (both at 1/10 volume of the total
volume). Basal and clamp biopsy lysates from individual
participants were alternately labelled with Cy3- or Cy5-dyes
(colour swap to exclude labelling effects). A standard (mix-
ture of equal protein amounts of all samples) was labelled
with Cy2 dye and used to improve the matching of gels and
normalisation of individual spotmaps.
2D-DIGE and image analysis This was performed as de-
scribed [21]. Briefly, for every analysis, two samples with
different dyes plus standard were combined (3 ×50 μg pro-
tein) and separated by carrier ampholyte-based isoelectric
focusing, followed by 15.2/1.3% acrylamide/bisacrylamide
gels [21]. Images were scanned (Typhoon TRIO-scanner,
GE Healthcare, Freiburg, Germany), cropped with Image-
Quant (GE Healthcare, Freiburg, Germany) and analysed
(spot detection and inter- and intra-gel spot matching) with
DeCyder-2D-V6.5 (GE Healthcare, Freiburg, Germany).
The estimated spot number was set to 10,000, and spots
<20,000 arbitrary units were removed.
Protein identification using nano-HPLC/ESI-MS/MS Two-
dimensional gel electrophoresis was performed as described
with 50 μg Cy2-labelled plus 200 μg unlabelled standard.
Spots were manually picked and in-gel trypsin digested [22,
23]. Tryptic peptides were extracted twice with 10 μl ace-
tonitrile/5% formamide (50/50 [vol./vol.]). Acetonitrile was
removed in vacuo, 5% formamide added to yield 20 μl, and
nano-HPLC-ESI-MS/MS performed [24]. MS/MS spectra
peaklists were generated using DataAnalysis-4 (Bruker-Dal-
tonics, Bremen, Germany). For identification, peaklists were
Table 1 Characteristics of individuals in the LE, OB and obese type 2
diabetes groups
Characteristic LE OB T2D
n (male/female) 10 (5/5) 11 (6/5) 10 (6/4)
Age (years) 51±1 49±1 50±1
BMI (kg/m2) 24.2±0.5 33.7±1.4* 33.5±1.1*
HbA1c (%) 5.5±0.1 5.4±0.1 7.8±0.5*†
HbA1c (mmol/mol) 37±0.8 36±1.0 61±5.9*†
Fasting plasma glucose (mmol/l) 5.7±0.1 5.7±0.2 10.0±0.6*†
Fasting serum insulin (pmol/l) 24±6 53±5* 95±10*†
Fasting serum C-peptide (nmol/l) 0.5±0.0 0.8±0.1* 1.2±0.1*†
Basal plasma glucose (mmol/l) 5.6±0.2 5.7±0.1 9.5±0.6*†
Clamp plasma glucose (mmol/l) 5.3±0.1 5.2±0.1 5.2±0.1
Basal serum insulin (pmol/l) 18±2 43±5* 77±7*†
Clamp serum insulin (pmol/l) 348±15 396±29 388±22
Basal GDR (mg m−2 min−1) 81±4 80±3 87±1
Clamp GDR (mg m−2 min−1) 352±18 244±21* 137±15*†
Data represent means±SEM
Plasma glucose, serum insulin, and C-peptide were measured as
described [19]
* p<0.05 compared with LE by one-way ANOVA; †p<0.05 compared
with OB by one-way ANOVA
T2D, obese and type 2 diabetes
1116 Diabetologia (2012) 55:1114–1127

correlated with UniProtKB/Swiss-Prot composite database
(release 10.08.2010) using MASCOT algorithm (V2.3.02)
[25] and the ProteinScape (V1.3, Bruker-Daltonics, Bre-
men, Germany) database. Searches were restricted to human
entries and performed with tryptic specificity allowing one
missed cleavage and mass tolerances of 0.4 and 0.6 Da for
MS and MS/MS experiments, respectively. Cysteine modi-
fication with propionamide was considered as fixed and
oxidation of methionine as a variable modification.
Proteins were assembled based on identifications using
ProteinExtractor (V1.0) in ProteinScape and sorted accord-
ing to identification scores. Each protein was identified with
at least two peptides (MASCOT peptide score >20), poten-
tial contaminants (keratins 1, 2, 9, 10, 14 and 16, and
albumin) were excluded. For identification, a score >50
was required. If more than one protein fulfilled this require-
ment the protein with highest score was assigned; the others
are listed in electronic supplementary material (ESM)
Table 1. For detection of phosphorylation or ubiquitinylation,
event searches were repeated with additional variable mod-
ification settings.
Western blots Solubilised protein (100 μg) was separated by
10% SDS-PAGE, transferred to polyvinylidene-fluoride
membranes and probed with anti-glyceraldehyde-3-phos-
phate-dehydrogenase (GAPDH) or anti-α-actin antibody
(Cell Signaling, Danvers, MA, USA and Abnova, Taipei,
Taiwan, respectively). Detection was with IRDye 800CW
goat-anti-rabbit-IgG (LI-COR Biosciences, Lincoln, NE,
USA) and Odyssey imaging (LI-COR Biosciences).
Data analysis and spot selection Data analyses were with R
(V2.10.1) [26]. Raw spot volumes were first standardised
(ratio with the respective Cy2 spot on same gel) and log-
transformed as proposed by DeCyder-2D-V6.5. Multiple
comparisons were by one-way ANOVA and, if appropriate,
subsequent Tukey’s honest test. Paired data were compared
with the paired t test. Correlations were analysed by Spear-
man’s rank-order correlation. Fold differences were calcu-
lated based on the difference of their mean log-transformed
volumes.
Spots that could be reliably measured/quantified in at
least five basal and five clamp biopsies per group were
selected if: (1) the difference between the highest and lowest
mean log-transformed spot volume of the three study groups
in basal or clamp biopsies was >0.41 (01.5-fold difference
of untransformed data) and p was <0.05; (2) basal and clamp
biopsy spot volumes correlated with clamp GDR (p<0.05)
or at least one other variable listed in ESM Table 2
(p<0.02); or (3) the difference between log-transformed
volumes of basal and clamp biopsies was >0.41 (1.5-fold
difference of untransformed data) in at least one study
group, and p<0.05.
Results
Clinical and metabolic characteristics The OB and obese
type 2 diabetes study groups were well matched with respect
to BMI, and all study groups were well matched with
respect to age (Table 1). Clamp GDR was lower in the OB
than in the LE group and again lower in the obese type 2
diabetes group than in the OB group.
Muscle proteome and differences between LE, OB and type
2 diabetes Overall, 2,852 protein spots were detected by the
software. In 107 of 128 spots that fulfilled the criteria for
protein identification, the protein was successfully identified
(for spectra results see PRIDE database, accession number
18608–18609)
Differential volumes between the three study groups were
displayed by 44 protein spots (Table 2; ESM Table 3). Some
proteins were identified in multiple spots, suggesting PTMs.
This was found, for example, for glycogen phosphorylase,
muscle form (PYGM), GAPDH, myosin regulatory light
chain 2, ventricular/cardiac muscle (MYL2), β-enolase
(ENO3) and myosin regulatory light chain 2, skeletal muscle
(MYLPF) with four, eight, seven, three and four spots, respec-
tively (Fig. 1; Table 2). Moreover, PYGM, MYL2 and
MYLPF represent examples where different spots with the
same protein displayed opposite changes in abundance be-
tween groups. Thus, in basal biopsies, the volume of PYGM
in spot 453 was higher in the obese type 2 diabetes and OB
groups compared with the LE group, whereas the volume of
PYGM in spot 450 was highest in the LE group (Fig. 2),
suggesting a shift in the abundance of PTMs. In addition, our
data suggest that PYGM in spot 453 was phosphorylated at
Ser15, and that the PTMs in MYL2 and MYLPF included
serine phosphorylations and ubiquitinylations (Table 2).
As for PYGM spots 450 and 453 (Fig. 2), differences
between groups tended to be smaller in clamp than basal
biopsies, resulting in 21 compared with 33 significantly
different spots between groups, respectively (Table 2). Apart
from this observation, results in clamp and basal biopsies
from the same individuals were fairly concordant and thus
served as an internal control (Fig. 2; Table 2).
Differences in spot volumes were most pronounced be-
tween the obese type 2 diabetes and LE groups (significant
in 29, 10 and five spots from basal, clamp or both basal and
clamp biopsies, respectively; Table 2). Almost the same
differences were observed between the OB and LE groups
(significant in 23, 12 or six spots from basal, clamp, or both
basal and clamp biopsies, respectively). Accordingly, only a
few spots displayed substantial differences between the
obese type 2 diabetes and OB groups.
Differentially expressed proteins comprised enzymes of
glucose metabolism, mitochondrial proteins, sarcomeric
proteins and proteins with other functions. With respect to
Diabetologia (2012) 55:1114–1127 1117

Table 2 Ratios of spot volumes between groups
Protein Gene accession
number
Spot Basal biopsies Clamp biopsies n
OB/LE T2D/LE T2D/OB OB/ LE T2D/ LE T2D/OB
Carbohydrate metabolism
Glycolytic
GAPDH P04406 1389 1.13 1.37* 1.22 1.21 1.46* 1.21 31
1398 1.25 1.23 0.99 1.25 1.28 1.02 24
1435 1.26 1.28 1.02 1.34 1.31 0.98 29
1443 1.35 1.32 0.98 1.42 1.30 0.92 28
1450 1.52* 1.38* 0.91 1.43* 1.19 0.83 30
1452 1.56* 1.46* 0.94 1.55* 1.36 0.88 30
1458 1.53* 1.49* 0.97 1.48* 1.32 0.89 28
1461a 1.75* 1.51 0.86 1.77* 1.42 0.80 19
PGAM2 P15259 1875 1.76* 1.95* 1.11 1.17 0.95 0.82 31
ENO3 P13929 1027 1.19 1.18 1.00 1.30 1.41* 1.09 27
1032 1.17 1.12 0.96 1.20 1.16 0.97 31
1072 2.20* 2.10* 0.95 2.24* 2.25* 1.00 30
PKM2 P14618 794 1.11 1.33 1.19 1.11 1.22 1.10 28
795 1.19 1.23 1.03 1.18 1.11 0.95 31
796 1.45 1.15 0.80 1.51 1.03 0.68 21
820 1.14 1.22 1.07 1.19 1.12 0.95 30
859 1.38 1.67* 1.21 1.09 1.29 1.19 19
Other
MDH1 P40925 1627 0.98 0.77 0.79 0.85 0.78 0.91 26
1630b 0.96 0.75 0.78 0.88 0.83 0.94 27
1635 0.96 0.73* 0.77 0.90 0.77 0.86 30
PYGM P11217 450 0.66 0.56* 0.84 0.80 0.74 0.93 30
451c 1.02 1.18 1.15 0.89 0.99 1.11 20
452 0.63 0,61 0.96 0.75 0.82 1.10 28
453c 1.47* 1.57* 1.07 1.19 1.17 0.99 30
Mitochondrial
ECH1 Q13011 1745 0.70 0.85 1.21 0.55* 0.83 1.49 22
GBAS O75323 1929 0.80 0.78 0.98 0.78* 0.80 1.03 24
1937 0.96 0.96 1.00 1.09 1.01 0.92 22
HADHB P55084 957 1.02 1.13 1.10 0.57 1.17 2.04* 28
HES1 P30042 2026 0.93 0.74* 0.80 0.95 0.74* 0.78 31
Sarcomeric
Fast
MYL1 P05976 2126 0.81 0.63 0.77 0.77 1.02 1.32 28
2582 1.56* 1.57* 1.01 1.57* 1.62* 1.04 20
2601 1.36 1.73* 1.28 1.47 1.75* 1.19 30
MYLPF Q96A32 2273 1.70* 2.22* 1.31 1.35 1.21 0.89 31
2334 0.54* 0.50* 0.92 0.84 0.72 0.86 26
2427 1.90* 1.94* 1.02 1.27 1.18 0.94 22
2447d 1.58* 1.90* 1.20 1.19 1.18 1.00 31
TNNT3 P45378 1538 1.67* 1.51 0.91 1.34 1.18 0.88 30
1540 1.48* 1.51* 1.02 1.29 1.13 0.88 31
1550 1.46* 1.45* 0.99 1.29 1.14 0.88 31
1602 1.16 1.12 0.96 1.10 1.07 0.98 28
1606 1.35* 1.50* 1.12 1.02 1.12 1.09 31
1118 Diabetologia (2012) 55:1114–1127

glucose metabolism, mean volumes of 17 of 17, or 16 of 17
spots in basal or clamp biopsies, respectively, assigned to
the glycolytic enzymes GAPDH, phosphoglycerate mutase-
2 (PGAM2), ENO3 and pyruvate kinase (PKM2) were
higher in the obese type 2 diabetes and OB groups than in
the LE group, and differences between groups were signif-
icant in eight (basal biopsies) or seven of these spots (clamp
biopsies). As GAPDH, ENO3 and PKM2 were identified in
Table 2 (continued)
Protein Gene accession
number
Spot Basal biopsies Clamp biopsies n
OB/LE T2D/LE T2D/OB OB/ LE T2D/ LE T2D/OB
Slow
MYL2 P10916 2220 1.20 1.20 1.00 0.75 0.91 1.21 31
2221e 0.37* 0.35* 0.94 0.53 0.75 1.43 23
2223 0.66 0.57 0.86 0.52* 0.51* 0.97 20
2227e, f, 0.34* 0.30* 0.88 0.52 0.58 1.13 31
2259 0.46* 0.31* 0.68 0.73 0.80 1.10 31
2264e 1.85* 2.49* 1.34 1.37 1.51 1.10 31
2269 0.51* 0.41* 0.80 0.89 0.79 0.89 30
MYL3 P08590 2141 0.76 0.58 0.76 0.68 0.80 1.17 30
2445 0.55* 0.48* 0.87 0.94 0.77 0.81 31
TNNT1 P13805 1571 0.78 0.78 1.00 0.60* 0.90 1.49 31
Other
DES P17661 890 1.00 1.18 1.18 0.54* 1.11 2.07* 28
Miscellaneous functions
ACTA2 P62736 200 1.12 0.96 0.85 1.09 0.88 0.81 16
1182 0.94 0.80* 0.86 0.83 0.77* 0.92 27
1835 0.45 0.36 0.80 0.92 0.85 0.93 19
ACTG2 P63267 1227 0.92 1.15 1.24* 0.85 1.11 1.30* 31
1793 1.01 0.82 0.81 0.95 0.82 0.86 30
UPF0366 protein Q9H7C9 2732 0.63 2.23 3.52* 0.77 1.88 2.43 18
CAP2 P40123 843 0.63* 0.73* 1.14 0.94 1.09 1.17 28
CFL2 Q9Y281 2290 0.70 0.86 1.23 0.62* 1.13 1.83* 22
CKM P06732 1039 1.03 1.12 1.08 1.15 1.30 1.13 25
1215 1.18 1.19 1.01 1.09 1.35* 1.23 29
1216 1.06 1.06 1.00 1.03 1.15 1.11 29
FGG P02679 904 1.00 0.99 0.99 0.62 1.05 1.70 30
951 1.08 1.11 1.03 0.60 1.22 2.05* 26
HSPA2 P54652 638 1.19 1.39 1.17 1.18 1.56* 1.33 31
HBA1 P69905 2737g 1.39 6.28* 4.51* 1.25 2.65 2.13 21
Muscle samples were subjected to 2D-DIGE, spot volumes determined and spots chosen for subsequent identification as described. Shown are
ratios of spot volumes between groups calculated based on the difference of mean log-transformed spot volumes. Listed are only spots with
significant differences and spots with proteins where other spots with the same protein were significantly different.
Log-transformed volumes and additional data are shown in ESM Table 3
* p<0.05 in one way ANOVA and post hoc Tukey honest significance test between log-transformed spot volumes
Phosphorylation at: a Ser210 or Thr211; b Ser332 or Ser333; c Ser15; d Ser15, Ser16 or Ser17;
e Ubiquitinylation at Lys165
Phosphorylation at: f Ser15 or Ser19; and g Tyr43
n, number of participants for whom spot volumes could be assigned
ACTA2, actin, aortic smooth muscle; ACTG2, actin, γ-enteric smooth muscle; CAP2, adenylylcyclase-associated protein 2; CFL2, cofilin-2;
CKM, creatine kinase M-type; DES, desmin; ECH1, Δ(3,5)-Δ(2,4)-dienoyl-CoA isomerase; HADHB, trifunctional enzyme subunit-β; MDH1,
malate dehydrogenase; TNNT1, slow skeletal muscle troponin-T; FGG, fibrinogen γ chain; HSPA2, heat shock-related 70 kDa protein 2; HBA1,
haemoglobin subunit α; T2D, obese and type 2 diabetes
Diabetologia (2012) 55:1114–1127 1119

multiple spots, we analysed whether the sums of volumes of
all identified spots with the respective enzyme (addition of
raw spot volumes before analysis) were different. This
revealed that, in addition to the single spot with PGAM2,
these summative spot volumes were different between
groups for GAPDH (LE −0.07 ±0.10, OB 0.17 ±0.08, obese
type 2 diabetes 0.20 ±0.06, p<0.05, corresponding to ratios
of OB/LE 1.27, obese type 2 diabetes/LE 1.31, and obese
type 2 diabetes/OB 1.03) and ENO3 (LE −0.42 ±0.13, OB
0.00 ± 0.08, obese type 2 diabetes 0.04 ± 0.08, p<0.05,
corresponding to ratios of OB/LE 1.52, obese type 2 diabe-
tes/LE 1.47, and obese type 2 diabetes/OB 0.96), whereas
for combined PKM2 spots differences were NS. GAPDH
levels were also analysed in a western blot. Although, in
contrast to the proteome data, differences were NS, a similar
tendency was confirmed (Fig. 3).
In contrast to glycolytic enzyme spots, spots withΔ(3,5)-
Δ(2,4)-dienoyl-CoA isomerase (ECH1) an enzyme in-
volved in fatty acid oxidation, and two mitochondrial pro-
teins, NipSnap-homologue-2 (GBAS), and ES1 protein
homologue (HES1) tended to be less abundant in obese type
2 diabetes and OB (Table 2). Moreover, multiple spots
containing the fast muscle proteins myosin light chain 1
(MYL1), MYLPF, or fast skeletal muscle troponin-T
(TNNT3) were upregulated, whereas spots containing the
slow-muscle proteins MYL2, myosin light chain 3 (MYL3),
or slow skeletal muscle troponin-T (TNNT1) were down-
regulated in OB and/or obese type 2 diabetes groups com-
pared with the LE group. If summative volumes of all spots
containing the respective enzymes were analysed as de-
scribed above, these were higher in the OB and obese type
2 diabetes groups than the LE group for MYLPF (p<0.05,
ratios OB/LE 1.50, obese type 2 diabetes/LE 1.85, obese
type 2 diabetes/OB 1.23), and TNNT3 (p<0.05, ratios OB/
LE 1.41, obese type 2 diabetes/LE 1.46, obese type 2
diabetes/OB 1.04) and lower in the OB and obese type 2
diabetes groups than the LE group for MYL3 (ratios OB/LE
0.67, obese type 2 diabetes/LE 0.52, obese type 2 diabetes/
OB 0.78). Differences of summative spots containing
MYL1 or MYL2 were NS.
Correlations of spot volumes with physiological and clinical
variables Although mean clamp GDRs were significantly
different between study groups, individual values overlap-
ped (Fig. 4). To identify additional proteins that might be
more strongly related to GDR or other variables than to the
Fig. 1 a Representative two-dimensional gel. Similar amounts of
protein from basal biopsies from an LE and an obese type 2 diabetic
individual were labelled with different CyDyes, subjected to 2D-DIGE
and scanned as described. Green, LE proteins dominate; red, type 2
diabetes proteins dominate; yellow, both signals similarly present. b–d
Portions of this image were magnified to demonstrate multiple spots
with PYGM (b), GAPDH (c) and MYL2 (d) identified in more detail
Fig. 2 Volumes of spots identified as PYGM in LE, OB and obese
type 2 diabetic participants. Muscle samples were analysed and protein
names assigned as described in Table 2. Shown are representative
portions of the 2D-DIGE gels with the spots containing PYGM (a)
and individual volumes of spot 453 (b) and 450 (c) in basal and clamp
biopsies. Thin lines connect data of basal and clamp biopsies of the
same individual; means are shown as strong horizontal lines; * p<0.05
compared with LE. Triangles, basal; circles, clamp biopsies. T2D,
obese and type 2 diabetes
1120 Diabetologia (2012) 55:1114–1127

multifactorial type 2 diabetes or obesity state, we analysed
correlations of spot volume data of all study participants
with GDR and other variables. For example, volumes of
spot 1458 (GAPDH) correlated negatively with clamp GDR
and glucose oxidation, but positively with clamp lipid oxi-
dation (Fig. 4). Similar negative correlations with clamp
GDR, NOGM and glucose oxidation, but positive correla-
tions with lipid oxidation, were found in basal biopsies for
almost all spots with identified glycolytic or fast-muscle
proteins, whereas opposite correlations existed for most
spots with tricarboxylic acid (TCA) or respiratory chain
enzymes, or slow-muscle proteins (Fig. 5; ESM Table 2).
Data were similar in clamp biopsies, except that fewer
correlations were statistically significant (ESM Table 2).
The same correlation pattern as with single spot volumes
was also observed for summative volumes of multiple spots
with the same identified protein. Thus, for the glycolytic
enzymes GAPDH and ENO3, and the fast-muscle proteins
MYLPF and TNNT3, these summative volumes in basal
biopsies correlated negatively (p<0.05) with clamp GDR.
In most cases, they also correlated negatively with clamp
NOGM and glucose oxidation, and positively with clamp
lipid oxidation. Correlations were opposite for mitochondri-
al proteins aconitate hydratase (ACO2), ATP subunit-α
(ATP5A1), ATPase subunit-β (ATP5B) and GBAS, and
the slow-muscle protein MYL3 (ESM Table 4).
Effect of insulin stimulation Several spots displayed different
volumes between basal and clamp biopsies (Table 3). These
proteins included the sarcomeric proteins actin, α skeletal
muscle (ACTA1), tropomyosin α-1 chain (TPM1), MYL1,
MYL3 andmyosin heavy chain 2 (MYH2), and the mitochon-
drial proteins ATP5A1 and trifunctional enzyme subunit-β
(HADHB).
Discussion
Skeletal muscle insulin resistance is critical to the pathogen-
esis of type 2 diabetes and obesity. Here, we investigated
with the sensitive 2D-DIGE proteomic approach whether
changes in abundance of proteins or patterns of proteins in
biological pathways could contribute to these forms of
insulin resistance.
Fig. 3 Western blot analysis of GAPDH protein expression. Similar
protein amounts (100 μg) of solubilised basal and clamp muscle were
subjected to western blot analysis with GAPDH-specific antibody as
described. α-Actin served as loading control and was not different
between groups. Shown are representative immunoblots and means
±SEM of the LE, OB and obese type 2 diabetes groups. White bars, LE
(n010); hatched bars, OB (n011); black bars, obese type 2 diabetes
(n010). AU, arbitrary units
Fig. 4 Correlations between spot 1458 (GAPDH) abundance and
clamp GDR (a), clamp glucose oxidation (b) and clamp lipid oxidation
(c). Shown are spot volumes of basal and clamp biopsies of the LE, OB
and type 2 diabetes groups. Dashed and solid lines represent linear
regressions for basal and clamp biopsies, respectively; all correlations
were p<0.05. White symbols, basal; black symbols, clamp; circles, LE;
squares, OB; diamonds, obese type 2 diabetes
Diabetologia (2012) 55:1114–1127 1121

A major observation of our analysis is that spot volumes
related to multiple glycolytic proteins were higher in OB
and obese type 2 diabetes groups than in the LE group, and/
or correlated negatively with clamp GDR (Figs 5 and 6).
This suggests that upregulation of glycolysis by increased
enzyme abundance could be a major characteristic of
insulin resistance. In particular, spots containing GAPDH,
PGAM2 and ENO3 were increased in obese and/or diabetic
individuals. All five spots containing PKM2 also tended to
be higher in obese type 2 diabetes and OB, but although the
difference was significant in one spot, the difference in
overall abundance did not reach statistical significance.
Fig. 5 Correlation of spot volumes with GDR, glucose oxidation, non-
oxidative glucose metabolism and lipid oxidation. Coloured circle
segments with spot numbers and protein abbreviations represent spots
with volumes that correlated with GDR (p<0.05) in basal biopsies,
starting with strongest negative correlation on the left and moving to
the strongest positive on the right. Lines connect these segments with
segments that represent the four variables, and line strength indicates
the strength of negative (blue) or positive (red) correlation. Colours of
circle segments indicate protein categories: dark blue, glycolytic; light
blue, fast muscle; dark red, respiratory chain; orange, TCA cycle;
purple, slow muscle; white, other functions. Detailed and additional
correlation data are provided in ESM Table 2. ACTG2, γ-enteric
smooth muscle actin; CAP2, adenylyl cyclase-associated protein 2;
CKM, creatine kinase M-type; GOT1, aspartate aminotransferase;
HES1, ES1 protein homologue, mitochondrial; HSPA2, heat shock-
related 70 kDa protein 2; IDH2, isocitrate dehydrogenase; IMMT,
mitochondrial inner membrane protein; MDH1, malate dehydrogenase;
MYOZ1, myozenin-1; NDUFB10, NADH dehydrogenase [ubiqui-
none] 1 β subcomplex subunit 10; NDUFS1, NADH-ubiquinone ox-
idoreductase 75 kDa subunit; UQCRC1, cytochrome b-c 1 complex
subunit 1
1122 Diabetologia (2012) 55:1114–1127

Most spots containing glycolytic enzymes correlated also
significantly negatively with clamp GDR and glucose oxi-
dation, and positively with clamp lipid oxidation.
In contrast to glycolytic proteins, proteins involved in the
TCA cycle, mitochondrial respiration and other mitochon-
drial functions appeared less abundant in insulin resistance.
In basal biopsies, 14 of 21 spots identified as proteins
involved in these processes correlated positively (p<0.05)
with clamp GDR and/or NOGM. Moreover, significant pos-
itive correlations were also observed for summative spot
volumes (sum of volumes of all spots with the same
identified protein) relating to cytoplasmic malate dehydro-
genase (MDH1), an enzyme controlling TCA cycle pool
size and providing contractile function [27], and the mito-
chondrial enzymes ACO2, ATP5A1, ATP5B and GBAS.
These results, suggesting reduced TCA cycle and mitochon-
drial protein content in insulin resistance, are consistent with
previous findings of reduced enzyme activities [10, 28, 29],
protein expression [16, 17, 30], altered phosphorylation [16,
31, 32], altered transcript levels [10, 33, 34] or altered flux
through mitochondrial ATP synthase [8].
Taken together, our observations of increased glycolytic
and decreased TCA cycle or mitochondrial protein content
in insulin resistance (Figs 5 and 6) support the hypothesis
that altered glycolytic and oxidative capacities contribute to
its phenotype. Although the mitochondrial content and dys-
function in insulin-resistant states has been studied exten-
sively [2–12, 32, 35, 36], few data exist about the potential
role of glycolytic enzymes in insulin resistance. Consistent
with increased glycolytic enzyme activity in insulin resis-
tance are previous findings that the ratio between glycolytic
(phosphofructokinase, GAPDH, hexokinase, PYGM) and
oxidative enzyme activities (citrate synthase, cytochrome
oxidase) was negatively correlated with insulin sensitivity
[11], and that phosphofructokinase activity decreased upon
10% weight loss in obese individuals [37]. In contracting
muscle, factors related to the energy state appear to control
glycolysis [38], and therefore impaired mitochondrial func-
tion with less energy production could be responsible for
increased glycolysis and, potentially, glycolytic enzyme
abundance. Conversely, increased glycolytic enzyme levels
and activity under basal conditions could result in substrate
overload for the mitochondria, contribute to ‘metabolic in-
flexibility’ in insulin resistance, i.e. a reduced ability of
skeletal muscle to switch from fatty acid oxidation in the
fasting state to glucose oxidation in the insulin-stimulated
state and back [39], and may dispose skeletal muscle
towards lipid accumulation [7, 39].
In our analysis, several proteins were identified in multi-
ple spots. For example, the key glycogenolytic enzyme
PYGM was found in four spots, one of which was increased
in OB and obese type 2 diabetes (spot 453), while another
was reduced in obese type 2 diabetes (spot 450). Moreover,
at least in LE individuals, spot 453 and 450 tended to be
increased and decreased, respectively, by the clamp. These
data suggest a shift in abundance between two differently
post-translationally modified PYGM proteins that may have
different properties. Interestingly, spot 453 contained
PYGM phosphorylated at Ser15, which results in activation
[40]. Although glycogen synthase has extensively been
studied in insulin-resistant skeletal muscle in the past years,
less information is available about PYGM. The notion that
the observed shift between spot 450 and 453 might be
associated with higher activity of the enzyme in insulin
Table 3 Ratios of spot volumes between clamp and basal biopsies
Protein Spot Ratio clamp/basal
LE OB T2D
Carbohydrate metabolism
PYGM 450 0.69* 0.83 0.92
Mitochondrial
ATP5A1 763 0.43 0.78 0.45*
HADHB 957 0.90 0.50* 0.93
PARK7 2094 1.04 1.23 2.62*
Sarcomeric
Fast
MYL1 2126 1.09 1.04 1.78*
Slow
MYH2 765 0.58* 0.72 0.39
MYL3 2141 1.39 1.24 1.92*
2445 0.57* 0.98 0.92
Other
ACTA1 1081 1.16 1.38* 1.97*
1185 1.11 1.23 1.84*
TPM1 1632 1.22 1.18 1.76*
Miscellaneous functions
ACTG2 1793 1.16 1.08 1.16*
CA3 1839 1.07 1.23 1.43*
CCT5 764 0.59* 0.76 0.48
FGB 804 0.77 0.66* 0.91
FGG 904 0.87 0.53* 0.92
HSPA8 627 1.04 1.01 1.13*
Muscle samples were analysed and protein names assigned as de-
scribed in Table 2
Shown are ratios of spot volumes between clamp and basal biopsies of
each group calculated based on the difference of mean log-transformed
spot volumes (ESM Table 3)
* p<0.05 in paired t test between log-transformed spot volumes of basal
and clamp biopsies
ACTG2, actin, γ-enteric smooth muscle; CA3, carbonic anhydrase 3;
CCT5, T-complex protein 1 subunit ε; FGB, fibrinogen β chain; FGG,
fibrinogen γ chain; HADHB, trifunctional enzyme subunit-β; HSPA8,
heat shock cognate 71 kDa protein; PARK7, protein DJ-1; TPM1,
tropomyosin α-1 chain; T2D, obese and type 2 diabetes
Diabetologia (2012) 55:1114–1127 1123

resistance is, however, not supported by previous studies.
Thus, activity of PYGM was not found to be different in
muscle from obese individuals with type 2 diabetes [11, 41]
or regulated by changes in glucose or insulin levels [42]. On
the other hand, lack of PYGM activity is associated with
insulin resistance [43]. In any case, regulation of PYGM by
PTMs might be quite complex. For example, three other
phosphorylation sites of PYGM have recently been detected
[31]. Activating and deactivating modifications may there-
fore counterbalance and thus ensure a normal glycogenoly-
sis in insulin-resistant muscle under basal conditions. This
balance may be disturbed, for example, in hypoglycaemia,
where PYGM activity increased by 50% only in those with
type 2 diabetes and not in control participants [41].
GAPDH was found in eight spots in our study, suggest-
ing PTMs, and in fact one spot (1461) was detected as being
phosphorylated. Multiple PTMs of GAPDH have also been
described in a recent analysis in GAPDH-overexpressing
human embryonic kidney cells, including deamidation,
methylation, methionine oxidation and phosphorylation
[44]. In our study, all eight GAPDH spots had higher mean
volumes in obese participants with type 2 diabetes and OB
individuals compared with LE participants, and this was
significant in five spots. Consistently, western blots revealed
a tendency of increased GAPDH protein abundance in these
states. As no substantial differences between the different
GAPDH spots were found regarding their volume ratios
between groups or following insulin stimulation, it is not
possible to conclude from our data that the underlying
PTMs are relevant for causing insulin resistance or are
modified by insulin.
As skeletal muscle contains type I (slow oxidative), IIa
(fast oxidative glycolytic) and IIx (fast glycolytic) fibres [3,
35, 36], altered fibre type distribution could have contribut-
ed to our results. Type I, IIa, and IIx fibres contain MYH7,
MYH2 and MYH1, respectively [45]. In our study, no spots
containing MYH7, MYH2 and MYH1 were identified as
different between groups or correlated to variables of insulin
resistance, and MYH2 was only identified because of a
difference between clamp and basal biopsies. This argues
Fig. 6 Role of identified proteins in metabolic pathways. Data refer to
basal biopsies; for detailed data see Table 2 and ESM Tables 2 and 4. A
complete dark red or dark blue label indicates proteins where combined
spot volumes (combination of all spots where the respective protein
was identified) were significantly negatively (red) or positively (blue)
correlated with GDR (p<0.05). Partial dark red and dark blue labelling
of a single protein indicates that at least one spot was significantly
negatively and one spot significantly positively correlated with GDR
(correlation of combined spot volumes NS). Light blue indicates proteins
where all identified spots had negative correlation coefficients (correlation
of combined spot volumes NS). * p<0.05 between groups of LE, OB and
obese participants with type 2 diabetes. ALDOA, aldolase; IDH2, isoci-
trate dehydrogenase; NDUFB10, NADH dehydrogenase [ubiquinone] 1β
subcomplex subunit 10; NDUFS1, NADH-ubiquinone oxidoreductase
75 kDa subunit; PGM1, phosphoglucomutase-1; TPT1, triosephosphate
isomerase; UQCRC1, cytochrome b-c 1 complex subunit 1
1124 Diabetologia (2012) 55:1114–1127

against a major role of differences in fibre-type distribution.
In contrast to heavy chains, marked differences between
groups were observed for other sarcomeric proteins. Most
spots of fast myosin light chain isoform MYLPF and all
spots of the fast-muscle protein TNNT3 were upregulated in
OB and/or obese type 2 diabetes groups, and upregulation
was also confirmed if all identified spots containing one of
these proteins were combined before analysis. Conversely,
combined spots of the slow myosin light chain isoform
MYL3 were downregulated. Combined spots were not dif-
ferent between groups for MYL1 or MYL2, but the marked
differences in individual spots suggest a role of differential
PTMs. Taken together, our data thus support the notion that
in OB and obese type 2 diabetic individuals muscle proper-
ties are shifted to a fast-twitch glycolytic pattern in the
absence of marked changes in fibre-type distribution. The
absence of marked changes in fibre-type distribution in OB
and obese type 2 diabetic participants is also supported by
several [3, 35], but not all [36], previous studies.
The fact that only a few spots displayed differences
following insulin stimulation may be explained by the rela-
tively short time for changes in protein expression or PTMs.
Spot volumes related to PYGM and ATP5A1 tended to be
lower in the clamp biopsies. Stronger changes were found in
sarcomeric proteins, including insulin-stimulated upregula-
tion of ACTA1, MYL1 and MYL3, and downregulation of
MYH2 spots. These changes may largely be due to phos-
phorylations [31, 46] and/or other PTMs. Insulin elicits
rapid dynamic remodelling of actin filaments, and this is
necessary for GLUT4 translocation [47, 48]. MYH2 phos-
phorylation has been implicated to participate in GLUT4
storage vesicle translocation [49]. Taken together, these data
support the notion that insulin actions include alterations of
proteins involved in contraction and/or intracellular
transport.
More differences in spot volumes were found be-
tween the OB and LE groups than between the obese
type 2 diabetes and OB groups. This suggests that
while obesity and the obesity-related insulin resistance
is associated with altered expression of more abundant
proteins and/or their PTMs, the additional reduction in
clamp GDR in obese type 2 diabetes was mainly asso-
ciated with changes of less abundant proteins, e.g.
signalling intermediates that were not detectable by
the proteomic approach.
The fluorescence labelling method used here has recently
been applied to analyse the mitochondrial skeletal muscle
proteome after endurance exercise training [14], but has so
far not been applied to compare the muscle proteome of LE
with OB or obese type 2 diabetic individuals. It has a higher
sensitivity and a more dynamic range compared with the
methods used in previous studies [15, 16]. Nevertheless, of
the 36 spots relating to 20 proteins found significantly
different by >1.5-fold between the groups in our study,
several have also been identified in these previous two-
dimensional-gel-based studies. This includes six from 13
proteins identified as different in a study with lean, obese
and morbidly obese participants [15], and four from eight
proteins identified as different in our own previous investi-
gation with type 2 diabetic and control individuals [16].
Thus, these two-dimensional-gel proteomic studies deliv-
ered largely consistent findings. Another recent study used
one-dimensional SDS-PAGE and label-free MS/MS-based
identification and quantification [17]. Of 92 proteins in-
creased more than twofold and of these, the 15 significantly
different between OB and/or obese type 2 diabetic and LE
participants in that study, only six and one, respectively,
were also identified by us. This is potentially explained by
the different method, in which quantification is based on
peptide abundance rather than whole-protein abundance,
and PTMs are not detected.
In summary, we have shown in human skeletal muscle that
insulin resistance in obesity is associated with different ex-
pression and/or PTMs of multiple proteins and that the in-
creased abundance of glycolytic enzymes and the decreased
abundance of mitochondrial proteins appear to be part of its
phenotype. In addition, our data suggest that this is accompa-
nied by a shift in abundance from slow to fast myosin light
chain isoforms in the absence of detected changes in myosin
heavy chain isoforms, consistent with a shift of muscle prop-
erties towards a fast-twitch glycolytic pattern without marked
changes in fibre-type distribution. Fewer differences were
found between obese diabetic and OB than between LE and
obese individuals, suggesting that the additional type-2-
diabetes-associated insulin resistance is largely related to other
mechanisms such as signalling events not detected by the
method. Compared with focused biochemical data, the
changes in protein abundance and PTMs observed in
multiple pathways add information on how these inter-
act in insulin resistance. The roles of several of the
differentially regulated proteins remain to be elucidated.
Acknowledgements We acknowledge L. Hansen, C. B. Olsen, K.
Pfeiffer, S. Link and C. Fischer-Lahdo for skilled technical assistance.
Funding The study was supported by grants from the Forum
programme at Bochum University, the German Diabetes Association,
the Danish Medical Research Council, and the Excellence Grant 2009
from the Novo Nordisk Foundation.
Contribution statement KH, WS, KL, HB-N, KS, HEM and HHK
conceived and designed the study. JG, GP, KH, KL, KS and WS
performed the experiments. JG, GP, KH, JWD, KL, KP and HHK
analysed and interpreted the data. JG, GP, KH and HHK drafted the
manuscript and all authors revised the manuscript for intellectual
content and approved the final version.
Duality of interest All authors declare that there is no duality of
interest associated with this manuscript.
Diabetologia (2012) 55:1114–1127 1125

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