Hamed Rezaei Nasab1, Abdol Hamid Habibi2, Masoud Nikbakht2, Mohammad Rashno3,4, Saeed Shakerian2. 1. Department of Exercise Physiology, Faculty of Sport Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran. Electronic Address: hamed.rezai93@yahoo.com. 2. Department of Exercise Physiology, Faculty of Sport Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 3. Department of Immunology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 4. Cellular and Molecular Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Changes in glucose, fat and protein metabolism are
usually observed in patients with diabetes. These metabolic
abnormalities can lead to a wide range of long-term effects
called diabetes complications. Several studies have shown
the directly negative effects of diabetes mellitus on the
cardiac muscle (1, 2). In addition, cardiovascular diseases
are the main cause of death in diabetic patients, not only
due to coronary artery disease and high blood pressure but
also due to the direct effects of diabetes complications on
the heart, independent of other pathologic factors (3).However, physical activity affects many physiological
systems of the body (4), including the structure and function
of the myocardium (5). Studies have shown that the
myocardium adapts structurally and efficiently to the type
of stimulus provided by physical activity, i.e. endurance
or strength (6). Tissue changes resulting from physical
activity also take place at the level of gene expression
(7) including changes in the biogenesis of mitochondria
and myosin heavy chains (MHCs) of the myocardium
(8, 9). Mitochondrial biogenesis, or increasing the size
and number of mitochondria, is a complex process that
requires combined function of different mechanisms and the
controlled expression of many genes. PGC-1 which is a cell
receptor and facilitates the release of mitochondrial proteins,
is the most important regulator of mitochondrial biogenesis.
PGC-1 has two, alpha and beta, isoforms, both are involved
in this process, but alpha is more important (10).Studies have shown that other members of this family
of transcription coactivators are activated in response to
environmental stimuli, such as heat and physical activity.
They also play an important role in maintaining glucose
homeostasis, lipid homeostasis, energy homeostasis
and possibly in pathogenic conditions such as obesity,
diabetes, neurodegeneration, and heart diseases (11).
Since the heart has a very high energy demand and
has basically no energy reserves, it needs to constantly
produce great amounts of energy in the form of adenosine
triphosphate (ATP) at a high speed to maintain contraction
performance and ion homeostasis. Most of the ATP in
mitochondria is produced by oxidative phosphorylation,
and fatty acids (FA) and carbohydrates are the primary
energy substrates. It must be noted that FA account for
50-75% of ATP production in the heart (12, 13). But a
diabetic heart cannot completely use glucose due to
insulin deficiency, and therefore may be forced to use
FA as its energy sources almost exclusively (14). FA
metabolism also consumes more oxygen per mole than
glucose and thus increases oxidative stress in the heart
tissue and reduces cardiac performance (15).Dichloroacetate (DCA), is imported by cells through
the monocarboxylate transporters and mostly a sodiumlinked monocarboxylate transporter also named solute
carrier family-5 member 8 (SLC5A8), while access to the
mitochondrial matrix is achieved by the mitochondrial
pyruvate carrier system (16). Studies have shown that
glucose incorporation into glycogen was decreased in
diabetic rats when DCA was used to activate pyruvate
dehydrogenase (PDH); which was accompanied by
an increase in glucose oxidation and a reduction of FA
oxidation (beta oxidation) in peripheral tissues of diabetic
rats (17). The pyruvate dehydrogenase complex (PDC)
is a multifunctional complex in the mitochondrial matrix
and has the role of gatekeeper in the tricarboxylic acid
cycle (TCA) and oxidative phosphorylation. DCA
and some of its derivatives play an important role in
this mechanism by activating PDC and regulating cell
metabolism in response to diabetes and other conditions
that increase the beta oxidation of FA e.g. endurance
training (18). However, despite studies done on PGC-1α
and its effects on diabetes as well as DCA consumption,
findings are still very contradictory and, to the best of our
knowledge, a study that can investigate the impact of DCA
consumption on PGC-1α expression and its relation with
aerobic training has not been conducted yet. Given the
need to develop therapeutic strategies to prevent or treat
diabetes complications, we conducted this study to assess
whether DCA consumption after endurance training can
reduce the complications of the PGC-1α mechanism in
diabetic patients.
Materials and Methods
In this experimental study, the Ethical guidelines set by
Shahid Chamran University of Ahvaz, Iran, was considered
during all stages of the experiment (EE/97.24.3.70001/
scu.ac.ir). The present study was designed as a posttestonly with the control groups experiment. In this study, 64
male Wistar rats at 8 weeks of age and weighting 200 ± 12
g were purchased from the Physiology Research Center,
Ahvaz Jundishapur University of Medical Sciences, Iran.
Rats were kept under the conditions of an even split of 12
hours of light and 12 hours of darkness) at 22 ± 2˚C and
50% humidity, fed with special rat food and water.After one week of familiarization with the laboratory
environment, rats were matched based on weight and
were randomly divided into eight groups including
healthy control groups (n=7), healthy control group+DCA
(n=7), healthy endurance training group (n=7), healthy
endurance training group+DCA (n=8), diabetes control
group (n=7), diabetes control group+DCA (n=8), diabetes
endurance training group (n=8), and diabetes endurance
training group+DCA (n=8).Daily intraperitoneal injections of 50 mg/kg body weight
of DCA was used to inhibit PDK4 in the myocardium (19).
After 12 hours of food deprivation, induction of diabetes was
done by intraperitoneal injection of 50 mg/kg body weight
of the STZ solution dissolved in 0.05 M citrate buffer with
4.5 pH (20). The equivalent volume of citrate buffer was also
intraperitoneally injected to non-diabetic rats. After 48 hours,
with a small lancet cut on the tail vein, a drop of blood was
placed on a glucometer strip and the strip was read using a
Glucotrend 2glucometer (Roche, Switzerland). Rats whose
glucose level was higher than 300 mg/dl were considered
diabetic. The rats’ blood sugar levels were measured again
at the end of the training program to ensure they had not
returned to normal (21).
Dichloroacetate
DCA was injected to rats intraperitoneally at 50 mg/kg
body weight in the form of 24-hour intervals, dissolved
in methyl cellulose 400 cP and combined with calcium
gluconate (22).
Endurance training protocol
The protocol was carried out for six weeks (five days/
week). First, training groups were trained for seven days with
a treadmill (model LE7800; Harvard Apparatus, France) at
a speed of 15 m/minutes for 20 minutes. Then, the duration
and speed were gradually increased over the course of six
weeks, so that in the final week the speed reached 30 m/
minutes and the training time reached 50 minutes/day, which
was equivalent to 75% of the maximum oxygen consumed.
Electric shocks were performed on the rats to make them
complete the training during the course of the experiment.
Control groups were kept in cages untreated during the
training period (Table 1) (23).
Table 1
Training protocol
Week
1(acclimatization)
2
3
4
5
6
7
Speed (m/minutes)
15
20
24
24
28
28
30
Time (minuts)
20
30
30
40
40
50
50
72 hours after the last training session, 64 rats were
anesthetized by intraperitoneal injection of ketamine (90
mg/kg body weight) and Xylazin (90 mg/kg body weight)
and the myocardium was immediately removed and
frozen in liquid nitrogen and transferred to -80˚C until
used for further analysis.Training protocol
Isol-RNA was used to extract mRNA. About 100
milligrams of myocardium tissue was ground and
homogenized in one milliliter of Isol-RNA Lysis Reagent.
Afterwards, the homogeneous product was centrifuged for
10 minutes at 12000 g and 4˚C, the supernatant was removed,
and transferred to a new microtube. In the next step, 200
μl of chloroform was added to the separated supernatant
and vigorously stirred for 15 seconds. Then, micro tubes
were re-centrifuged for 15 minutes at 12000 g and 4˚C. The
aqueous phase was removed and 600 μl of isopropyl alcohol
was added and centrifuged at 12000 g to extract total RNA.
The concentration of RNA and its purity were calculated
by controlling the ratio of 260/280 nm OD where values
between 1.8 to 2 were defined as acceptable purity. Synthesis
of cDNA was carried out using Takara’s cDNA synthesis kit,
according to the manufacturer’s instructions. Expression of
the desired genes was measured using real-time polymerase
chain reaction (PCR) and the results were quantified using
the 2-ΔΔCT formula (24). PCR reactions were performed using
AMPLIQON RealQ Plus 2x Master Mix Green High ROX.
40 cycles were considered for each cycle of real-time PCR.
And the temperatures of each cycle were set at 94˚C for 20
seconds, 60-58˚C for 30 seconds and 72˚C for 30 seconds.
GAPDH was used as the reference gene to measure relative
gene expression and melting curve analysis was performed
to control the specificity of the product. The sequence of the
primers used in the study is reported in Table 2.
Table 2
Mouse-specific primer pairs used for quantitative reverse transcription polymerase chain reaction
Gene
Primer sequence (5ˊ-3ˊ)
Base per
Accession No.
GAPDH
F: TGATTCTACCCACGGCAAGTT
21
M17701.1
R: TGATGGGTTTCCCATTGATGA
PGC-1α
F: TGGAGTCCACGCATGTGAAG
20
NM_013196.1
R: CGCCAGCTTTAGCCGAATAG
PDK4
F: TATCGACCCCAACTGCGATG
20
NM_053551.1
R: TGGATTGGTTGGCCTGGAAA
Blood analysis
The concentration of PGC-1α in the serum was assessed
and quantified using an enzyme-linked immunosorbent
assay (ELISA) kit according to the manufacturer’s
instructions (Cusabio- EL018425RA-USA). These
concentrations were expressed as picograms per milligram
of total protein (pg/ml protein). Detection range was in the
domain of 125-8000 pg/ml with 31.25 pg/ml sensitivity
(Table 3).
Table 3
Physical characteristics and plasma metabolites of groups
Group
CONT
CONT+DCA
TRA
TRA+DCA
DM
DM+DCA
DM+TRA
DM+TRA+DCA
Variable
Starting body weight (g)
204 ± 7
207 ± 8
208 ± 9
211 ± 5
205 ± 11
209 ± 6
208 ± 10
209 ± 7
terminal body weight (g)
223 ± 14
198 ± 11
188 ± 10
168 ± 11
216 ± 14
182 ± 12
163 ± 12
149 ± 6
Starting glucose (mg/dl)
104 ± 11
103 ± 6
107 ± 8
108 ± 10
440 ± 81
490 ± 61
412 ± 77
472 ± 58
Terminal glucose (mg/dl)
110 ± 14
111 ± 6
107 ± 8
108 ± 10
407 ± 69
328 ± 52
274 ± 32
178 ± 28
PGC-1α (pg/ml)
75.7 ± 12.6
70.1 ± 10.8
59.6 ± 9.1
73.1 ± 14.8
82.3 ± 13.8
70.3 ± 12.3
64.7 ± 11.7
60.5 ± 10.9
Data are presented as mean ± SD. CONT; Control, DCA; Dichloroacetate, TRA; Training, and DM; Diabetes.
Statistical analysis
Shapiro-Wilk test was used to determine the normality
of the data and Levene’s test was used to test the
homogeneity of the variances. One-way ANOVA and
Tukey’s test were used to determine the difference between
the groups’ variables. All statistical analyses were done at
a significance level of P<0.05 (SPSS Statistics 22).
Results
The results of the study showed that PDK4 gene
expression was higher in the endurance training group
(P=0.018), diabetes+endurance training group (P=0.008),
diabetes+endurance training+DCA group (P=0.001) and
endurance training+DCA group (P=0.026) compared to
the control group (Fig.1). PGC-1α gene expression in
the endurance training group was also higher compared
to the control group (P=0.020) but was lower in the
diabetes+endurance training+DCA group (P=0.003) and
diabetes+DCA group (P=0.001) compared to the control
group (Fig.2).
Fig.1
The normalized gene expression of PDK4 in different groups.
From left to right, control group (CONT, n=7); diabetes group (DM, n=7);
control+DCA group (CONT+DCA, n=7); diabetes+DCA group (DM+DCA,
n=8); healthy group+training group (TRA, n=7); diabetes+training
group (DM+TRA, n=8); healthy+training+DCA group (TRA+DCA, n=8);
diabetes+training+DCA group (DM+TRA+DCA, n=8). Data are expressed
as mean ± SD.
*; P<0.05, **; P<0.01 compared with the control group, #; P<0.05, ##;
P<0.01 compared with the diabetic group, and DCA; Dichloroacetate.
Fig.2
The normalized gene expression of PGC-1α in different groups.
From left to right, control group (CONT, n=7); diabetes group (DM, n=7);
control+DCA group (CONT+DCA, n=7); diabetes+DCA group (DM+DCA,
n=8); healthy group+training group (TRA, n=7); diabetes+training
group (DM+TRA, n=8); healthy+training+DCA group (TRA+DCA, n=8);
diabetes+training+DCA group (DM+TRA+DCA, n=8). Data are expressed
as mean ± SD.
*; P<0.05, **; P<0.01 compared with the control group, #; P<0.05, ##;
P<0.01 compared with the diabetic group, and DCA; Dichloroacetate.
Mouse-specific primer pairs used for quantitative reverse transcription polymerase chain reactionPhysical characteristics and plasma metabolites of groupsData are presented as mean ± SD. CONT; Control, DCA; Dichloroacetate, TRA; Training, and DM; Diabetes.The normalized gene expression of PDK4 in different groups.
From left to right, control group (CONT, n=7); diabetes group (DM, n=7);
control+DCA group (CONT+DCA, n=7); diabetes+DCA group (DM+DCA,
n=8); healthy group+training group (TRA, n=7); diabetes+training
group (DM+TRA, n=8); healthy+training+DCA group (TRA+DCA, n=8);
diabetes+training+DCA group (DM+TRA+DCA, n=8). Data are expressed
as mean ± SD.*; P<0.05, **; P<0.01 compared with the control group, #; P<0.05, ##;
P<0.01 compared with the diabetic group, and DCA; Dichloroacetate.The normalized gene expression of PGC-1α in different groups.
From left to right, control group (CONT, n=7); diabetes group (DM, n=7);
control+DCA group (CONT+DCA, n=7); diabetes+DCA group (DM+DCA,
n=8); healthy group+training group (TRA, n=7); diabetes+training
group (DM+TRA, n=8); healthy+training+DCA group (TRA+DCA, n=8);
diabetes+training+DCA group (DM+TRA+DCA, n=8). Data are expressed
as mean ± SD.*; P<0.05, **; P<0.01 compared with the control group, #; P<0.05, ##;
P<0.01 compared with the diabetic group, and DCA; Dichloroacetate.
ELISA results
The results of One-way ANOVA test showed a significant
difference in PGC-1α variables (F=72.33, df=7, P=0.001).
Also, results of Turkey’s test showed that the mean serum
levels of PGC-1α in the diabetic+endurance training
group, endurance training group and diabetes+endurance
training+DCA was significantly lower than the control
group (P≤0.01). But the mean serum levels of PGC-1α
in the diabetes group was significantly higher than the
control group (P≤0.01, Fig.3).
Fig.3
PGC-1α serum levels in different groups. From left to right,
control group (CONT, n=7); diabetes group (DM, n=7); control+DCA
group (CONT+DCA, n=7); diabetes+DCA group (DM+DCA, n=8); healthy
group+training group (TRA, n=7); diabetes+training group (DM+TRA, n=8);
healthy+training+DCA group (TRA+DCA, n=8); diabetes+training+DCA
group (DM+TRA+DCA, n=8). Data are expressed as mean ± SD.
**; P<0.01 compared with the control group, ##; P<0.01 compared with
the diabetic group, and DCA; Dichloroacetate.
PGC-1α serum levels in different groups. From left to right,
control group (CONT, n=7); diabetes group (DM, n=7); control+DCA
group (CONT+DCA, n=7); diabetes+DCA group (DM+DCA, n=8); healthy
group+training group (TRA, n=7); diabetes+training group (DM+TRA, n=8);
healthy+training+DCA group (TRA+DCA, n=8); diabetes+training+DCA
group (DM+TRA+DCA, n=8). Data are expressed as mean ± SD.**; P<0.01 compared with the control group, ##; P<0.01 compared with
the diabetic group, and DCA; Dichloroacetate.
Discussion
The purpose of this study was to investigate the effect
of PDK4 inhibition and endurance training on PGC-1α
serum levels and gene expression in the cardiac muscle of
diabetic rats. The most important results were that after the
endurance training, the expression of PDK4 and PGC-1α
increased in line with each other. But following inhibition
of PDK4 in the cardiac muscle using DCA, expression
of PGC-1α decreased in endurance training+DCA group,
the endurance training+diabetes+DCA group, and the
diabetes+DCA group. The results also showed that
PGC-1α serum levels in the diabetes group was higher
than the control group but PGC-1α serum levels in the
diabetic+endurance training group, endurance training
group and diabetes+endurance training+DCA were lower
than the control group. In the present study, DCA as a
halogenated carboxylic acid increased the activity of PDC
in the animal muscle (25) competitively by controlling
PDK2 and PDK4 (26). DCA is known as an activator of
PDC (27). Among the more important features of DCA is
its ability to lower the blood sugar level in diabetic rats
but not cause changes in the blood glucose levels of nondiabetic ones (28).On the other hand, PGC-1α plays a main role in
regulating cellular energy metabolism (29) and by
connecting to PPAR-γ1 and regulating gene expression,
it is linked to mitochondrial biogenesis. In addition,
it plays an important role in the metabolism of FA and
amino acids, secretion of insulin, insulin sensitivity, and
obesity. As has been reported, PGC-1α is involved in the
pathogenesis of type 2 diabetes mellitus (30). On the other
hand, the amount of PGC-1α in aerobic tissues, including
the myocardium, is high (31). Studies have shown that
the burden of work-induced physical activity on the
heart causes a change in the heart myosin heavy chain
(MHC) which is similar to what happens in hypertrophy
(32). Endurance activities reduce the level of ATP
and increase intracellular calcium which activates two
pathways, AMP-activated protein kinase (AMPK) and
calcium calmodulin/dependent protein kinase (CaMK)
(33). The activation of these two pathways leads to an
increase in the synthesis of PGC-1α which, by regulating
the expression of contractile and enzymatic proteins
that participate in the metabolic network, increases the
working capacity and also provides the energy needed
for increased heart activity. Endurance activity increases
the consumption of ATP and a decrease in the amount of
ATP activates the AMPK pathway. In this way, PGC-1α
gene expression in the heart tissue, that is affected by
endurance activity, is increased. One of the main actions
of the PGC-1α gene is mitochondrial biogenesis and thus
supply of oxidative enzymes so its increased expression
is consistent with increasing aerobic metabolism of the
heart (34). Matsuhashi et al. (35) showed in a study that
stable activation of the PDH enzyme through PDK4
inhibition by DCA causes excessive CoA production,
meaning increased oxidation in the citric acid cycle and
leads to histone acetylation which is one of the most
important epigenetic processes that occurs to regulate
the expression of genes. Inhibition of the PDK4 enzyme
following 6 weeks of DCA injection led to increased
PDK4 expression at the level of mRNA, which this is
a natural response to inhibiting this key enzyme in the
metabolism of aerobic energy. PGC-1α is involved in
the upregulation of the expression of genes regulating
FA oxidation in the heart and skeletal muscles (36).
Conclusion
The results of this study showed that endurance training
increased PDK4 and PGC-1α expressions in the cardiac
muscle of diabetic rats by inhibiting PDK4, PGC-1α
expression decreased in the cardiac muscle of diabetic
rats. Given that PGC-1α plays an important role in
mitochondrial biogenesis, it is likely that by controlling
PDK4 and subsequently controlling oxidation of FA (beta
oxidation) in heart tissue, oxidative stress in the heart
tissues of diabetic patients can be reduced and cardiac
efficiency increased.
Authors: Heinrich Taegtmeyer; Martin E Young; Gary D Lopaschuk; E Dale Abel; Henri Brunengraber; Victor Darley-Usmar; Christine Des Rosiers; Robert Gerszten; Jan F Glatz; Julian L Griffin; Robert J Gropler; Hermann-Georg Holzhuetter; Jorge R Kizer; E Douglas Lewandowski; Craig R Malloy; Stefan Neubauer; Linda R Peterson; Michael A Portman; Fabio A Recchia; Jennifer E Van Eyk; Thomas J Wang Journal: Circ Res Date: 2016-03-24 Impact factor: 17.367
Authors: Brian T O'Neill; Jaetaek Kim; Adam R Wende; Heather A Theobald; Joseph Tuinei; Jonathan Buchanan; Aili Guo; Vlad G Zaha; Don K Davis; John C Schell; Sihem Boudina; Benjamin Wayment; Sheldon E Litwin; Tetsuo Shioi; Seigo Izumo; Morris J Birnbaum; E Dale Abel Journal: Cell Metab Date: 2007-10 Impact factor: 27.287
Authors: Steven J Keteyian; Clinton A Brawner; Patrick D Savage; Jonathan K Ehrman; John Schairer; George Divine; Heather Aldred; Kristin Ophaug; Philip A Ades Journal: Am Heart J Date: 2008-05-22 Impact factor: 4.749