Antiaging phenotype in skeletal muscle after endurance exercise is associated with the oxidative phosphorylation pathway
Article Category: Original article
Published Online: Jan 31, 2017
Page range: 455 - 471
DOI: https://doi.org/10.5372/1905-7415.0904.415
Keywords
© 2015 Wasin Laohavinij, Apiwat Mutirangura
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Physical activity can be described as a situation in which the skeletal muscles are used for any purpose that results in an increase in energy expenditure compared with the resting state [1]. Long-term physical activity is associated with a reduction of the morbidity and mortality rates in humans [2, 3]. Numerous meta-analyses have shown that regular physical activity has a significant benefit by reducing the risk of mortality from all causes and from cardiovascular causes by 33% and 35% respectively in people without cardiovascular disease [4]; people with cardiovascular disease received similar benefits from regular physical activity [5, 6].
Exercise is a well-planned, structured, and repetitive activity that has the primary objective of improving or maintaining health and fitness, and is one of the most well-known and best-studied types of physical activity. Exercise is used in numerous guidelines for the primary and secondary prevention of numerous diseases, such as cardiovascular disease, diabetes, sarcopenia, dementia, osteoporosis, and some types of cancer [5-15]. It can be divided into 2 main types, aerobic and anaerobic, based on energy metabolism. Aerobic or endurance exercise is based on improving the cardiorespiratory system by maintaining the intensity level of activity in a low to moderate range for an extended period of time, using oxygen as a primary energy source [16]. By contrast with aerobic exercise, anaerobic exercise induces muscle contractions in a limited amount of time, stimulating a greater intensity of activity. The primary energy sources for this metabolism are the high-energy phosphates adenosine triphosphate (ATP) and creatine phosphate (CP), and anaerobic glycolysis, which generates lactic acid [16].
Aging is a degenerative physiological process influenced by many factors that can be categorized into 2 groups, intrinsic (genetic factors) and extrinsic (environmental and psychosocial factors), which affect numerous organ systems [1, 17]. A decrease in cardiorespiratory fitness, age-associated cognitive impairment, muscle and flexibility loss, reduction of stem-cell maintenance and proliferation, hormonal dysregulation, osteoporosis, depression, dementia, sarcopenia, diabetes, and cancers are some examples of physiological and pathological changes that occur during aging [5-13,17-20]. Although the consequences of aging have been identified in terms of pathologies and physiologies, the overall mechanisms leading to this event remain unknown. Some hypothesize that genomic instability, epigenetic alterations, errors in proteostasis, telomere shortening, and mitochondrial dysfunction are characteristics of aging [17-25]. Mitochondrial dysfunction is a significant process of aging because of the reduced biogenesis of new mitochondria and clearance of defective mitochondria, combined with the mutation and deletion of mitochondrial DNA (mtDNA) because of ineffective repair mechanisms. This results in the impairment of oxidative phosphorylation that is the primary energy-generating metabolic process in cells, the disruption of cellular signaling and interorganelle crosstalk at the interface between the outer mitochondrial membrane and the endoplasmic reticulum, and the initiation of inflammatory processes and activation of inflammasomes [17-22].
In an effort to slow the aging process, various interventions have been used. One of the most promising interventions is regular physical activity combined with a healthy diet and psychosocial well-being, as a holistic approach to maintaining a healthy lifestyle [1]. Although the physical appearances and benefits of each type of exercise have been shown in numerous sources, the similarities and differences in the molecular phenotypes among them remain unknown. The purpose of this study was to identify the transcriptome of the exercises that have the potential to delay aging using publicly available data obtained from online sources to screen for genes that are associated with aging and exercise. In particular, we extracted expression profiles from the Gene Expression Omnibus repository (GEO datasets;
Overall methodical framework is shown in
Figure 1
The overall methodological framework for the present study. Significant genes common to the exercise and aging groups were analyzed.
CU-DREAM = Connection Up-and Down-Regulation Expression Analysis of Microarrays, DAVID = Database for Annotation, Visualization and Integrated Discovery, GSE = Gene Expression Omnibus Series record, KEGG = Kyoto Encyclopedia of Genes and Genomes
The keywords “endurance”, “aerobic”, “physical activity”, and “skeletal muscle” were used to identify the expression profiles from microarray experiments that were related to the topics of interest and were published between July 2007 and April 2013. All the supplementary information, including series matrix files and related platforms, that was freely available from the Gene Expression Omnibus repository (GEO datasets;
The keywords “resistance” and “skeletal muscle” were used to identify the expression profiles from microarray experiments that were related to the topics of interest and were published between July 2007 and April 2013. All the supplementary information, including series matrix files and related platforms, that was freely available from the GEO datasets [26-28] was downloaded. Subsequently, all of the GSMs were extracted for template preparation. In the template preparation process, the “control” samples included the samples labeled “before resistance exercise” and “before power training”, while the “experimental” samples included the samples labeled “after resistance exercise” and “after power training.” The threshold parameter was set to a significance level of 0.01 for each regulation.
Four Gene Expression Omnibus Series (GSE) records (115 expression microarrays) were collected for 4 different types of exercises, and 5 GSE records (150 expression microarrays) were collected for the aging group. These data were used for analysis by the CU-DREAM software program. The program analyzed each exercise experimental group to identify which gene expression was significantly up- or downregulated in exercise populations compared with sedentary populations. We also used the program to analyze the aging experimental group to identify which genes were significantly up- or downregulated compared with the younger populations. The significant genes from each analysis were cross-referenced with each other and categorized into 4 groups: exercise–aging up–up, up–down, down–up, and down–down regulations. After obtaining cross-referenced genes from the CU-DREAM experiments, we categorized the genes into groups according to their exercise type and then mapped the significant pathways from the cross-referenced genes in each GSE record using DAVID and selecting the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways informatics resources. We also analyzed the molecular and cellular phenotypes and specific functions of each gene using GeneCards.
The keywords “aging” and “skeletal muscle” were used to identify the expression profiles from microarray experiments published between August 2002 and April 2013 that were related to the topics of interest. All the supplementary information, including series matrix files and related platforms, that were freely available from the Gene Expression Omnibus repository (GEO datasets;
First, the total RNA levels in the experimental and control samples from the exercise and aging groups were evaluated. Using the prepared templates from the microarrays, series matrix files and platforms, a Student
The results of the correlations between the exercise and the aging experiments included the number of genes in groups A through D, and the ORs, 95% CIs, and
Datasets from GEO were screened for expression in exercise and aging microarrays published through April 2013. The exercise microarrays from GEO included GSE8479 for resistance exercise [11], GSE9103 for endurance exercise [32], GSE16907 for power training [33], and GSE20319 for physical activity [34]; the aging microarrays from GEO included GSE80 [35], GSE1428 [36], GSE8479 [11], GSE9103 [32], and GSE38718 [37]. These datasets were chosen because endurance exercise and physical activity represent aerobic exercises, by contrast with resistance exercise and power training, which represent anaerobic exercises. Details of the available demographic data for these GSEs are shown in
Demographic data for the exercise and aging groups
| Exercise group GSE Type | 8479 Experimental results were measured before and after training from the same individuals for a specific duration. | 9103 Experimental results were measured from 2 defined sample groups (active/inactive). | 16907 Experimental results were measured before and after training from the same individuals for a specific duration. | 20319 Experimental results were measured from 2 defined sample groups (active/inactive). | Aging group GSE | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Duration of exercise | 6 months | 4 years | 12 months | 32 years | 80 | 1428 | 8479 | 9103 | 38718 | |
| Experimental group Experimental groups are the active or after-training groups in exercise GSEs and are the old age groups in the aging GSEs. | Samples (n) | 14 | 20 | 8 | 10 | 19 | 12 | 25 | 10 | 8 |
| Age (y) | 69.6 + 9.4 | 19-76 | 50–57 | 50–74 | 68.6 ± 8.4 | 70–80 | 70.5±13.5 | 65.1 ± 7.3 | 65-76 | |
| Control group Control groups are the sedentary or before-training groups in exercise GSEs and are the young age groups in the aging GSEs. | Samples (n) | 25 | 20 | 8 | 10 | 16 | 10 | 26 | 10 | 14 |
| Age (y) | 70.5+13.5 | 18–72 | 50–57 | 50–74 | 24.5 ± 6.5 | 19-25 | 21.2 ± 6.8 | 22.9 ± 4.1 | 19-28 | |
| Sex | Mixed | Mixed | Female | Mixed | Male | Male | Mixed | Mixed | Mixed | |
| Muscle biopsy | Vastus | Vastus | Vastus | Vastus | Vastus | Vastus | Vastus | Vastus | Biceps | |
| lateralis | lateralis | lateralis | lateralis | lateralis | lateralis | lateralis | lateralis | brachii | ||
| Extracted molecule | Total RNA | Total RNA | Total RNA | Total RNA | Total RNA | Total RNA | Total RNA | Total RNA | Total RNA | |
| Organization name | Buck Institute for Research on Aging USA | Mayo Clinic and Foundation USA | University of Jyväskylä Finland | University of Jyväskylä Finland | University of Rochester USA | Boston University USA | Buck Institute for Research on Aging USA | Mayo Clinic and Foundation USA | University of Michigan USA | |
In this study, we screened numerous microarray data from exercise and aging datasets provided in the GEO database. The data were analyzed using CU-DREAM to identify correlations between each type of exercise and the aging process. The analysis found 15 experiments with significant (
CU-DREAM comparing upregulation in exercise and downregulation in aging
| Aging group | Exercise group | 8479 | 9103 | 16907 | 20319 |
|---|---|---|---|---|---|
| GSEs | GSEs (types) | (Resistance) | (Endurance) | (Power training) | (Physically active) |
| 80 | 6.73 × 10–2 | 3.78 × 10–4 | 6.58 × 10–1 | 1.32 × 10–1 | |
| OR | 0.40 | 2.91 | 0 | 4.14 | |
| 95% CI | 0.15–1.10 | 1.57–5.38 | – | 0.56–30.80 | |
| 1428 | 5.29 × 10–1 | 9.73 × 10–15 | 3.54 × 10–2 | 8.29 × 10–1 | |
| OR | 0.93 | 2.30 | 2.65 | 0.86 | |
| 95% CI | 0.73–1.17 | 1.85–2.85 | 1.03–6.79 | 0.21–3.54 | |
| 8479 | 5.07 × 10–24 | 1.34 × 10–22 | 1.20 × 10–1 | 8.31 × 10–2 | |
| OR | 0.16 | 2.65 | 0 | 2.22 | |
| 95% CI | 0.11–0.24 | 2.16–3.24 | – | 0.88–5.60 | |
| 9103 | 4.13 × 10–2 | 2.76 × 10–12 | 5.90 × 10–1 | 5.97 × 10–1 | |
| OR | 0.60 | 3.17 | 0.58 | 1.70 | |
| 95% CI | 0.36–0.99 | 2.26–4.47 | 0.08–4.20 | 0.23–12.36 | |
| 38718 | 2.85 × 10–18 | 2.35 × 10–9 | 4.65 × 10–1 | 6.82 × 10–2 | |
| OR | 0.47 | 1.58 | 0.68 | 1.88 | |
| 95% CI | 0.40–0.56 | 1.36–1.83 | 0.25–1.90 | 0.94–3.74 |
* Significant experiments noted in bold, OR = odds ratio, CI = confidence interval
CU-DREAM comparing downregulation in exercise and upregulation in aging
| Aging Group | Exercise group | 8479 | 9103 | 16907 | 20319 |
|---|---|---|---|---|---|
| GSEs | GSEs (types) | (Resistance) | (Endurance) | (Power Training) | (Physically active) |
| 80 | 1.81 × 10–3 | 1.25 × 10–2 | 7.93 × 10–1 | 6.39 × 10–1 | |
| OR | 2.55 | 2.56 | 0.00 | 0.00 | |
| 95% CI | 1.39–4.68 | 1.19–5.48 | – | – | |
| 1428 | 9.35 × 10–1 | 3.03 × 10–1 | 9.34 × 10–1 | 8.91 × 10–1 | |
| OR | 0.93 | 2.93 | 0.00 | 0.00 | |
| 95% CI | 0.16–5.56 | 0.34–25.15 | – | – | |
| 8479 | 1.81 × 10–103 | 7.43 × 10–2 | 9.05 × 10–1 | 7.52 × 10–1 | |
| OR | 5.65 | 1.30 | 0.89 | 0.80 | |
| 95% CI | 4.74–6.73 | 0.97–1.74 | 0.12- 6.53 | 0.19–3.27 | |
| 9103 | 9.14 × 10–-61 | 3.04 × 10–51 | 3.98 × 10–1 | 9.03 × 10–1 | |
| OR | 4.08 | 4.44 | 1.43 | 0.92 | |
| 95% CI | 3.41–4.89 | 3.59–5.48 | 0.62-3.26 | 0.22–3.76 | |
| 38718 | 9.22 × 10–9 | 8.59 × 10–13 | 2.32 × 10–1 | 3.09 × 10–1 | |
| OR | 1.50 | 2.14 | 0.00 | 1.60 | |
| 95% CI | 1.31–1.72 | 1.73–2.64 | – | 0.64–4.01 |
* Significant experiments noted in bold, OR = odds ratio, CI = confidence interval
CU-DREAM comparing downregulation in exercise and downregulation in aging
| Aging group | Exercise group | 8479 | 9103 | 16907 | 20319 |
|---|---|---|---|---|---|
| GSEs | GSEs (types) | (Resistance) | (Endurance) | (Power Training) | (Physically active) |
| 80 | 1.47 × 10–1 | 4.72 × 10–1 | 7.60 × 10–1 | 5.84 × 10–1 | |
| OR | 1.44 | 1.36 | 0.00 | 0.00 | |
| 95% CI | 0.88–2.38 | 0.58–3.17 | – | – | |
| 1428 | 1.81 × 10–1 | 9.97 × 10–1 | 3.44 × 10–1 | 8.72 × 10–1 | |
| OR | 0.89 | 1.00 | 0.00 | 1.10 | |
| 95% CI | 0.76–1.05 | 0.73–1.37 | – | 0.34–3.54 | |
| 8479 | 1.24 × 10–67 | 9.92 × 10–1 | 8.39 × 10–1 | 9.69 × 10–2 | |
| OR | 3.63 | 1.00 | 0.81 | 0.00 | |
| 95% CI | 3.11–4.24 | 0.73–1.37 | 0.11–6.00 | – | |
| 9103 | 1.72 × 10–5 | 9.47 × 10–1 | 1.11 × 10–1 | 4.09 × 10–1 | |
| OR | 1.93 | 1.02 | 2.48 | 0.00 | |
| 95% CI | 1.42–2.61 | 0.55–1.88 | 0.78–7.89 | – | |
| 38718 | 5.21 × 10–54 | 2.10 × 10–7 | 2.50 × 10–1 | 8.04 × 10–1 | |
| OR | 2.17 | 1.59 | 0.33 | 0.90 | |
| 95% CI | 1.97–2.40 | 1.33–1.90 | 0.04–2.43 | 0.39–2.09 |
* Significant experiments are noted in bold, OR = odds ratio, CI = confidence interval
Lists of in common genes from significant CU-DREAM results comparing upregulation in endurance exercise and downregulation in aging
| In common | 5 (n= 2) | 4 (n= 6) | 3 (n= 23) | 2 (n= 66) |
|---|---|---|---|---|
| Genes |
Lists of in common genes from significant CU-DREAM results comparing downregulation in resistance exercise and upregulation in aging
| In common | 4 (n= 1) | 3 (n= 12) | 2 (n= 134 |
|---|---|---|---|
| Genes |
Lists of in common genes from significant CU-DREAM results comparing downregulation in resistance exercise and downregulation in aging
| In common | 3 (n= 8) | 2 (n= 165) |
|---|---|---|
| Genes |
Figure 2
Connection Up- and Down-Regulation Expression Analysis of Microarray (CU-DREAM) results: inverse correlation between exercise and aging.
Figure 3
Connection Up-and Down-Regulation Expression Analysis of Microarray (CU-DREAM) results: correlation between exercise and aging.
Because CU-DREAM showed only all experiments (5/5) comparing endurance exercise and aging to have a significant up–down correlation, further studies of the molecular pathways associated with the antiaging processes associated with endurance exercise were conducted using the DAVID/KEGG database. In the experiments, 6 molecular pathways were upregulated (
DAVID/KEGG pathway results for endurance exercise.
| Significant molecular pathways | No. of GSEs in common | No. of genes | Lists of genes associated with the pathways | |
|---|---|---|---|---|
| Oxidative phosphorylation | 8.8 × 10–44 –1.6 × 10–2 | 5/5 | 43 | |
| Cardiac muscle contraction | 3.8 × 10–10 –2.6 × 10–5 | 3/5 | 14 | |
| Citrate cycle (TCA cycle) Significant in GSE 8479. | 3.6 × 10–5 | 1/5 | 7 | |
| Propanoate metabolism Significant in GSE 8479. | 8.3 × 10–3 | 1/5 | 5 | |
| Pyruvate metabolism Significant in GSE 8479. | 1.7 × 10–2 | 1/5 | 5 | |
| Arginine and proline metabolism Significant in GSE 8479. | 4.1 × 10–2 | 1/5 | 5 |
a Significant in GSE 80, 1428, and 8479.
Oxidative phosphorylation, which had 43 genes coding for pathway components upregulated after endurance exercise, was the pathway most significantly associated with downregulation of genes coding for aging phenotypes, found in 5/5 GSEs. The
We reviewed the genes significant in at least 3 of the 5 GSEs in the endurance-exercise–aging correlation from CU-DREAM for their molecular functions and their associations with molecular and cellular phenotypes using GeneCards. We divided the lists of significant genes into 2 groups, the genes associated with the DAVID/KEGG oxidative phosphorylation (OXPHOS) pathway (
Molecular functions and phenotypes of genes significant in endurance exercise from the Kyoto Encyclopedia of Genes and Genomes oxidative phosphorylation pathway
| Gene | Number of GSEs in common | Molecular function | Molecular and cellular phenotypes | |
|---|---|---|---|---|
| 5/5 | 6.80 × 10–5 to 8.86 × 10–3 | ATP synthase γ-subunit | Catalyzes AT P synthesis, mainly expressed in the heart | |
| 4/5 | 4.12 × 10–7 to 2.06 × 10–3 | ATP synthase lipid-binding Catalyzes ATP synthesis protein | ||
| 4/5 | 6.58 × 10–5 to 3.57 × 10–3 | ATP synthase-coupling factor | Catalyzes AT P synthesis | |
| 4/5 | 8.81 × 10–5 to 6.36 × 10–3 | Cytochrome C oxidase subunit | Terminal oxidase in the mitochondrial electron transport | |
| Accepts electrons from the Rieske | ||||
| 4/5 | 1.34 × 10–5 to 6.77 × 10–3 | Cytochrome C | protein and transfers electrons to cytochrome c in the mitochondrial | |
| respiratory chain | ||||
| 4/5 | 9.80 × 10–6 to 4.15 × 10–3 | Ubiquinol-Cytochrome C reductase complex subunit | Redox-linked proton pumping | |
| 3/5 | 1.69 × 10–4 to 9.69 × 10–4 | Cytochrome C oxidase subunit | Catalyzes the electron transfer from reduced cytochrome c to oxygen | |
| 3/5 | 2.20 × 10–4 to 9.60 × 10–3 | Cytochrome C oxidase subunit | Catalyzes the electron transfer from reduced cytochrome c to oxygen | |
| 3/5 | 7.89 × 10–4 to 8.57 × 10–3 | α-Subcomplex subunit | Transfer of electrons from NADH to the respiratory chain | |
| 3/5 | 4.95 × 10–5 to 5.58 × 10–3 | β-Subcomplex subunit | Transfer of electrons from NADH to the respiratory chain | |
| 3/5 | 4.35 × 10–3 to 6.58 × 10–3 | β-Subcomplex Subunit | Transfer of electrons from NADH to the respiratory chain | |
| 3/5 | 4.03 × 10–7 to 4.14 × 10–3 | β-Subcomplex Subunit | Transfer of electrons from NADH to the respiratory chain | |
| 3/5 | 6.28 × 10–6 to 2.61 × 10–3 | Ubiquinol-Cytochrome C reductase complex subunit | Generates an electrochemical potential coupled to ATP synthesis | |
ATP = adenosine triphosphate, NADH = reduced form of nicotinamide adenine dinucleotide
Molecular functions and phenotypes of significant remaining genes in endurance exercise
| Gene | Number of GSEs in common | Molecular function | Molecular and cellular phenotypes | |
|---|---|---|---|---|
| 5/5 | 6.96 × 10–5 to 4.85 × 10–3 | Cytochrome C | Electron carrier protein, important apoptotic role | |
| 4/5 | 3.22 × 10–5 to 7.78 × 10–3 | Glutathione S-transferase subunit | Cellular detoxification | |
| 3/5 | 1.34 × 10–6 to 9.60 × 10–3 | Helix-loop-helix protein | Transcriptional repressor | |
| 3/5 | 7.06 × 10–5 to 4.83 × 10–3 | Mitochondrial proteolipid | Chromosome 14 open reading frame Reversibly catalyzes the transfer of | |
| 3/5 | 7.66 × 10–9 to 5.91 × 10–3 | Mitochondrial creatine kinase | phosphate between ATP and phosphagens | |
| 3/5 | 7.39 × 10–5 to 7.27 × 10–3 | COQ3 methyltransferase | Coenzyme Q biosynthesis Prevents premature joining of the 40S | |
| 3/5 | 1.34 × 10–4 to 5.21 × 10–3 | Translation initiation factor | and 60S ribosomal subunits prior to initiation | |
| 3/5 | 5.26 × 10–6 to 6.07 × 10–3 | Fatty acid-binding protein | Amino acid metabolism, facilitates cellular uptake of long-chain free fatty acids Key component of the small ribosomal subunit | |
| 3/5 | 5.51 × 10–4 to 3.48 × 10–3 | Mitochondrial ribosomal protein | ||
| 3/5 | 2.64 × 10–4 to 3.45 × 10–3 | Neural precursor cell protein | Mitosis progression, promotes the nucleation of microtubules from the spindle | |
| 3/5 | 2.16 × 10–4 to 2.53 × 10–3 | Phosphodiesterase isozyme | Regulating the cellular concentration of cAMP | |
| 3/5 | 3.69 × 10–5 to 6.88 × 10–3 | Mitochondrial cyclophilin | Antiapoptotic activity | |
| 3/5 | 4.61 × 10–4 to 9.86 × 10–3 | Thiol-specific antioxidant | Eliminating peroxides generated during metabolism | |
| 3/5 | 2.03 × 10–4 to 4.98 × 10–3 | Regulatory protein | Regulation of transcription and apoptosis | |
| 3/5 | 6.38 × 10–5 to 9.09 × 10–3 | Solute carrier | Catalyzes the movement of monocarboxylates across the plasma membrane | |
| 3/5 | 3.49 × 10–5 to 9.75 × 10–4 | Solute carrier | Mediates cotransport of glutamine and sodium ions | |
| 3/5 | 1.04 × 10–5 to 1.97 × 10–3 | Sialyltransferase | Synthesis of gangliosides | |
| 3/5 | 3.33 × 10–5 to 9.54 × 10–3 | Triosephosphate isomerase | Catalyzes the isomerization of G3P and DHAP in glycolysis and gluconeogenesis |
ATP = adenosine triphosphate, COQ3 = 3-demethylubiquinone-9,3-O, DHAP = Dihydroxyacetone phosphate, G3P = glyceraldehyde-3-phosphate dehydrogenase, NADH = nicotinamide adenine dinucleotide reduced form
There were 13 genes significant in at least 3/5 GSEs from the 43 endurance exercise genes that were involved in the OXPHOS pathway (
Among the remaining genes, 18 were present in at least 3/5 GSEs (
The main objective of this study was to find correlative evidence between types of exercise and their ability to at least partially delay the aging process based on changes in the cellular and molecular phenotypes caused by specific molecular pathways and genes. In this study, we evaluated the correlations between 4 exercise groups and 5 aging groups that contained hundreds of microarray experiments obtained from the GEO dataset. Benjamini FDR analysis was used to correct for the false positives as a result of chance from multiple comparisons in the mapping pathways.
The CU-DREAM program was designed to find the genes that correlate between 2 specific subjects and their gene distributions. If the distributions among these genes are higher than normal randomization, we can conclude that there was a relationship between the 2 specific subjects we studied. Furthermore, this program showed the significant advantage of a highly precise statistical significance by identifying the whole genome, which provided reliable results. However, there is a general limitation in using this program because it usually provides less significant results, lower ORs (in the case of OR>1) and higher
This may result in low significance or no significant difference between the experiments. Furthermore, the heterogeneities of tissues between the GSEs can interrupt the results because some genes may be ignored or marked as nonassociated genes. In conclusion, the CU-DREAM program can be a reliable source for performing statistically significant tests, but the results must be interpreted with caution when the nonsignificant test results have been reported.
In the present study, we found that endurance exercise could potentially delay aging by upregulating numerous sets of genes, which can be mapped using the DAVID/KEGG database into the OXPHOS pathway, a primary energy metabolism process of cells. Numerous studies about the molecular effects of endurance exercise on antiaging demonstrated that endurance exercise produced higher levels of ATP production capacities, mtDNA abundance, protein expression of mitochondrial biogenesis, and the upregulation of mitochondrial and oxidative metabolism pathways than sedentary activity [9, 25, 38, 39]. Increasing OXPHOS can be interpreted as an increase in mitochondrial biogenesis, improved ATP production capacity and an increase in numerous proteins associated with the metabolic process as a result of the chronic adaptations of a cell to endurance exercise. These molecular changes may be the primary mechanism for slowing the aging process.
Furthermore, we trawled the gene lists associated with endurance exercise and aging to identify the potential antiaging genes and provide a better understanding of the genetic regulation during OXPHOS. We found that the majority of genes were significantly involved in the OXPHOS pathway, directly and indirectly. For example,
Although numerous significant genes associated directly with OXPHOS had been indicated, numerous genes in the non-OXPHOS group were indirectly involved in OXPHOS, for example, genes associated with antioxidants. Antioxidant enzymes and heat shock proteins are significantly increased in trained subjects as an adaptation to the increase in reactive oxygen species, which are the by-products of the increased oxygen consumption occurring during exercise [41]. This finding is consistent with our present findings. We found that numerous genes, such as
Our study indicates that there is an association between certain types of exercise and aging. Endurance exercise showed the strongest association with downregulation of expression of genes related to the aging phenotype. Increased activity of the oxidative phosphorylation pathway, combined with specific molecular and cellular phenotypes, such as ATP synthesis, electron transfer in the mitochondrial respiratory chain, and cellular detoxification, resulted from the chronic adaptation to endurance exercise. This has the potential to slow mitochondrial dysfunction, one of the hallmarks of aging, by increasing mitochondrial biogenesis and increasing ATP production capacity.