Oxidative Medicine and Cellular Longevity

Oxidative Medicine and Cellular Longevity / 2019 / Article
Special Issue

Applications of Antioxidants in Metabolic Disorders and Degenerative Diseases: Mechanistic Approach

View this Special Issue

Research Article | Open Access

Volume 2019 |Article ID 9141343 | https://doi.org/10.1155/2019/9141343

Jing Jye Lim, Wan Ngah Wan Zurinah, Vincent Mouly, Abdul Karim Norwahidah, "Tocotrienol-Rich Fraction (TRF) Treatment Promotes Proliferation Capacity of Stress-Induced Premature Senescence Myoblasts and Modulates the Renewal of Satellite Cells: Microarray Analysis", Oxidative Medicine and Cellular Longevity, vol. 2019, Article ID 9141343, 19 pages, 2019. https://doi.org/10.1155/2019/9141343

Tocotrienol-Rich Fraction (TRF) Treatment Promotes Proliferation Capacity of Stress-Induced Premature Senescence Myoblasts and Modulates the Renewal of Satellite Cells: Microarray Analysis

Academic Editor: Mohamed M. Abdel-Daim
Received15 Aug 2018
Revised18 Oct 2018
Accepted12 Nov 2018
Published10 Jan 2019


Human skeletal muscle is a vital organ involved in movement and force generation. It suffers from deterioration in mass, strength, and regenerative capacity in sarcopenia. Skeletal muscle satellite cells are involved in the regeneration process in response to muscle loss. Tocotrienol, an isomer of vitamin E, was reported to have a protective effect on cellular aging. This research is aimed at determining the modulation of tocotrienol-rich fraction (TRF) on the gene expressions of stress-induced premature senescence (SIPS) human skeletal muscle myoblasts (CHQ5B). CHQ5B cells were divided into three groups, i.e., untreated young control, SIPS control (treated with 1 mM hydrogen peroxide), and TRF-posttreated groups (24 hours of 50 μg/mL TRF treatment after SIPS induction). The differential gene expressions were assessed using microarray, GSEA, and KEGG pathway analysis. Results showed that TRF treatment significantly regulated the gene expressions, i.e., p53 (RRM2B, SESN1), ErbB (EREG, SHC1, and SHC3), and FoxO (MSTN, SMAD3) signalling pathways in the SIPS myoblasts compared to the SIPS control group (). TRF treatment modulated the proliferation capacity of SIPS myoblasts through regulation of ErbB (upregulation of expression of EREG, SHC1, and SHC3) and FoxO (downregulation of expression of MSTN and SMAD3) and maintaining the renewal of satellite cells through p53 signalling (upregulation of RRM2B and SESN1), MRF, cell cycle, and Wnt signalling pathways.

1. Introduction

Skeletal muscle is one of the largest organs in the body and contributed to 45-55% of the total body weight [1]. However, skeletal muscle cells are not able to self-replenish as it is a terminally differentiated cells. Thus, skeletal muscle cells require a population of resident adult stem cells, satellite cells for maintenance and repair [2]. Satellite cells are typically mitotically quiescent in resting muscle and will be activated to prepare for cell cycle entry upon stimulation and during muscle injury. A subset of satellite cells will be self-renewed to maintain the satellite cell pool and regenerate a rapidly proliferating transit-amplifying myoblast population.

Regeneration of skeletal muscle cells deteriorated with the aging process. This muscle degeneration process due to aging is termed sarcopenia [3]. Muscle degeneration started with 0.5–1% after 30 years of age and the rate increases as age reaches 65 years [4]. The mechanism of this degeneration is still yet to be deciphered clearly, but it can involve oxidative stress [5]. Muscle atrophy is reported to be stimulated by the PI3K/Akt and NF-κB signalling pathways [6]. Activation of PI3K/Akt signalling regulates the skeletal muscle mass and metabolism in skeletal muscle [7]. On the other hand, inhibition of this signalling pathway would inhibit the FoxO protein and upregulate the atrophy-related gene (atrogin), such as atrogin1/MAFbx1 and MuRF [7, 8]. Braun and Gautel proposed that NF-κB also would involve in the regulation of atrogin and regulate the catabolism and anabolism of muscle protein [7].

Myostatin, a member of the TGF superfamily, was reported as a muscle atrophy inducer by activating the transcription factor SMAD3 which leads to atrogin-1 expression and inhibition of Akt/mTOR signalling and protein synthesis. Moreover, SMAD3 is also involved in the inhibition of peroxisome proliferator-activated receptor-coactivator-1 (PGC1) promoter activity and increased FoxO-mediated signalling [9]. Besides the mentioned FoxO signalling pathway and the expression of atrogin in muscle atrophy, Brack et al. suggested a different signalling pathway in muscle aging, which is the activation of the Wnt/β-catenin signalling pathway that leads to increased muscle fibrosis [10].

Cell senescence is categorised into three major mechanisms, i.e., replicative senescence, oncogene-induced senescence, and stress-induced premature senescence (SIPS) [11]. SIPS cells and replicative senescence cells are similar in molecular action and morphology, such as flat and larger cells, increased activities of senescence-associated β-galactosidase, and cell cycle arrest. However, SIPS was not affected by the length of telomere [1214]. Hydrogen peroxide (H2O2) is a common stress mediator in the SIPS model as it induced the cells to have senescence morphology alike with the replicative senescence cells [11, 12]. Generation of H2O2 by the mitochondria or external sources from the cells leads to the damage of mitochondrial components and thus initiated the degeneration process [15].

Since the degeneration of skeletal muscle was closely related to oxidative stress, it was suggested that reestablishment of the redox balance would be beneficial in the amelioration of age-related degeneration in skeletal muscle [16]. Vitamin E, especially tocotrienol, has been widely reported for its antioxidant effects in preventing aging [1719]. Supplementation of vitamin E (α-tocopherol) together with vitamin C reduced oxidative stress and increased the activities of antioxidant enzymes in the rat skeletal muscle [20].

Various approaches, including pharmacological interventions, nutrition, and physical activities, have been reported to improve the aging conditions of the skeletal muscle. However, to date there is neither supplementation nor intervention that works effectively with minimum side effects [3]. Howard et al. reported that tocopherol promoted the repair of plasma membrane in mouse myocytes [21]. However, these findings were focused on the effects of tocopherol, but not tocotrienol, on skeletal muscle. Previously, our study revealed that the tocotrienol-rich fraction (TRF) has reduced the senescence morphology and improved the proliferation capacity towards stress-induced premature senescence cells [22]. Hence, we would like to identify and elucidate the mechanism involved in the regenerative action of TRF on stress-induced premature skeletal muscle cells.

2. Materials and Methods

2.1. CHQ5B Cell Culture

Human satellite cells were isolated from a biopsy of a 5-day-old infant quadriceps muscle and were kindly provided by Dr. Vincent Mouly from UMRS 787, Institut de Myologie, INSERM, Université Pierre et Marie Curie, Paris, France. Upon isolation, the satellite cells proliferated in culture as myoblasts (known as CHQ5B cells) and were considered to be at 1 mean population doubling (MPD). CHQ5B cells were cultivated in a growth medium at 37°C in a humid atmosphere containing 5% carbon dioxide (CO2) as described previously [22].

2.2. Treatment Protocols

The Tocotrienol-rich fraction (TRF) was purchased from Sime Darby Sdn. Bhd., Selangor, Malaysia (TRF Gold Tri E 70). TRF consists of α-tocotrienol (26.67%), β-tocotrienol (4.29%), γ-tocotrienol (32.60%), δ-tocotrienol (15.53%), and α-tocopherol (20.81%) [22]. The CHQ5B cells (PD 29 ± 3) were incubated with different treatments, i.e., untreated young control, stress-induced premature senescence (SIPS), and TRF-treated group (SIPS myoblasts posttreated with TRF). Untreated young control cells were cultured without any treatment with ROS inducer or TRF. The SIPS model was established by exposure of CHQ5B cells to the stressor, 1 mM H2O2 diluted in growth medium for 30 minutes [22, 23]. In the TRF-treated group, the CHQ5B cells were exposed to the stressor at 1 mM H2O2 for 30 minutes followed by incubation in 50 μg/mL TRF (Sime Darby Bioganic Sdn Bhd) for 24 hours [22]. This concentration and duration of TRF treatment were in line with a previous study which showed that SIPS cells posttreated with 24 hours 50 μg/mL TRF were able to increase the proliferation capacity of myoblast cells.

2.3. Total RNA Extraction and Purification

Total RNA from CHQ5B cells in different treatment groups was extracted using TRI Reagent (Molecular Research Center, Cincinnati, OH, USA) according to the manufacturer’s instruction. Polyacryl Carrier (Molecular Research Center) was added in each extraction to precipitate the total RNA. The extracted total RNA pellet was then washed with 75% ethanol and air-dried. The extracted total RNA was dissolved in RNase and DNase-free distilled water and purified by using the RNeasy® Mini Kit (Qiagen, USA) according to the manufacturer’s instruction. Total RNA was then stored at −80°C immediately after extraction and purification. RNA concentration and purity of the extracted RNA were determined by NanoDrop (Thermo Scientific, USA). The quality of RNA was assessed by an Agilent 2100 bioanalyzer (Agilent Technologies, USA). The high quality of RNA, i.e., RNA integrity number (RIN) ranging from 7 to 10 and absorbance ratio of A260 to A280 ranging from 1.5 to 2.1, was utilized for microarray analysis (Figure S01, Supplementary Materials).

2.4. Gene Expression Microarray Profiling

The isolated RNA was amplified and labelled using Affymetrix GeneChip 3’ IVT Express and Affymetrix GeneChip Hybridisation, Wash, and Stain Kit (Affymetrix, Santa Clara, CA, USA) according to the manufacturer’s protocol. The RNA was then hybridised to the Affymetrix GeneChip PrimeView Human Gene Expression Array cartridge, washed, and scanned according to the manufacturer’s protocol. The arrays from three samples each of untreated young control, SIPS control, and TRF-treated cells were scanned and processed by using the AGCC Scan Control (Affymetrix).

2.5. Gene Expression Microarray Data Analysis and Statistics

The raw CEL data files from microarray profiling were imported into the Partek Genomics Suite (v. 6.6; Partek, St. Louis, MO) for analysis, and two-way analysis of variance (2-way ANOVA) was applied with a fold change of 1.5 for the selection of differentially expressed genes at a significance level of . The differentially expressed gene lists were further correlated for their relevant biological function and reaction pathway by analysing the GSEA (Gene Set Enrichment Analysis) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the Partek Genomic Suite. A significance level ofin the GSEA analysis to identify the significant biological process involved was observed, whereas an enrichment score ofin the KEGG pathway to identify the significant pathway was observed.

2.6. Quantitative Real-Time PCR (qPCR)

The microarray data was validated by using qualitative qPCR. Genes for validation, i.e., GDF15, EREG, RRM2B, SHC3, SHC1, SESN1, MSTN, MYOD1, and SMAD3, were chosen from pathway analysis. By using 2 μL total RNA as template and iScript Reverse Transcription Supermix (Bio-Rad, USA), the cDNA is generated from RNA. The reactions were carried out as follows: priming for 5 minutes at 25°C, then reverse-transcription for 30 minutes at 42°C and inactivation of the reverse-transcription for 5 min at 85°C.

Primer sequences for GDF15, EREG, RRM2B, SHC3, SHC1, SESN1, MSTN, MYOD1, and SMAD3 are shown in Table 1(a). qPCR was carried out by using 1 μL cDNA as template, 1 μL of forward and reverse primers for genes of interest, and SSoAdvanced SYBR Green Supermix (Bio-Rad, USA). All reactions were run in duplicate using Real-Time PCR iQ5 (Bio-Rad, USA). The thermal cycling profiles are presented in Table 1(b). The melt curve analysis of each pair of primers and agarose gel electrophoresis that was performed on the PCR products was used to determine the primer specificity (Figure S02, Supplementary Materials). The gene expression level of each targeted gene was normalized to that of glyceraldehyde 3-phosphate dehydrogenase (GAPDH). It was presented as relative expression value (REV) by using the 2−ΔΔCt method of relative quantification and the following equation:


Accession numberGenePrimer typePrimer sequences (5–3)












Cycling stepTemperatureTimeNumber of cycles

Enzyme activation/initial DNA denaturation95°C30 seconds1
Denaturation95°C5 seconds40
Annealing/extension60°C30 seconds
Melt curve65°C to 95°C (in 0.5°C increments)5 seconds1

By referring to each REV value of the targeted gene, the fold change (FC) can be calculated by using the following equation:

2.7. Statistical Analysis

Microarray data was analysed by using Partek Genomic Suite (v. 6.6; Partek, St. Louis, MO), and the differentially expressed gene lists were filtered based on a fold change of 1.5 and a significance level of by using two-way analysis of variance (2-way ANOVA). The relevant biological function and reaction pathway was identified based on GSEA analysis at a significance level of and KEGG analysis at an enrichment score by using the Partek Genomic Suite.

The REV data in qPCR are presented as error of the mean (SEM). Statistical analysis was performed with the software IBM SPSS Statistics (version 20). Independent sample test was used to determine the significant differences in between the SIPS control and TRF-treated groups. For all of the tests, was considered statistically significant.

3. Results

3.1. Quality Control Assessment of the Samples and the Hierarchical Clustering of Significantly Expressed Genes

Principal component analysis (PCA) is a multivariate statistic which allows viewing of separation between groups of replicates. The untreated young control, SIPS, and TRF-posttreated groups were well separated (Figure 1(a)). Hierarchical cluster analysis was performed to organize genes into cluster based on their similarities of expression. The upregulation of gene expression was indicated in red, whereas the downregulation of gene expression was indicated in blue. Clustering analysis was able to distinguish gene expressions between untreated young control and SIPS groups as well as between TRF-posttreated and SIPS groups (Figure 1(b)).

3.2. Identification of Gene Expression Changes Associated with SIPS Myoblasts

The gene expression analysis using Partek Genomic Suite was performed to identify changes in the SIPS myoblasts. Statistical analysis of two-way analysis of variance (2-way ANOVA) revealed that a total of 41 genes were significantly regulated in SIPS myoblasts as compared to untreated young control cells (fold or fold ; ); i.e., 11 genes were upregulated and 30 genes were downregulated (Figure 1(c)). The complete list of 41 differentially expressed genes is available in Table S01, Supplementary Materials.

3.3. Identification of Gene Expression Changes Associated with TRF-Post-treatment on SIPS Myoblasts

The gene expression analysis using Partek Genomic Suite was performed to identify changes in TRF-posttreated SIPS myoblasts. Statistical analysis of two-way analysis of variance (2-way ANOVA) revealed that a total of 905 genes were significantly regulated in TRF-posttreated SIPS myoblasts as compared to the SIPS group (fold or fold ; ); i.e., 378 genes were upregulated and 527 genes were downregulated (Figure 1(c)). The complete list of 905 differentially expressed genes is available in Table S02, Supporting Materials. At present, only selected differentially expressed genes including growth differentiation factor 15 (GDF15), epiregulin (EREG), ribonucleotide reductase M2B (RRM2B), SHC (Src homology 2 domain containing) transforming protein 3 (SHC3), SHC transforming protein 1 (SHC1), sestrin 1 (SESN1), myostatin (MSTN), myogenic differentiation 1 (MYOD1), and SMAD family member 3 (SMAD3) that have attracted our interest will be discussed. The chosen genes regulated by TRF treatment were selected based on the biological processes and KEGG pathway analysis.

3.4. Biological Processes and Pathways Affected by SIPS

GSEA analysis was carried out by using the Partek Genomic Suite. GSEA analysis revealed the selected significant biological processes involved in SIPS myoblasts compared to the untreated young control group () (Table 2). The positive value of the normalized enrichment score (NES) indicated an increment in the regulation of the biological process, whereas the negative value of NES indicated a reduction in the regulation of the stated biological process. SIPS myoblasts showed a significant increase in cellular biogenic amine metabolic process and apoptosis but decreased in activities of lipoprotein and regulation of skeletal muscle cell differentiation.

GO accessionBiological processNES value

GO:0006576Cellular biogenic amine metabolic process1.332.63E − 02
GO:0044106Cellular amine metabolic process1.332.63E − 02
GO:0046885Regulation of hormone biosynthetic process−1.403.17E − 02
GO:0051004Regulation of lipoprotein lipase activity−1.323.28E − 02
GO:0070874Negative regulation of glycogen metabolic process−1.443.39E − 02
GO:0032106Positive regulation of response to extracellular stimulus−1.483.45E − 02
GO:0032109Positive regulation of response to nutrient levels−1.483.45E − 02
GO:0031442Positive regulation of mRNA 3′-end processing−1.243.57E − 02
GO:0043001Golgi to plasma membrane protein transport−1.413.64E − 02
GO:2000973Regulation of pro-B cell differentiation−1.693.77E − 02
GO:0032494Response to peptidoglycan1.403.85E − 02
GO:0006400tRNA modification1.503.92E − 02
GO:0045807Positive regulation of endocytosis−1.344.11E − 02
GO:0006970Response to osmotic stress−1.154.17E − 02
GO:0090199Regulation of release of cytochrome c from mitochondria1.294.35E − 02
GO:0042953Lipoprotein transport−1.184.35E − 02
GO:0044872Lipoprotein localization−1.184.35E − 02
GO:0061418Regulation of transcription from RNA polymerase II promoter in response to hypoxia−1.524.35E − 02
GO:0048644Muscle organ morphogenesis−1.704.35E − 02
GO:0022615Protein to membrane docking−1.494.41E − 02
GO:0035563Positive regulation of chromatin binding1.554.44E − 02
GO:0048259Regulation of receptor-mediated endocytosis−1.254.48E − 02
GO:0032922Circadian regulation of gene expression−1.254.55E − 02
GO:0006694Steroid biosynthetic process−1.374.55E − 02
GO:2001014Regulation of skeletal muscle cell differentiation−1.574.55E − 02
GO:2000641Regulation of early endosome to late endosome transport−1.314.62E − 02
GO:0043112Receptor metabolic process−1.254.69E − 02
GO:0006891Intra-Golgi vesicle-mediated transport−1.384.69E − 02
GO:0035330Regulation of hippo signalling−1.444.69E − 02
GO:0035640Exploration behavior−1.474.69E − 02
GO:0006270DNA replication initiation1.464.76E − 02
GO:0031536Positive regulation of exit from mitosis1.294.76E − 02
GO:0048020CCR chemokine receptor binding−1.414.76E − 02
GO:0017144Drug metabolic process−1.424.76E − 02
GO:2000095Regulation of Wnt signalling pathway, planar cell polarity pathway−1.694.76E − 02
GO:0042347Negative regulation of NF-kappaB import into nucleus−1.374.84E − 02
GO:0046854Phosphatidylinositol phosphorylation−1.384.84E − 02
GO:0006901Vesicle coating−1.424.84E − 02
GO:0051045Negative regulation of membrane protein ectodomain proteolysis1.624.88E − 02
GO:0006335DNA replication-dependent nucleosome assembly1.514.88E − 02
GO:0034723DNA replication-dependent nucleosome organization1.514.88E − 02
GO:2000649Regulation of sodium ion transmembrane transporter activity−1.334.92E − 02
GO:0048260Positive regulation of receptor-mediated endocytosis−1.354.92E − 02
GO:0002381Immunoglobulin production involved in immunoglobulin-mediated immune response−1.364.92E − 02
GO:0018879Biphenyl metabolic process−1.454.92E − 02

3.5. Biological Processes and Pathways Affected by the TRF-Post-treatment on SIPS Myoblasts

GSEA analysis revealed the selected significant biological processes involved in response to TRF-posttreatment SIPS myoblasts compared to SIPS control myoblasts () (Table 3). TRF treatment has significantly increased the regulation of JNK cascade, cell growth, and adult walking behavior, but decreased the cell cycle activity. KEGG pathway analysis was carried out by using Partek Genomic Suite, and the data can be categorised according to the cluster of differentially expressed genes (fold or fold ; ) and the pathway ANOVA statistical method ().

GO accessionBiological processNES value

GO:0009165Nucleotide biosynthetic process−1.361.85E − 02
GO:0006338Chromatin remodeling−1.761.85E − 02
GO:0046328Regulation of JNK cascade1.211.96E − 02
GO:0030968Endoplasmic reticulum unfolded protein response1.712.04E − 02
GO:0032069Regulation of nuclease activity1.652.04E − 02
GO:0016558Protein import into peroxisome matrix1.412.13E − 02
GO:0030259Lipid glycosylation1.562.22E − 02
GO:0090501RNA phosphodiester bond hydrolysis−1.642.22E − 02
GO:0071695Anatomical structure maturation1.392.27E − 02
GO:0032869Cellular response to insulin stimulus1.352.27E − 02
GO:0016049Cell growth1.552.38E − 02
GO:0019321Pentose metabolic process1.442.44E − 02
GO:2001241Positive regulation of the extrinsic apoptotic signalling pathway in absence of ligand1.292.44E − 02
GO:2001239Regulation of the extrinsic apoptotic signalling pathway in the absence of ligand1.242.44E − 02
GO:0032092Positive regulation of protein binding1.642.50E − 02
GO:0045599Negative regulation of fat cell differentiation1.292.56E − 02
GO:0071479Cellular response to ionizing radiation−1.373.17E − 02
GO:0006308DNA catabolic process−1.563.28E − 02
GO:0000082G1/S transition of mitotic cell cycle−1.773.28E − 02
GO:0044843Cell cycle G1/S phase transition−1.773.28E − 02
GO:0090025Regulation of monocyte chemotaxis1.523.57E − 02
GO:0046325Negative regulation of glucose import1.343.57E − 02
GO:0010800Positive regulation of peptidyl-threonine phosphorylation−1.283.57E − 02
GO:0007127Meiosis I−1.663.64E − 02
GO:0002689Negative regulation of leukocyte chemotaxis1.813.70E − 02
GO:0006534Cysteine metabolic process1.323.70E − 02
GO:0090231Regulation of spindle checkpoint−1.613.70E − 02
GO:1902850Microtubule cytoskeleton organization involved in mitosis−1.733.70E − 02
GO:0032200Telomere organization−1.883.70E − 02
GO:0071300Cellular response to retinoic acid1.263.77E − 02
GO:0006302Double-strand break repair−1.883.77E − 02
GO:0031064Negative regulation of histone deacetylation1.473.85E − 02
GO:0010155Regulation of proton transport1.373.85E − 02
GO:0032924Activin receptor signalling pathway−1.353.85E − 02
GO:0051181Cofactor transport1.623.92E − 02
GO:0000060Protein import into nucleus, translocation−1.713.92E − 02
GO:1902668Negative regulation of axon guidance1.674.00E − 02
GO:0043388Positive regulation of DNA binding1.394.00E − 02
GO:0043320Natural killer cell degranulation−1.344.00E − 02
GO:0010887Negative regulation of cholesterol storage1.624.08E − 02
GO:0032845Negative regulation of homeostatic process1.474.08E − 02
GO:0051896Regulation of protein kinase B signalling1.334.08E − 02
GO:0008334Histone mRNA metabolic process−1.694.08E − 02
GO:0097191Extrinsic apoptotic signalling pathway1.224.17E − 02
GO:0002686Negative regulation of leukocyte migration1.534.26E − 02
GO:0048706Embryonic skeletal system development1.364.26E − 02
GO:0097035Regulation of membrane lipid distribution1.354.26E − 02
GO:0061157mRNA destabilization−1.724.26E − 02
GO:2000615Regulation of histone H3-K9 acetylation−1.734.26E − 02
GO:0072528Pyrimidine-containing compound biosynthetic process−1.884.26E − 02
GO:1903036Positive regulation of response to wounding1.354.35E − 02
GO:1900746Regulation of vascular endothelial growth factor signalling pathway−1.534.35E − 02
GO:0009303rRNA transcription−1.454.41E − 02
GO:0045080Positive regulation of chemokine biosynthetic process1.884.44E − 02
GO:0008207C21-steroid hormone metabolic process1.694.44E − 02
GO:0050795Regulation of behavior1.514.44E − 02
GO:1902107Positive regulation of leukocyte differentiation1.284.44E − 02
GO:0017157Regulation of exocytosis1.164.44E − 02
GO:0007098Centrosome cycle−1.424.44E − 02
GO:0001946Lymphangiogenesis−1.434.48E − 02
GO:0050919Negative chemotaxis1.684.55E − 02
GO:0060338Regulation of the type I interferon-mediated signalling pathway1.654.55E − 02
GO:0050748Negative regulation of the lipoprotein metabolic process1.564.55E − 02
GO:0038031Noncanonical Wnt signalling pathway via JNK cascade1.534.55E − 02
GO:0010935Regulation of macrophage cytokine production1.494.55E − 02
GO:0060907Positive regulation of macrophage cytokine production1.404.55E − 02
GO:0034661ncRNA catabolic process−1.714.55E − 02
GO:0090503RNA phosphodiester bond hydrolysis, exonucleolytic−1.594.62E − 02
GO:1902624Positive regulation of neutrophil migration1.694.65E − 02
GO:2000406Positive regulation of T cell migration1.634.65E − 02
GO:2000403Positive regulation of lymphocyte migration1.564.65E − 02
GO:1901725Regulation of histone deacetylase activity2.144.76E − 02
GO:1902667Regulation of axon guidance1.814.76E − 02
GO:0048841Regulation of axon extension involved in axon guidance1.734.76E − 02
GO:0050882Voluntary musculoskeletal movement1.514.76E − 02
GO:0042113B cell activation−1.194.76E − 02
GO:0072522Purine-containing compound biosynthetic process−1.294.76E − 02
GO:1901991Negative regulation of mitotic cell cycle phase transition−1.474.76E − 02
GO:0006919Activation of cysteine-type endopeptidase activity involved in apoptotic process−1.234.84E − 02
GO:2000757Negative regulation of peptidyl-lysine acetylation−1.804.84E − 02
GO:0035067Negative regulation of histone acetylation−1.834.84E − 02
GO:0007628Adult walking behavior1.944.88E − 02
GO:0021756Striatum development−1.314.92E − 02
GO:1901988Negative regulation of cell cycle phase transition−1.464.92E − 02
GO:0045185Maintenance of protein location−1.484.92E − 02
GO:0051053Negative regulation of DNA metabolic process−1.524.92E − 02
GO:0051258Protein polymerization−1.534.92E − 02
GO:0044774Mitotic DNA integrity checkpoint−1.684.92E − 02
GO:0009124Nucleoside monophosphate biosynthetic process−1.714.92E − 02
GO:0043487Regulation of RNA stability−1.754.92E − 02
GO:0034501Protein localization to kinetochore−1.824.92E − 02
GO:0000077DNA damage checkpoint−1.914.92E − 02
GO:0000079Regulation of cyclin-dependent protein serine/threonine kinase activity−2.014.92E − 02

Our KEGG pathway analysis showed that TRF treatment significantly modulated the p53 signalling pathway, FoxO signalling pathway, Wnt signalling pathway, cell cycle, and ErbB signalling pathway (Table 4). The regulation of gene expression involved in the stated pathway is presented in Figure 2.


KEGG entryPathwayNES value

hsa04110Cell cycle25.935.50E − 12
hsa03030DNA replication20.997.64E − 10
hsa04115p53 signalling pathway16.765.25E − 08
hsa05206MicroRNAs in cancer16.081.03E − 07
hsa03460Fanconi anemia pathway14.933.29E − 07
hsa05203Viral carcinogenesis14.455.30E − 07
hsa05200Pathways in cancer12.882.54E − 06
hsa03430Mismatch repair10.173.81E − 05
hsa05322Systemic lupus erythematosus10.004.53E − 05
hsa04114Oocyte meiosis9.111.11E − 04
hsa05034Alcoholism8.641.76E − 04
hsa03420Nucleotide excision repair7.963.50E − 04
hsa03440Homologous recombination6.781.13E − 03
hsa00900Terpenoid backbone biosynthesis6.721.21E − 03
hsa05219Bladder cancer6.381.70E − 03
hsa05166HTLV-I infection6.162.11E − 03
hsa00310Lysine degradation6.022.43E − 03
hsa05210Colorectal cancer5.563.86E − 03
hsa04914Progesterone-mediated oocyte maturation5.205.52E − 03
hsa04068FoxO signalling pathway5.106.10E − 03
hsa05205Proteoglycans in cancer4.758.61E − 03
hsa04978Mineral absorption4.501.11E − 02
hsa04390Hippo signalling pathway4.491.12E − 02
hsa05222Small cell lung cancer4.231.46E − 02
hsa05217Basal cell carcinoma4.061.72E − 02
hsa05212Pancreatic cancer4.041.76E − 02
hsa00480Glutathione metabolism3.403.34E − 02
hsa05214Glioma3.174.22E − 02
hsa04310Wnt signalling pathway3.164.25E − 02


KEGG entryPathway valueFold change

hsa05323Rheumatoid arthritis8.98E − 031.13
hsa04630Jak-STAT signalling pathway1.12E − 021.07
hsa04978Mineral absorption1.56E − 021.13
hsa00860Porphyrin and chlorophyll metabolism1.89E − 021.14
hsa04340Hedgehog signalling pathway1.93E − 021.10
hsa00520Amino sugar and nucleotide sugar metabolism2.06E − 021.11
hsa05217Basal cell carcinoma2.12E − 021.10
hsa00561Glycerolipid metabolism2.18E − 021.09
hsa00630Glyoxylate and dicarboxylate metabolism2.20E − 02−1.16
hsa04330Notch signalling pathway2.22E − 021.08
hsa00564Glycerophospholipid metabolism2.33E − 021.07
hsa00100Steroid biosynthesis2.46E − 02−1.20
hsa00040Pentose and glucuronate interconversions2.66E − 021.16
hsa00051Fructose and mannose metabolism2.76E − 021.10
hsa03050Proteasome2.79E − 011.11
hsa00980Metabolism of xenobiotics by cytochrome P4503.08E − 021.08
hsa00650Butanoate metabolism3.11E − 02−1.13
hsa00052Galactose metabolism3.13E − 021.12
hsa04640Hematopoeitic cell lineage3.22E − 021.08
hsa03060Protein export3.34E − 021.13
hsa04130SNARE interactions in vesicular transport3.35E − 021.08
hsa04110Cell cycle3.39E − 02−1.31
hsa04966Collecting duct acid secretion3.48E − 021.23
hsa03430Mismatch repair3.68E − 02−1.38
hsa03030DNA replication3.70E − 02−1.80
hsa03460Fanconi anemia pathway3.79E − 02−1.29
hsa00533Glycosaminoglycan biosynthesis-keratan sulfate3.99E − 021.12
hsa04012ErbB signalling pathway4.04E − 021.05
hsa03450Nonhomologous end-joining4.25E − 02−1.19
hsa04070Phosphatidylinositol signalling system4.28E − 021.06
hsa00190Oxidative phosphorylation4.33E − 021.08
hsa00730Thiamine metabolism4.41E − 021.17
hsa03440Homologous recombination4.43E − 02−1.16
hsa00982Drug metabolism - cytochrome P4504.55E − 021.04
hsa00072Synthesis and degradation of ketone bodies4.65E − 02−1.27
hsa00532Glycosaminoglycan biosynthesis - chondroitin sulfate/dermatan sulfate4.69E − 021.15
hsa04360Axon guidance4.74E − 021.05
hsa00280Valine, leucine, and isoleucine degradation4.93E − 02−1.07
hsa04210Apoptosis4.96E − 021.06
hsa00300Lysine biosynthesis4.99E − 02−1.47

3.6. Microarray Result Validation

To confirm the microarray expression, we performed qPCR validation and found that all of the tested genes have consistent gene profiles. The REV value and fold change for all of the genes were consistent for both microarray analysis and qPCR analysis (Figure 3).

4. Discussion

Vitamin E, especially tocopherol, with a combination of other approaches, such as exercise, vitamin C, or selenium, has been reported to improve the condition of aged skeletal muscles by reducing oxidative stress and increasing the activities of antioxidant enzymes in the muscles of aged rats [20, 24]. Recently, one of the isomers of vitamin E, i.e., tocotrienol, has been proven for its effects in antioxidant defence enhancement and for improving the proliferation and differentiation in both replicative senescence and oxidative stress-induced premature myoblasts [22, 25, 26]. Khor et al. suggested that TRF reduced the senescence phenotypes in the skeletal muscle which may not only be limited to the encounter of oxidative stress but also instead be possibly associated with its regenerative capacity [23]. Little is known on the mechanism of tocotrienol towards the oxidative stress-associated senescence in myoblasts, especially human myoblasts. Thus, in this study, we revealed the novel insight into the differential gene expression by stress-induced human myoblasts with the treatment of TRF.

4.1. Alteration in Gene Expression and Biological Process in SIPS Cells

Hydrogen peroxide (H2O2) is a common inducer in the SIPS model in various cells [27, 28]. Research has shown that H2O2 downregulated the expression of miRNAs such as miR-15 and miR-106b families which contributes to some of the features of senescence cells. These features include the increased resistance to apoptosis and activation of p21CDKN1A. At the same time, it upregulates miR-182 which causes specific changes in senescence-associated gene expression [29]. Our GSEA analysis showed that SIPS has upregulated the cellular biogenic amine metabolic process in the cells while differential gene expression analysis showed that spermine oxidase (SMOX) was upregulated. Biogenic amine, such as polyamine, monoamine, and histamine, is involved in the mechanism of apoptosis progression [30]. The increasing expression of SMOX would increase the oxidation of spermine in the nucleus to spermidine, H2O2, and 3-aminopropanal and reduce the concentration of nuclear spermine, thus dysregulating the protective roles of spermine in free radical scavenging and DNA shielding and resulting in an overall increased potential for oxidative DNA damage in these cells [31]. Apoptosis has always been one of the suggested factors in the mass loss of skeletal muscle and muscle atrophy [32, 33], and our GSEA analysis was in line with this factor. The GSEA analysis showed the upregulation of cytochrome c released from mitochondria which is an indicator of early-stage apoptosis in SIPS myoblasts.

4.2. Alteration in Gene Expression, Biological Process, and Signalling Pathway in TRF-Post-treated SIPS Cells
4.2.1. ErbB Signalling Pathway

KEGG pathway analysis showed that TRF treatment on SIPS myoblasts significantly regulated a few biological processes and pathways related to cell proliferation, including the ErbB signalling pathway and FoxO signalling pathway. Several ErbB signalling-related genes, such as EGF, EGFR, EREG, SHC1, and SHC3, were significantly regulated by the treatment of TRF. The upregulation of SHC1 and SHC3 expression by 2.70-fold and 2.36-fold, respectively, by TRF treatment on SIPS myoblasts suggested the involvement of TRF in modulating the ErbB pathway, hence improving the regenerative capacity of skeletal muscle via cell proliferation. A similar effect was observed in C. elegans, in which SHC1 activated JNK signalling by binding to the MEK-1 kinase and hence regulating stress response and aging [34]. Epiregulin (EREG), an ErbB-signalling related gene, was upregulated with a 5.68-fold in TRF-posttreatment SIPS myoblasts compared to the SIPS group. Upregulation of EREG expression was reported before in C2C12 mouse myoblast cells after 4 hours from differentiation of myoblast cells. However, downregulation of the MRF gene and regulation of canonical Wnt signalling by the TRF on SIPS myoblasts minimised the possibilities of differentiation of human myoblasts (CHQ5B) in this study. Other studies with human cells showed that upregulation of EREG expression improved the proliferation ability of various human cells, i.e., mesenchymal stem cells, keratinocytes, and fibroblasts [3537]. Hence, upregulation of EREG expression by TRF-post-treatment in SIPS myoblasts suggested a similar mechanism, i.e., modulation of the ErbB pathway which leads to increased proliferation capacity of myoblasts.

4.2.2. Myostatin (MSTN)

Unpredictably, our findings showed that myostatin (MSTN), a negative regulator in the muscle growth and regenerative potential of skeletal muscle, was downregulated significantly by 5.46-fold. This would be a novel finding in the modulation of muscle regeneration by TRF. Exposure of myostatin to mouse C2C12 myoblast cells reduced the proliferation and protein synthesis and thus prevented the progression of cells from the G1 to S phase in the cell cycle [38, 39]. On the other hand, aside from an increment in total RNA and protein synthesis rate, mice with myostatin knockout had a larger size of myotube [40]. A study showed that muscle-associated senescence upregulated the expression of myostatin, but antagonists of myostatin activated the satellite cells and increased the protein level of Pax7 and MyoD which in turn improved the regenerative capacity of muscle cells [41, 42].

The signalling pathways for myostatin can be divided into SMAD-mediated and non-SMAD pathways. The present microarray analysis showed that the treatment of TRF on SIPS myoblasts modulated the FoxO signalling pathway through downregulation of MSTN and SMAD3. FoxO and SMAD are involved in the regulation of muscle growth through amplification of atrophy response and activation of MSTN expression [43]. SMAD3-mediated myostatin signalling increased the sarcomeric protein degradation via the ubiquitin-proteasome pathway by stimulating FoxO1 and atrogin-1 expression [44]. In this study, the downregulation of MSTN and SMAD3 by TRF treatment suggested that the reduction in the SMAD-mediated myostatin signalling pathway and TRF treatment would reduce the protein degradation in the SIPS myoblasts.

4.2.3. Ribonucleotide Reductase M2B (RRM2B)

Modulation of the ErbB signalling pathway and the FoxO signalling pathway by TRF treatment suggested that the mechanism involved might be related to cell proliferation. However, at the same time KEGG analysis demonstrated that TRF regulated the satellite cells to remain in the quiescent state through the p53 signalling, cell cycle, and Wnt signalling pathways. Interestingly, these findings showed a contradiction. Nevertheless, recent studies showed that the satellite cells are a heterogeneous population as they consist of satellite stem cells and committed progenitors [45]. Symmetric division of satellite cells allowed the cells to divide and expand the satellite stem cell subpopulation, whereas asymmetric division of satellite cells would maintain the stem cell population which is involved in the self-renewal and generation of myogenic progenitors [46, 47]. Dumont et al. suggested that a dynamic balance must exist in between the symmetric and asymmetric division even though the satellite cells are able to choose whether to perform symmetric or asymmetric division, and it allows them to coordinate their activity with the needs of the regenerating muscle [46]. An imbalance in the ratio of symmetric and asymmetric division would deteriorate the regenerative capacity of muscles. These were observed in the aging cells with a disruption in the satellite cells’ ability to self-renew or return to the quiescent state [48, 49].

The TRF-post-treatment on SIPS myoblasts has targeted p53 signalling by upregulating the expression of the p53 target gene, i.e., RRM2B and SESN1. RRM2B activation is initiated by DNA damage and further involved in DNA repair regulated by p53 [50, 51]. Dysfunction of RRM2B was first reported in mitochondrial DNA depletion syndrome which caused early fatality in children [52, 53]. Kuo et al. reported that the expression of RRM2B was highly induced during oxidative stress in the human primer fibroblast (IMR90 cell) to stimulate antioxidant reaction [50]. Silencing of RRM2B expression leads to an increase in reactive oxygen species level, mitochondrial membrane depolarisation, and premature senescence in young fibroblasts. Thus, upregulation of RRM2B expression by TRF was suggested to stimulate the antioxidant defence in order to counteract with the increasing stress level in SIPS myoblasts.

4.2.4. Sestrins

Aside from RRM2B, SESN1 was also upregulated by the TRF. Sestrins were the third recently identified hallmark of sarcopenia [54]. Sestrin expression was upregulated in DNA damaged cells, oxidative stress condition, and hypoxia [55]. SESN1 and SESN2 are p53-regulated genes which are involved in the regulation of autophagy and cell viability, whereas SESN3 is a FoxO-regulated gene which in turn activated SESN1 [5658]. Inhibition of mTORC1 activation via the sestrin-AMPK pathway was reported to extend the lifespan of mammalia [5961]. A more specific study was carried out to investigate the expression of sestrins towards the skeletal muscle [62]. The thorax of adult Drosophila mainly consists of skeletal muscle enriched with Drosophila sestrins (dSesn). dSesn-null flies experienced acceleration in aging-associated degeneration, such as loss of sarcomeric structure and abnormality in mitochondria. However, treatment with vitamin E effectively prevented the deterioration in skeletal muscle and cardiac muscle by reducing the involvement of ROS [62].

GSEA and KEGG pathway analysis showed that the cell cycle was downregulated by TRF treatment in SIPS myoblasts. This finding was in contrast with the findings which reported that TRF reduced the cell cycle arrest in senescence human fibroblast cells [63]. However, according to Blagosklonny, cell cycle arrest does not absolutely refer to senescence and vice versa [64]. Cell cycle arrest actually induced the cells to proceed to another stage of the cell cycle, such as quiescence, senescence, apoptosis, motility, and differentiation [65]. Skeletal muscle satellite cells withdraw from the cell cycle for two purposes, i.e., to return to the quiescence state and carry out self-renewal or to differentiate and generate new muscle fibres. These withdrawals can be differentiated by the differential expression of genes or pathway activation. Notch signalling was activated, and Spry1 expressions were upregulated when the cells return to the quiescence state, whereas Delta1 expression was upregulated for the cells to differentiate after withdrawing from the cell cycle [45, 46]. However, in our study, neither Spry1 nor Delta1 expression was regulated by TRF treatment. Instead, the expression of Spry4 was upregulated and the expression of Fgf1 was downregulated by the TRF. Downregulation of Fgf1 was once reported in the inhibition of mouse myoblast differentiation [66, 67]. Furthermore, upregulation of Fgf2 was reported in the activation of satellite cells and in turn increased the expression of Twist2 and Spry4, thus inhibiting the differentiation of mouse mesenchyme stem cells [68, 69]. Hence, we proposed that the withdrawal from the cell cycle of SIPS myoblasts posttreated with TRF was not due to differentiation, as differentiation was inhibited by downregulation of Fgf1 and upregulation of Spry4. Instead, the TRF promoted the myoblast cells to exit the cell cycle and return to their quiescence state.

4.2.5. Myogenic Regulatory Factor (MRF) and Wnt Signalling Pathway

The effect of TRF towards expression of the myogenic regulatory factor (MRF) was in line with the suggestion that the myoblasts exit the cell cycle for the maintenance of the quiescence cell population. The myogenic potential of satellite cells will highly depend on the expression of Pax and MRF (MyoD, Myf5, myogenin, and MRF4) [70]. Expression of Myf5 and/or MyoD was upregulated during the activation of satellite cells to the myoblast and early myogenic differentiation [7173], whereas differentiation of myoblasts would upregulate the expression of myogenin and MRF4 (also known as Myf6) [73]. TRF treatment has downregulated the expression of Myf5, MyoD1, and Myf6, which means that TRF treatment did not promote the myoblast cells to withdraw from the cell cycle for differentiation.

The expression of MRF was regulated by the Wnt signalling pathway during the development of the embryo. However, in the adult skeletal muscle, the canonical Wnt signalling pathway regulated the differentiation of muscle satellite cells, whereas the noncanonical Wnt signalling pathway mediated the self-renewal of satellite stem cells and the growth of muscle fibres [74]. Our GSEA analysis revealed that the noncanonical Wnt signalling pathway was significantly regulated by TRF treatment. Among the Wnt involved in the noncanonical Wnt signalling pathway (Wnt4, Wnt5a, Wnt5b, Wnt8a, Wnt8b, Wnt10a, and Wnt10b), TRF treatment has upregulated the expression of Wnt5a and Wnt7b. Studies reported that Wnt were expressed at different times of injury in order to promote regeneration [75, 76]. Wnt5a, Wnt5b, and Wnt7a were upregulated at the early stage of regeneration, and Wnt7b and Wnt3a were regulated at the final stage after injury. The regulation of the Wnt signalling pathway, cell cycle, and p53 signalling by TRF treatment suggested the promotion of satellite cells to return to the quiescence state and maintain the population of quiescence cells.

4.2.6. Growth Differentiation Factor 15 (GDF15)

Another critical finding of our microarray analysis was the expression of GDF15 which was highly upregulated by 14.9-fold. The expression of GDF15 and its mechanism involved in the CHQ5B cells were not well studied. Previously, GDF15 was reported as a negative regulator in skeletal muscle growth [77, 78]. Senescence, smoking, and environmental factors would increase the GDF15 level [79]. The levels of GDF15 increased in patients experiencing intensive care unit acquired muscle weakness (ICUAW) or muscle atrophy [80, 81]. However, aside from a high level of GDF15, SMAD2/3 was activated by GDF15 in the ICUAW patients and this was in contrast with our findings. Findings showed that SMAD3 expression was downregulated by TRF treatment. GDF15 prevents the ROS production and exhibits an antiapoptotic effect and cell proliferation regulation [78, 82, 83]. Hence, the conclusion on the activity of GDF15 towards CHQ5B cells remains unknown due to its lack of investigation. Development of therapeutic interventions with GDF15 or anti-GDF15 agents remains difficult until the mechanism that drives its activity is revealed with more evidences.

5. Conclusions

At present, most of the studies focused on the effect of tocopherol on the skeletal muscles, but less in vitro findings were reported on the effects of tocotrienol towards human skeletal muscle cells and the mechanism involved remains unclear. Recent studies showed that muscle regeneration does not solely depend on the myogenic proliferation of the satellite cell as prolonged imbalance between the expansion and maintenance of the satellite stem cell population leads to impaired muscle regeneration [46]. Our findings proposed that TRF treatment not only promotes the proliferation capacity of SIPS myoblasts through regulation of the ErbB signalling pathway (upregulation of expression of EREG, SHC1, and SHC3) and FoxO signalling pathway (downregulation of expression of MSTN and SMAD3). At the same time, TRF treatment is proposed to modulate the renewal of satellite cells through regulation of p53 signalling (upregulation of RRM2B and SESN1), cell cycle, Wnt signalling pathway, and expression of MRF. Even though these findings were exciting, more extensive studies such as proteomic analysis and various time points of TRF treatments in the SIPS myoblast model are suggested in order to give a more thorough idea on the involvement of TRF in the regenerative capacity and mechanism of skeletal muscles.


AGCC:Affymetrix GeneChip Command Console
aRNA:Amplified RNA
CEL:Cell intensity file
Ct:Threshold cycle
DMEM:Dulbecco’s modified Eagle medium
EGFR:Epidermal growth factor receptor
GAPDH:Glyceraldehyde 3-phosphate dehydrogenase
GDF15:Growth differentiation factor 15
GSEA:Gene set enrichment analysis
H2O2:Hydrogen peroxide
KEGG:Kyoto Encyclopedia of Genes and Genomes
MRF:Myogenic regulatory factor
Myf5:Myogenic factor 5
MyoD:Myogenic differentiation 1
PCA:Principal component analysis
qPCR:Quantitative real-time polymerase chain reaction
REV:Relative expression value
RIN:RNA integrity number
SIPS:Stress-induced premature senescence
SMOX:Spermine oxidase
TRF:Tocotrienol-rich fraction.

Data Availability

The data used to support the findings of this study are included within the article and supplementary information files.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Authors’ Contributions

JJL, NAK, and WZWN conceived and designed the experiments. VM provided the human satellite cells CHQ5B and technical guidance on CHQ5B cell culture. JJL performed the experiments and analysed the data. JJL and NAK drafted the manuscript. All authors read and approved the final manuscript.


This research was financially supported by the grant from Universiti Kebangsaan Malaysia (UKM FF-03-FRGS0034-2010). We would like to acknowledge Dr. Vincent Mouly from UMRS 787, Institut de Myologie, INSERM, Université Pierre et Marie Curie, Paris, France, for providing us the human satellite cells CHQ5B.

Supplementary Materials

Figure S01: RNA integrity number (RIN) assessed by the electropherogram bioanalyzer for the (a) untreated young control cells, (b) SIPS control, and (c) TRF-posttreated SIPS cells. Figure S02: the specificity of primers assessed by (a–j) standard curve and melt curve analysis of each pair of primers and (k) agarose gel electrophoresis that was performed on the PCR products. Table S01: the complete list of 41 differentially expressed genes associated with stress-induced premature senescent control cells as compared to untreated young control cells (fold change < −1.5 or fold change > 1.5; ). Table S02: the complete list of 905 differentially expressed genes associated with TRF-posttreated SIPS cells as compared to SIPS control cells (fold change < −1.5 or fold change > 1.5; ). (Supplementary Materials)


  1. A. J. Cruz-Jentoft, F. Landi, E. Topinkova, and J.-P. Michel, “Understanding sarcopenia as a geriatric syndrome,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 13, no. 1, pp. 1–7, 2010. View at: Publisher Site | Google Scholar
  2. N. H. Farina, M. Hausburg, N. D. Betta et al., “A role for RNA post-transcriptional regulation in satellite cell activation,” Skeletal Muscle, vol. 2, no. 1, p. 21, 2012. View at: Publisher Site | Google Scholar
  3. P. Kwan, “Sarcopenia, a neurogenic syndrome?” Journal of Aging Research, vol. 2013, Article ID 791679, 10 pages, 2013. View at: Publisher Site | Google Scholar
  4. K. S. Nair, “Aging muscle,” The American Journal of Clinical Nutrition, vol. 81, no. 5, pp. 953–963, 2005. View at: Publisher Site | Google Scholar
  5. E. Barbieri and P. Sestili, “Reactive oxygen species in skeletal muscle signaling,” Journal of Signal Transduction, vol. 2012, Article ID 982794, 17 pages, 2012. View at: Publisher Site | Google Scholar
  6. A. Fanzani, V. M. Conraads, F. Penna, and W. Martinet, “Molecular and cellular mechanisms of skeletal muscle atrophy: an update,” Journal of Cachexia, Sarcopenia and Muscle, vol. 3, no. 3, pp. 163–179, 2012. View at: Publisher Site | Google Scholar
  7. T. Braun and M. Gautel, “Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis,” Nature Reviews Molecular Cell Biology, vol. 12, no. 6, pp. 349–361, 2011. View at: Publisher Site | Google Scholar
  8. M. Romanick, L. V. Thompson, and H. M. Brown-Borg, “Murine models of atrophy, cachexia, and sarcopenia in skeletal muscle,” Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, vol. 1832, no. 9, pp. 1410–1420, 2013. View at: Publisher Site | Google Scholar
  9. C. A. Goodman, R. M. McNally, F. M. Hoffmann, and T. A. Hornberger, “Smad3 induces atrogin-1, inhibits mTOR and protein synthesis, and promotes muscle atrophy in vivo,” Molecular Endocrinology, vol. 27, no. 11, pp. 1946–1957, 2013. View at: Publisher Site | Google Scholar
  10. A. S. Brack, M. J. Conboy, S. Roy et al., “Increased Wnt signaling during aging alters muscle stem cell fate and increases fibrosis,” Science, vol. 317, no. 5839, pp. 807–810, 2007. View at: Publisher Site | Google Scholar
  11. B. Gao, K. Li, Y. Y. Wei et al., “Zinc finger protein 637 protects cells against oxidative stress-induced premature senescence by mTERT-mediated telomerase activity and telomere maintenance,” Cell Death & Disease, vol. 5, no. 7, article e1334, 2014. View at: Publisher Site | Google Scholar
  12. J. Jamal, M. R. Mustafa, and P.-F. Wong, “Paeonol protects against premature senescence in endothelial cells by modulating Sirtuin 1 pathway,” Journal of Ethnopharmacology, vol. 154, no. 2, pp. 428–436, 2014. View at: Publisher Site | Google Scholar
  13. J.-S. Kim, E.-J. Kim, H.-J. Kim, J.-Y. Yang, G.-S. Hwang, and C.-W. Kim, “Proteomic and metabolomic analysis of H2O2-induced premature senescent human mesenchymal stem cells,” Experimental Gerontology, vol. 46, no. 6, pp. 500–510, 2011. View at: Publisher Site | Google Scholar
  14. D. Volonte and F. Galbiati, “Polymerase I and transcript release factor (PTRF)/Cavin-1 is a novel regulator of stress-induced premature senescence,” Journal of Biological Chemistry, vol. 286, no. 33, pp. 28657–28661, 2011. View at: Publisher Site | Google Scholar
  15. M. Giorgio, M. Trinei, E. Migliaccio, and P. G. Pelicci, “Hydrogen peroxide: a metabolic by-product or a common mediator of ageing signals?” Nature Reviews Molecular Cell Biology, vol. 8, no. 9, pp. 722–728, 2007. View at: Publisher Site | Google Scholar
  16. S. C. Khor, N. Abdul Karim, W. Z. Wan Ngah, Y. A. Mohd Yusof, and S. Makpol, “Vitamin E in sarcopenia: current evidences on its role in prevention and treatment,” Oxidative Medicine and Cellular Longevity, vol. 2014, Article ID 914853, 16 pages, 2014. View at: Publisher Site | Google Scholar
  17. S.-F. Chin, J. Ibahim, S. Makpol et al., “Tocotrienol rich fraction supplementation improved lipid profile and oxidative status in healthy older adults: a randomized controlled study,” Nutrition & Metabolism, vol. 8, no. 1, p. 42, 2011. View at: Publisher Site | Google Scholar
  18. S. Makpol, A. Zainuddin, K. H. Chua, Y. A. Yusof, and W. Z. Ngah, “Gamma-tocotrienol modulation of senescence-associated gene expression prevents cellular aging in human diploid fibroblasts,” Clinics, vol. 67, no. 2, pp. 135–143, 2012. View at: Publisher Site | Google Scholar
  19. A. A. Qureshi, X. Tan, J. C. Reis et al., “Inhibition of nitric oxide in LPS-stimulated macrophages of young and senescent mice by δ-tocotrienol and quercetin,” Lipids in Health and Disease, vol. 10, no. 1, p. 239, 2011. View at: Publisher Site | Google Scholar
  20. M. J. Ryan, H. J. Dudash, M. Docherty et al., “Vitamin E and C supplementation reduces oxidative stress, improves antioxidant enzymes and positive muscle work in chronically loaded muscles of aged rats,” Experimental Gerontology, vol. 45, no. 11, pp. 882–895, 2010. View at: Publisher Site | Google Scholar
  21. A. C. Howard, A. K. McNeil, and P. L. McNeil, “Promotion of plasma membrane repair by vitamin E,” Nature Communications, vol. 2, no. 1, p. 597, 2011. View at: Publisher Site | Google Scholar
  22. J. Jye Lim, W. Zurinah Wan Ngah, V. Mouly, and N. A. Karim, “Reversal of myoblast aging by tocotrienol rich fraction posttreatment,” Oxidative Medicine and Cellular Longevity, vol. 2013, Article ID 978101, 11 pages, 2013. View at: Publisher Site | Google Scholar
  23. V. Renault, L.-E. Thornell, G. Butler-Browne, and V. Mouly, “Human skeletal muscle satellite cells: aging, oxidative stress and the mitotic clock,” Experimental Gerontology, vol. 37, no. 10-11, pp. 1229–1236, 2002. View at: Publisher Site | Google Scholar
  24. S. G. Harsini, M. Habibiyan, M. M. Moeini, and A. R. Abdolmohammadi, “Effects of dietary selenium, vitamin E, and their combination on growth, serum metabolites, and antioxidant defense system in skeletal muscle of broilers under heat stress,” Biological Trace Element Research, vol. 148, no. 3, pp. 322–330, 2012. View at: Publisher Site | Google Scholar
  25. S. C. Khor, A. M. Razak, W. Z. Wan Ngah, Y. A. Mohd Yusof, N. Abdul Karim, and S. Makpol, “The tocotrienol-rich fraction is superior to tocopherol in promoting myogenic Differentiation in the Prevention of Replicative Senescence of Myoblasts,” PLoS One, vol. 11, no. 2, article e0149265, 2016. View at: Publisher Site | Google Scholar
  26. S. C. Khor, W. Z. Wan Ngah, Y. A. Mohd Yusof, N. Abdul Karim, and S. Makpol, “Tocotrienol-rich fraction ameliorates antioxidant defense mechanisms and improves replicative senescence-associated oxidative stress in human myoblasts,” Oxidative Medicine and Cellular Longevity, vol. 2017, Article ID 3868305, 17 pages, 2017. View at: Publisher Site | Google Scholar
  27. A. Mougeolle, S. Poussard, M. Decossas, C. Lamaze, O. Lambert, and E. Dargelos, “Oxidative stress induces caveolin 1 degradation and impairs caveolae functions in skeletal muscle cells,” PLoS One, vol. 10, no. 3, article e0122654, 2015. View at: Publisher Site | Google Scholar
  28. Y. Wang, M. N. Scheiber, C. Neumann, G. A. Calin, and D. Zhou, “MicroRNA regulation of ionizing radiation-induced premature senescence,” International Journal of Radiation Oncology, Biology, Physics, vol. 81, no. 3, pp. 839–848, 2011. View at: Publisher Site | Google Scholar
  29. G. Li, C. Luna, J. Qiu, D. L. Epstein, and P. Gonzalez, “Alterations in microRNA expression in stress-induced cellular senescence,” Mechanisms of Ageing and Development, vol. 130, no. 11-12, pp. 731–741, 2009. View at: Publisher Site | Google Scholar
  30. A. Toninello, M. Salvi, P. Pietrangeli, and B. Mondovì, “Biogenic amines and apoptosis: minireview article,” Amino Acids, vol. 26, no. 4, pp. 339–343, 2004. View at: Publisher Site | Google Scholar
  31. T. Murray-Stewart, Y. Wang, A. Goodwin, A. Hacker, A. Meeker, and R. A. Casero Jr, “Nuclear localization of human spermine oxidase isoforms – possible implications in drug response and disease etiology,” FEBS Journal, vol. 275, no. 11, pp. 2795–2806, 2008. View at: Publisher Site | Google Scholar
  32. E. Marzetti, R. Calvani, R. Bernabei, and C. Leeuwenburgh, “Apoptosis in skeletal myocytes: a potential target for interventions against sarcopenia and physical frailty – a mini-review,” Gerontology, vol. 58, no. 2, pp. 99–106, 2012. View at: Publisher Site | Google Scholar
  33. S. E. Wohlgemuth, A. Y. Seo, E. Marzetti, H. A. Lees, and C. Leeuwenburgh, “Skeletal muscle autophagy and apoptosis during aging: effects of calorie restriction and life-long exercise,” Experimental Gerontology, vol. 45, no. 2, pp. 138–148, 2010. View at: Publisher Site | Google Scholar
  34. E. Neumann-Haefelin, W. Qi, E. Finkbeiner, G. Walz, R. Baumeister, and M. Hertweck, “SHC-1/p52Shc targets the insulin/IGF-1 and JNK signaling pathways to modulate life span and stress response in C. elegans,” Genes & Development, vol. 22, no. 19, pp. 2721–2735, 2008. View at: Publisher Site | Google Scholar
  35. Y. Cao, D. S. Xia, S. R. Qi et al., “Epiregulin can promote proliferation of stem cells from the dental apical papilla via MEK/Erk and JNK signalling pathways,” Cell Proliferation, vol. 46, no. 4, pp. 447–456, 2013. View at: Publisher Site | Google Scholar
  36. C. Lindvall, M. Hou, T. Komurasaki et al., “Molecular characterization of human telomerase reverse transcriptase-immortalized human fibroblasts by gene expression profiling: activation of the epiregulin gene,” Cancer Research, vol. 63, no. 8, pp. 1743–1747, 2003. View at: Google Scholar
  37. Y. Shirakata, T. Komurasaki, H. Toyoda et al., “Epiregulin, a novel member of the epidermal growth factor family, is an autocrine growth factor in normal human keratinocytes,” Journal of Biological Chemistry, vol. 275, no. 8, pp. 5748–5753, 2000. View at: Publisher Site | Google Scholar
  38. W. E. Taylor, S. Bhasin, J. Artaza et al., “Myostatin inhibits cell proliferation and protein synthesis in C2C12 muscle cells,” American Journal of Physiology-Endocrinology and Metabolism, vol. 280, no. 2, pp. E221–E228, 2001. View at: Publisher Site | Google Scholar
  39. M. Thomas, B. Langley, C. Berry et al., “Myostatin, a negative regulator of muscle growth, functions by inhibiting myoblast proliferation,” Journal of Biological Chemistry, vol. 275, no. 51, pp. 40235–40243, 2000. View at: Publisher Site | Google Scholar
  40. J. Rodriguez, B. Vernus, M. Toubiana et al., “Myostatin inactivation increases myotube size through regulation of translational initiation machinery,” Journal of Cellular Biochemistry, vol. 112, no. 12, pp. 3531–3542, 2011. View at: Publisher Site | Google Scholar
  41. A. Baumann, C. Ibebunjo, W. Grasser, and V. Paralkar, “Myostatin expression in age and denervation-induced skeletal muscle atrophy,” Journal of Musculoskeletal and Neuronal Interactions, vol. 3, no. 1, pp. 8–16, 2003. View at: Google Scholar
  42. V. Siriett, M. S. Salerno, C. Berry et al., “Antagonism of myostatin enhances muscle regeneration during sarcopenia,” Molecular Therapy, vol. 15, no. 8, pp. 1463–1470, 2007. View at: Publisher Site | Google Scholar
  43. D. L. Allen and T. G. Unterman, “Regulation of myostatin expression and myoblast differentiation by FoxO and SMAD transcription factors,” American Journal of Physiology-Cell Physiology, vol. 292, no. 1, pp. C188–C199, 2007. View at: Publisher Site | Google Scholar
  44. S. Lokireddy, V. Mouly, G. Butler-Browne et al., “Myostatin promotes the wasting of human myoblast cultures through promoting ubiquitin-proteasome pathway-mediated loss of sarcomeric proteins,” American Journal of Physiology-Cell Physiology, vol. 301, no. 6, pp. C1316–C1324, 2011. View at: Publisher Site | Google Scholar
  45. S. Kuang, K. Kuroda, F. Le Grand, and M. A. Rudnicki, “Asymmetric self-renewal and commitment of satellite stem cells in muscle,” Cell, vol. 129, no. 5, pp. 999–1010, 2007. View at: Publisher Site | Google Scholar
  46. N. A. Dumont, Y. X. Wang, and M. A. Rudnicki, “Intrinsic and extrinsic mechanisms regulating satellite cell function,” Development, vol. 142, no. 9, pp. 1572–1581, 2015. View at: Publisher Site | Google Scholar
  47. Y. X. Wang, N. A. Dumont, and M. A. Rudnicki, “Muscle stem cells at a glance,” Journal of Cell Science, vol. 127, no. 21, pp. 4543–4548, 2014. View at: Publisher Site | Google Scholar
  48. F. D. Price, J. von Maltzahn, C. F. Bentzinger et al., “Inhibition of JAK-STAT signaling stimulates adult satellite cell function,” Nature Medicine, vol. 20, no. 10, pp. 1174–1181, 2014. View at: Publisher Site | Google Scholar
  49. M. T. Tierney, T. Aydogdu, D. Sala et al., “STAT3 signaling controls satellite cell expansion and skeletal muscle repair,” Nature Medicine, vol. 20, no. 10, pp. 1182–1186, 2014. View at: Publisher Site | Google Scholar
  50. M.-L. Kuo, A. J. Sy, L. Xue et al., “RRM2B suppresses activation of the oxidative stress pathway and is up-regulated by P53 during senescence,” Scientific Reports, vol. 2, no. 1, p. 822, 2012. View at: Publisher Site | Google Scholar
  51. R. D. S. Pitceathly, C. Smith, C. Fratter et al., “Adults with RRM2B-related mitochondrial disease have distinct clinical and molecular characteristics,” Brain, vol. 135, no. 11, pp. 3392–3403, 2012. View at: Publisher Site | Google Scholar
  52. B. Bornstein, E. Area, K. M. Flanigan et al., “Mitochondrial DNA depletion syndrome due to mutations in the RRM2B gene,” Neuromuscular Disorders, vol. 18, no. 6, pp. 453–459, 2008. View at: Publisher Site | Google Scholar
  53. A. Bourdon, L. Minai, V. Serre et al., “Mutation of RRM2B, encoding p53-controlled ribonucleotide reductase (p53R2), causes severe mitochondrial DNA depletion,” Nature Genetics, vol. 39, no. 6, pp. 776–780, 2007. View at: Publisher Site | Google Scholar
  54. F. Sanchis-Gomar, H. Pareja-Galeano, S. Mayero, C. Perez-Quilis, and A. Lucia, “New molecular targets and lifestyle interventions to delay aging sarcopenia,” Frontiers in Aging Neuroscience, vol. 6, 2014. View at: Publisher Site | Google Scholar
  55. J. H. Lee, A. V. Budanov, and M. Karin, “Sestrins orchestrate cellular metabolism to attenuate aging,” Cell Metabolism, vol. 18, no. 6, pp. 792–801, 2013. View at: Publisher Site | Google Scholar
  56. A. V. Budanov, T. Shoshani, A. Faerman et al., “Identification of a novel stress-responsive gene Hi95 involved in regulation of cell viability,” Oncogene, vol. 21, no. 39, pp. 6017–6031, 2002. View at: Publisher Site | Google Scholar
  57. V. Nogueira, Y. Park, C.-C. Chen et al., “Akt determines replicative senescence and oxidative or oncogenic premature senescence and sensitizes cells to oxidative apoptosis,” Cancer Cell, vol. 14, no. 6, pp. 458–470, 2008. View at: Publisher Site | Google Scholar
  58. S. Velasco-Miguel, L. Buckbinder, P. Jean et al., “PA26, a novel target of the p53 tumor suppressor and member of the GADD family of DNA damage and growth arrest inducible genes,” Oncogene, vol. 18, no. 1, pp. 127–137, 1999. View at: Publisher Site | Google Scholar
  59. A. V. Budanov and M. Karin, “p53 target genes Sestrin1 and Sestrin2 connect genotoxic stress and mTOR signaling,” Cell, vol. 134, no. 3, pp. 451–460, 2008. View at: Publisher Site | Google Scholar
  60. W. Mair, I. Morantte, A. P. C. Rodrigues et al., “Lifespan extension induced by AMPK and calcineurin is mediated by CRTC-1 and CREB,” Nature, vol. 470, no. 7334, pp. 404–408, 2011. View at: Publisher Site | Google Scholar
  61. R. A. Miller, D. E. Harrison, C. M. Astle et al., “Rapamycin, but not resveratrol or simvastatin, extends life span of genetically heterogeneous mice,” The Journals of Gerontology: Series A, vol. 66A, no. 2, pp. 191–201, 2011. View at: Publisher Site | Google Scholar
  62. J. H. Lee, A. V. Budanov, E. J. Park et al., “Sestrin as a feedback inhibitor of TOR that prevents age-related pathologies,” Science, vol. 327, no. 5970, pp. 1223–1228, 2010. View at: Publisher Site | Google Scholar
  63. S. Makpol, L. W. Durani, K. H. Chua, Y. A. Mohd Yusof, and W. Z. Wan Ngah, “Tocotrienol-rich fraction prevents cell cycle arrest and elongates telomere length in senescent human diploid fibroblasts,” Journal of Biomedicine and Biotechnology, vol. 2011, Article ID 506171, 11 pages, 2011. View at: Publisher Site | Google Scholar
  64. M. V. Blagosklonny, “Cell cycle arrest is not yet senescence, which is not just cell cycle arrest: terminology for TOR-driven aging,” Aging, vol. 4, no. 3, pp. 159–165, 2012. View at: Publisher Site | Google Scholar
  65. C. Gérard and A. Goldbeter, “The balance between cell cycle arrest and cell proliferation: control by the extracellular matrix and by contact inhibition,” Interface Focus, vol. 4, no. 3, article 20130075, 2014. View at: Publisher Site | Google Scholar
  66. C. H. Clegg, T. A. Linkhart, B. B. Olwin, and S. D. Hauschka, “Growth factor control of skeletal muscle differentiation: commitment to terminal differentiation occurs in G1 phase and is repressed by fibroblast growth factor,” The Journal of Cell Biology, vol. 105, no. 2, pp. 949–956, 1987. View at: Publisher Site | Google Scholar
  67. C. Conte, N. Ainaoui, A. Delluc-Clavières et al., “Fibroblast growth factor 1 induced during myogenesis by a transcription–translation coupling mechanism,” Nucleic Acids Research, vol. 37, no. 16, pp. 5267–5278, 2009. View at: Publisher Site | Google Scholar
  68. J. V. Chakkalakal, K. M. Jones, M. A. Basson, and A. S. Brack, “The aged niche disrupts muscle stem cell quiescence,” Nature, vol. 490, no. 7420, pp. 355–360, 2012. View at: Publisher Site | Google Scholar
  69. W.-T. Lai, V. Krishnappa, and D. G. Phinney, “Fibroblast growth factor 2 (Fgf2) inhibits differentiation of mesenchymal stem cells by inducing Twist2 and Spry4, blocking extracellular regulated kinase activation, and altering Fgf receptor expression levels,” Stem Cells, vol. 29, no. 7, pp. 1102–1111, 2011. View at: Publisher Site | Google Scholar
  70. F. Le Grand and M. A. Rudnicki, “Skeletal muscle satellite cells and adult myogenesis,” Current Opinion in Cell Biology, vol. 19, no. 6, pp. 628–633, 2007. View at: Publisher Site | Google Scholar
  71. D. D. W. Cornelison and B. J. Wold, “Single-cell analysis of regulatory gene expression in quiescent and activated mouse skeletal muscle satellite cells,” Developmental Biology, vol. 191, no. 2, pp. 270–283, 1997. View at: Publisher Site | Google Scholar
  72. A. Montesano, P. Senesi, L. Luzi, S. Benedini, and I. Terruzzi, “Potential therapeutic role of L-carnitine in skeletal muscle oxidative stress and atrophy conditions,” Oxidative Medicine and Cellular Longevity, vol. 2015, Article ID 646171, 13 pages, 2015. View at: Publisher Site | Google Scholar
  73. Y. X. Wang and M. A. Rudnicki, “Satellite cells, the engines of muscle repair,” Nature Reviews Molecular Cell Biology, vol. 13, no. 2, pp. 127–133, 2012. View at: Publisher Site | Google Scholar
  74. J. von Maltzahn, N. C. Chang, C. F. Bentzinger, and M. A. Rudnicki, “Wnt signaling in myogenesis,” Trends in Cell Biology, vol. 22, no. 11, pp. 602–609, 2012. View at: Publisher Site | Google Scholar
  75. A. S. Brack, I. M. Conboy, M. J. Conboy, J. Shen, and T. A. Rando, “A temporal switch from notch to Wnt signaling in muscle stem cells is necessary for normal adult myogenesis,” Cell Stem Cell, vol. 2, no. 1, pp. 50–59, 2008. View at: Publisher Site | Google Scholar
  76. A. Polesskaya, P. Seale, and M. A. Rudnicki, “Wnt signaling induces the myogenic specification of resident CD45+ adult stem cells during muscle regeneration,” Cell, vol. 113, no. 7, pp. 841–852, 2003. View at: Publisher Site | Google Scholar
  77. A. Kalinkovich and G. Livshits, “Sarcopenia – the search for emerging biomarkers,” Ageing Research Reviews, vol. 22, pp. 58–71, 2015. View at: Publisher Site | Google Scholar
  78. S. Subramaniam, J. Strelau, and K. Unsicker, “Growth differentiation factor-15 prevents low potassium-induced cell death of cerebellar granule neurons by differential regulation of Akt and ERK pathways,” Journal of Biological Chemistry, vol. 278, no. 11, pp. 8904–8912, 2003. View at: Publisher Site | Google Scholar
  79. R. Adela and S. K. Banerjee, “GDF-15 as a target and biomarker for diabetes and cardiovascular diseases: a translational prospective,” Journal of Diabetes Research, vol. 2015, Article ID 490842, 14 pages, 2015. View at: Publisher Site | Google Scholar
  80. S. A. A. Bloch, J. Y. Lee, T. Syburra et al., “Increased expression of GDF-15 may mediate ICU-acquired weakness by down-regulating muscle microRNAs,” Thorax, vol. 70, no. 3, pp. 219–228, 2015. View at: Publisher Site | Google Scholar
  81. S. A. A. Bloch, J. Y. Lee, S. J. Wort, M. I. Polkey, P. R. Kemp, and M. J. D. Griffiths, “Sustained elevation of circulating growth and differentiation factor-15 and a dynamic imbalance in mediators of muscle homeostasis are associated with the development of acute muscle wasting following cardiac surgery,” Critical Care Medicine, vol. 41, no. 4, pp. 982–989, 2013. View at: Publisher Site | Google Scholar
  82. C. Carrillo-Garcia, S. Prochnow, I. K. Simeonova et al., “Growth/differentiation factor 15 promotes EGFR signalling, and regulates proliferation and migration in the hippocampus of neonatal and young adult mice,” Development, vol. 141, no. 4, pp. 773–783, 2014. View at: Publisher Site | Google Scholar
  83. J. Heger, E. Schiegnitz, D. von Waldthausen, M. M. Anwar, H. M. Piper, and G. Euler, “Growth differentiation factor 15 acts anti-apoptotic and pro-hypertrophic in adult cardiomyocytes,” Journal of Cellular Physiology, vol. 224, no. 1, pp. 120–126, 2010. View at: Publisher Site | Google Scholar

Copyright © 2019 Jing Jye Lim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles