Integrative analyses of genes and miRNAs associated with age-related sarcopenia

doi:10.21203/rs.3.rs-732253/v2

Download PDF

Research Article

Integrative analyses of genes and miRNAs associated with age-related sarcopenia

https://doi.org/10.21203/rs.3.rs-732253/v2

This work is licensed under a CC BY 4.0 License

Version 2

posted

You are reading this latest preprint version

Identifying genome-wide transcription expression patterns is important to confirm general gene function in sarcopenia patients. The purpose of this study was to find differentially expressed miRNAs (DE-miRNAs) in the muscle samples of older people. In addition, we researched to find new miRNA-mRNA regulatory network for treating sarcopenia. Microarray datasets of Gene Expression Series (GSE) 8479 and GSE1428 were obtained for the vastus lateralis muscles of young and older male subjects. Dataset GSE23527 contained microRNA arrays of 12 young muscle samples and 12 older muscle samples. In the GSE8479 and GSE1428 datasets, a total of 81 DEGs were discovered, including 4 upregulated genes and 77 downregulated genes. The top 12 clusters and their representative enriched terms were identified using Metascape. A total of 79 nodes and 186 edges were predicted in the PPI network. 1 upregulated DE-miRNA (hsa-miR-450a-5p) and 6 downregulated DE-miRNAs (hsa-miR-127-3p, hsa-miR-24-2-5p, hsa-miR-378a-5p, hsa-miR-532-5p, hsa-miR-487b-5p, and has-miR-487b-3p) were selected in the miRBase database. The MiRWalk online database was utilized for exploring 8017 genes that were selected as genes regulated by DE-miRNAs and six of them overlapped with hub genes. COX7A1 and NDUFB5 showed significantly low expression in sarcopenia patients compared to the controls. The bioinformatics analysis showed that two target genes (COX7A1 and NDUFB5) were potentially downregulated in sarcopenia patients. Several miRNAs (hsa-miR-378a-5p, hsa-miR-532-5p, hsa-miR-127-3p, and hsa-miR-24-2-5p) were identified as regulating the target genes. These results suggest that controlling the identified miRNAs could be a prospective strategy for treating sarcopenia by regulating the mRNA-miRNA network.

Level of Evidence: Level I

Integrative analyses

COX7A1

NDUFB5

miRNAs

Sarcopenia

Aging

Sarcopenia is an age-related disease with skeletal muscle loss, weakness, and functional impairment.(Doherty 2003) As skeletal muscle typically comprises the largest amount of body protein, a reduction in muscle can lead to muscle injury, decreasing the voluntary muscle capacity.(Fried et al. 2005) The potential cause of sarcopenia including the programmed cell death of muscles, inflammation, reactive oxygen species (ROS), protein turnover, and mitochondrial dysfunction has been studied.(Kujoth et al. 2005; Yasuda et al. 2005; Emelyanova et al. 2018) Because society is aging, age-related sarcopenia will become even more obvious. Therefore, it is necessary to research the mechanism and ways to improve treatment of the disease.

Undoubtedly, energy-producing related factors might be important because sarcopenia is a muscle disorder of aged people. In addition, sarcopenia is not just a single protein, gene, or system dysfunction. Thus identifying genome-wide transcription expression patterns to confirm general gene function is an efficient method. To investigate the molecular mechanisms of muscle loss with age, researchers have utilized various methods like oligonucleotide-based microarrays, gene expression changes in skeletal muscle, and cDNA arrays. (Larsson et al. 1979; Welle et al. 2000, 2003; Jozsi et al. 2000) These studies showed that the genes related to DNA damage repair, energy metabolism, mitochondrial function, and oxidative stress were involved in the differential expression. The results provided meaningful insight into sarcopenia, but there were indisputable limitations due to the sample size.

MicroRNAs (miRNAs) are composed of ubiquitous and small non-coding RNA molecules. They contain approximately 22 nucleotides and function in RNA silencing or the regulation of gene expression.(Bartel 2018) Each individual miRNA can modulate diverse mRNAs, indicating their great potential for the treatment and prognosis of diseases.(Bartel 2004) A number of studies suggested that the aging process might be regulated by miRNAs. (Ibáñez-Ventoso et al. 2006; Liang et al. 2009; Hackl et al. 2010, p. 17; van Rooij et al. 2012; Nie et al. 2015) Especially, since mitochondrial dysfunction and oxidative damage are very important in age-related diseases, many researchers have studied these topics. For example, Schmidt and Khatri suggested that nuclear respiratory factor-1 (Nrf2) was regulated by miR-340-5p and miR-206, which is a key factor in regulating redox environments.(Khatri et al. 2018; Schmidt et al. 2019) Several mitochondrial (mito)miRs (-34a, -146a, and − 181a) were also related to aging and directly controlled mitochondrial function by regulating mitochondrial protein expression. The loss of mitochondrial function results in oxidative stress, inflammation, and age-related diseases.(Rippo et al. 2014; Giuliani et al. 2018) These factors associated with the mitochondrial system might be related to sarcopenia due to age-related gene regulation, but the relationship is still poorly understood.

The purpose of this study was to find differentially expressed miRNAs (DE-miRNAs) in the muscle samples of older people. In addition, we researched to identify new miRNA-mRNA regulatory network for treating sarcopenia.

2.1. Microarray Data Source

First, the gene expression profiles were downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo) databases. Microarray datasets of Gene Expression Series (GSE) 8479 and GSE1428 were obtained for the vastus lateralis muscles of young and older male subjects. The microarray of the GS8479 and GSE1428 datasets was based on GPL2700 (Sentrix HumanRef-8 Expression BeadChip) and GPL96 ([HG-U133A] Affymetrix Human Genome U133A Array). GSE8479 contained 12 young muscle samples and 13 older muscle samples, and GSE1428 contained 10 young muscle samples and 12 older muscle samples. Additionally, the GSE23527 dataset was derived from the GPL10358 platform (LC_MRA-1001_miRHuman_11.0_080411) and contained the microRNA arrays of 12 young muscle samples and 12 older muscle samples.

2.2. Data Processing and Pathway Enrichment Analysis of GSE8479 and GSE1428

The DEGs between the older and young GSE8479 and GSE1428 samples were detected using the imaGEO (https://imageo.genyo.es) online analysis tool. An False Discovery Rate (FDR) P-value of < 0.05 was considered to indicate a DEG. We used the Metascape online database (http://metascape.org) to analyze the DEG enrichment of several pathways (min overlap = 3, P-value cutoff = 0.01, min enrichment = 1.5). Pathway and process enrichment analysis with ontology sources including the KEGG pathway, GO biological processes, Reactome gene sets, WikiPathways, and CORUM were analyzed by Metascape. The GO biological process was used to identify unique properties of the genomic data and KEGG was utilized for defining relationships, molecular interactions, and reaction networks of the DEGs. Reactome and WikiPathways are free pathway databases selected by experts and the scientific community. The CORUM databases are a resource of manually annotated protein complexes.

2.3. Protein-Protein Interaction Network Analysis and Hub Gene Identification

The PPI network of the DEGs was constructed using the Search Tool for Retrieval of Interacting Genes (STRING) app in Cytoscape software (version 1.6.0). Then, the PPI network was evaluated using the MCODE app in Cytoscape (degree cutoff = 2, max. depth = 100, node score cutoff = 0.2, and k-core = 2). The 20 hub genes of the DEGs were identified by CytoHubba, a Cytoscape plugin, using maximum correlation criterion (MCC).

2.4. Prediction of Potential miRNA and Target Genes

GEO2R (https://www.ncbi.nlm.nih.gov) was used to select the DE-miRNAs in the GSE 23527 dataset. Adjusted P-values of < 0.05 |logFC| > 0.8 were set as the cutoff criteria with force normalization.

The genes regulated by DE-miRNAs in GSE23527 were selected using miRWalk and miRBase (http://www.mirbase.org/) databases using scores of > 0.95 in the random forest-based approach by executing the TarPmiR algorithm for miRNA target site prediction. The intersection of the genes regulated by the DE-miRNAs and hub genes of the DEGs were considered the target genes. The Venn diagram web tool (http://bioinformatics.psb.ugent.be/webtools/Venn) was used to identify the target genes.

2.5. Statistics

Bioinformatic tools were used for most of the statistical analyses above. When using the imaGEO web tool, meta-analysis was performed using the effect size of the random-effects model. In GEO2R, the Benjamini and Hochberg false discovery rate method was selected by default.

3.1. Identification and Enrichment of Differentially Expressed Genes in Young and Older Muscle Samples

In the GSE8479 and GSE1428 datasets, a total of 81 DEGs were discovered, including 4 upregulated genes and 77 downregulated genes (Fig. 1A). The specific results of the gene analysis are presented in Supplementary Table 1.

Functional and pathway enrichment analyses of the DEGs were performed using Metascape online tools. The top 12 clusters and their representative enriched terms were identified (Fig. 1B). The Reactome gene sets showed that the citric acid (TCA) cycle and respiratory electron transport were the most significant pathways. In addition, GO biological processes and KEGG pathway analyses showed that mitochondrion organization and cardiac muscle contraction were also major pathways. The enriched GO terms are described in Table 1. A network plot of the enriched terms is also presented with the best p-values from each of the 20 clusters. Terms with a similarity of > 0.3 are connected by edges (Figs. 1C and 1D).

Table 1

Top 12 clusters with their representative enriched terms
GO	Category	Description	Count	%	Log10(P)	Log10(q)
R-HSA-1428517	Reactome Gene Sets	The citric acid (TCA) cycle and respiratory electron transport	17	20.99	-20.77	-16.41
GO:0007005	GO Biological Processes	mitochondrion organization	16	19.75	-11.45	-8.43
hsa04260	KEGG Pathway	Cardiac muscle contraction	7	8.64	-8.55	-5.64
R-HSA-1268020	Reactome Gene Sets	Mitochondrial protein import	6	7.41	-7.51	-4.68
hsa01200	KEGG Pathway	Carbon metabolism	6	7.41	-6.01	-3.26
GO:0032787	GO Biological Processes	monocarboxylic acid metabolic process	10	12.35	-4.79	-2.16
CORUM:320	CORUM	55S ribosome, mitochondrial	4	4.94	-4.14	-1.6
hsa00020	KEGG Pathway	Citrate cycle (TCA cycle)	3	3.7	-4.06	-1.53
WP4255	WikiPathways	Non-small cell lung cancer	3	3.7	-2.93	-0.62
GO:0030901	GO Biological Processes	midbrain development	3	3.7	-2.7	-0.45
GO:0051341	GO Biological Processes	regulation of oxidoreductase activity	3	3.7	-2.4	-0.18
GO:0106106	GO Biological Processes	cold-induced thermogenesis	3	3.7	-2.08	0
“Count” is the number of genes in the user-provided lists with membership in the given ontology term. "%" is the percentage of all of the user-provided genes that are found in the given ontology term. "Log10(P)" is the p-value in log base 10. "Log10(q)" is the multi-test adjusted p-value in log base 10.

3.2 PPI Network and Module Analysis

Using the STRING app in Cytoscape, the potential correlation between these DEGs was examined (maximum additional interactors = 0, confidence score cutoff = 0.4) (Fig. 2A). A total of 79 nodes and 186 edges were predicted in the PPI network. Then, the MCODE app in Cytoscape showed the significant modules in the PPI network (degree cutoff = 2, max. depth = 100, node score cutoff = 0.2, and k-core = 2) (Figs. 2B, 2C, and 2D). CytoHubba plugin in Cytoscape is used for clustering the hub genes (Fig. 2E). The top 20 hub genes are included in Table 2 with Maximum correlation criterion (MMC) scores.

Table 2

Top 20 in network String Network ranked by MCC method.
Rank	Name	shared name	Score
1	9606.ENSP00000317780	COX5A	39900000
2	9606.ENSP00000306397	UQCRFS1	39900000
3	9606.ENSP00000367939	UQCRQ	39900000
4	9606.ENSP00000457513	COX4I1	39900000
5	9606.ENSP00000359098	COX7A2	39900000
6	9606.ENSP00000419087	NDUFB2	39900000
7	9606.ENSP00000417656	COX7B	39900000
8	9606.ENSP00000377033	ATP5G1	39900000
9	9606.ENSP00000284727	ATP5G3	39900000
10	9606.ENSP00000259037	NDUFB5	39900000
11	9606.ENSP00000389649	ATP5J	39900000
12	9606.ENSP00000237889	NDUFB3	39900000
13	9606.ENSP00000292907	COX7A1	5040
14	9606.ENSP00000377446	SUCLG1	1683
15	9606.ENSP00000342056	CS	889
16	9606.ENSP00000368528	APOO	850
17	9606.ENSP00000401770	C14orf2	744
18	9606.ENSP00000369134	PDHA1	173
19	9606.ENSP00000361993	TIMM8A	49
20	9606.ENSP00000387262	IMMT	44

3.3 Prediction of Potential miRNAs and Target Genes

The microarray GSE23527expression dataset was analyzed using GEO2R. A total of seven DE-miRNAs were obtained (Fig. 3A volcano plot). Based on data from GEO2R, 1 upregulated DE-miRNA (hsa-miR-450a-5p) and 6 downregulated DE-miRNAs (hsa-miR-127-3p, hsa-miR-24-2-5p, hsa-miR-378a-5p, hsa-miR-532-5p, hsa-miR-487b-5p, and has-miR-487b-3p) were selected from the miRBase database for further analysis (Table 3). Full DE-miRNA data are included in Supplementary Table 2.

Table 3

Selection of DE-miRNAs from miRBase.
DE-miRNAs	Regulation state	Selected DE-miRNAs from miRBase	Regulation state
Ctr01-3M10	Up	x	x
hsa-miR-127-3p	Down	hsa-miR-127-3p	Down
hsa-miR-24-2*	Down	hsa-miR-24-2-5p	Down
hsa-miR-378*	Down	hsa-miR-378a-5p	Down
hsa-miR-450a	Up	hsa-miR-450a-5p	Up
hsa-miR-487b	Down	hsa-miR-487b-5p has-miR-487b-3p	Down Down
hsa-miR-532-5p	Down	hsa-miR-532-5p	Down

The MiRWalk online database was utilized for exploring 8017 genes that were selected as genes regulated by DE-miRNAs (Supplementary Table 3) and 6 of them overlapped with the hub genes (Fig. 3B). These target genes all showed low expression in older people (Fig. 1A). These target genes were entered into the miRNA-target gene regulatory network in Cytoscape (Fig. 3C).

Sarcopenia refers to gradual and progressive functional limitations and deterioration in muscle strength and endurance. (Greenlund and Nair 2003; Mühlberg and Sieber 2004) Aging is a major factor in this disease by affecting physical activity capability and the molecular basis for the loss of muscle mass. (Goyns et al. 1998; Starling et al. 1999) To understand the underlying biology of sarcopenia and discover an effective intervention that can improve muscle function, it is necessary to monitor gene expression changes in a genome-wide study. Bioinformatics analyses including extended previous data are based on screening genetic alterations of gene expression.(Mullighan et al. 2007)

By identifying the key expression genes and miRNAs of disease including mRNA-miRNA interactions, many helpful disease control strategies can be acquired. To identify more optimal core genes of muscle weakness involved in aging, this study used two profile datasets (GSE8479 and GSE1428) based on bioinformatics methods. Twenty-five aged muscle specimens and twenty-two young muscle specimens were used in the research. Via imaGEO online analysis, a total of 81 commonly changed DEGs including 4 upregulated and 77 downregulated DEGs were revealed. After that, the Metascape online tool was used for functional and pathway enrichment analysis. DEGs were enriched in the category of 1) Reactome gene sets including the TCA cycle and respiratory electron transport and mitochondrial protein import; 2) GO biological processes including mitochondrion organization, monocarboxylic acid metabolic processes, midbrain development, and the regulation of oxidoreductase activity and cold-induced thermogenesis; 3) the KEGG pathway including cardiac muscle contraction, carbon metabolism, and the citrate cycle; and 4) CORUM including the mitochondrial 55S ribosome. Particularly, 23 DEGs including the citric acid (TCA) cycle, respiratory electron transport, and mitochondrial protein import were identified in the Reactome gene sets. In GO biological process and KEGG pathway analyses, mitochondrion organization and cardiac muscle contraction were most associated in each category. Next, a PPI network complex of the DEGs was constructed, which was composed of 79 nodes and 186 edges via Cytoscape using the STRING database. Three vital clusters were obtained by MCODE analysis including 1) cluster one of 12 nodes (COX7B, COX4I1, COX5A, COX7A2, ATP5J, ATP5G1, ATP5G3, NDUFB2, NDUFB3, NDUFB5, UQCRQ, and UQCRFS1) and 66 edges; 2) cluster two of four nodes (CS, APOO, SUCLG1, and PDHA1) and five edges; and 3) cluster three of three nodes (MRPL2, MRPL12, and MRPL34) and three edges. In CytoHubba, a Cytoscape plugin, 20 hub genes (COX5A, UQCRFS1, UQCRQ, COX4I1, COX7A2, NDUFB2, COX7B, ATP5G1, ATP5G3, NDUFB5, ATP5J, NDUFB3, COX7A1, SUCLG1, CS, APOO, C14orf2, PDHA1, and TIMM8A) were clarified. Seven DE-MiRNAs (hsa-miR-450a-5p, hsa-miR-127-3p, hsa-miR-24-2-5p, hsa-miR-378a-5p, hsa-miR-532-5p, hsa-miR-487b-5p, and has-miR-487b-3p) based on one profile dataset (GSE23527) were identified and the Venn diagram web tool was used for the target genes. Finally, we found six target genes (COX7A1, NDUFB5, COX7B, PDHA1, TIMM8A, and CS) and miRNAs that could be considered novel effective targets to treat patients with age-related sarcopenia.

The present study showed that most genes were related to mitochondrial function and energy-producing capabilities. Skeletal muscle mitochondrial capacity is an important factor and is well-studied.(Coen et al. 2013; Gonzalez-Freire et al. 2018) Human aging generally results in lower mitochondrial function.(Tonkonogi et al. 2003; Lanza et al. 2008; Porter et al. 2015) There are close links between muscle mass and mitochondrial energetics including reduced ATP(Romanello et al. 2010) and increased ROS generation.(Kavazis et al. 2009; Min et al. 2011) ROS produced in aged muscle results in proteolytic degradation (proteasome system) and energetic stress, leading to reduced muscle mass. (Jang et al. 2010) If muscle mitochondria fail to provide sufficient ATP, the cells choose either growth or somatic maintenance, and this results in the disruption of proteostasis equilibrium.(Hou 2013)

Because miRNAs are very competitive therapeutic molecules, our findings of miRNAs regulating mitochondrial function-related genes could be used in strategies for recovering the muscle atrophy of patients with age-related sarcopenia.(van Rooij et al. 2012) A recent clinical trial indicated that specific inhibitors of miRNAs called anti-miR compounds represented potential drugs for incurable diseases(Chakraborty et al. 2017), and our study showed a new approach to treating sarcopenia, which remains a challenging muscle disorder.

The present study found that several miRNAs (hsa-miR-378a-5p, hsa-miR-532-5p, hsa-miR-127-3p, hsa-miR-24-2-5p, has-miR-487b-5p, and has-miR-450a-5p) regulating target genes (COX7A1, NDUFB5, COX7B, PDHA1, TIMM8A, and CS) shows significant expression change in aged people. The bioinformatics analysis showed that the controlling miRNAs regulating mitochondrial function-related genes could be used in strategies for treating sarcopenia by regulating the mRNA-miRNA network. It is supposed that this mRNA-miRNA network could be the cause of sarcopenia with aging. These results suggest that the controlling miRNAs found could be used in strategies for treating sarcopenia by regulating the mRNA-miRNA network.

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board (IRB) of Gyeongsang National University Hospital (IRB number: 2019-02-013).

Data availability

The datasets generated and/or analyzed during the current study are available in the [GEO data, (GSE8479 and GSE1428] repository, [http://www.ncbi.nlm.nih.gov/geo].

Competing interests

None declared by all authors.

Funding

This work was funded by the by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2019R1F1A1059208). The National Research Foundation of Korea had no role in design or analysis of the project, or the writing of this article.

Contributions

Concept – Jun-Il Yoo, Sangyeob Lee; Design - Jun-Il Yoo, Sangyeob Lee; Supervision - Jun-Il Yoo; Materials – Sangyeob Lee, Sung Hyo Seo; Data Collection and/or Processing - Sangyeob Lee, Sung Hyo Seo; Analysis and/or Interpretation - Jun-Il Yoo, Sangyeob Lee; Literature Search - Sangyeob Lee, Sung Hyo Seo; Writing Manuscript - Sangyeob Lee; Critical Review - Jun-Il Yoo

Acknowledgments

Not Applicable

Bartel DP (2018) Metazoan MicroRNAs. Cell 173:20–51. https://doi.org/10.1016/j.cell.2018.03.006
Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297. https://doi.org/10.1016/s0092-8674(04)00045-5
Chakraborty C, Sharma AR, Sharma G, et al (2017) Therapeutic miRNA and siRNA: Moving from Bench to Clinic as Next Generation Medicine. Mol Ther Nucleic Acids 8:132–143. https://doi.org/10.1016/j.omtn.2017.06.005
Coen PM, Jubrias SA, Distefano G, et al (2013) Skeletal muscle mitochondrial energetics are associated with maximal aerobic capacity and walking speed in older adults. J Gerontol A Biol Sci Med Sci 68:447–455. https://doi.org/10.1093/gerona/gls196
Doherty TJ (2003) Invited review: Aging and sarcopenia. J Appl Physiol (1985) 95:1717–1727. https://doi.org/10.1152/japplphysiol.00347.2003
Emelyanova L, Preston C, Gupta A, et al (2018) Effect of Aging on Mitochondrial Energetics in the Human Atria. J Gerontol A Biol Sci Med Sci 73:608–616. https://doi.org/10.1093/gerona/glx160
Fried LP, Hadley EC, Walston JD, et al (2005) From bedside to bench: research agenda for frailty. Sci Aging Knowledge Environ 2005:pe24. https://doi.org/10.1126/sageke.2005.31.pe24
Giuliani A, Cirilli I, Prattichizzo F, et al (2018) The mitomiR/Bcl-2 axis affects mitochondrial function and autophagic vacuole formation in senescent endothelial cells. Aging (Albany NY) 10:2855–2873. https://doi.org/10.18632/aging.101591
Gonzalez-Freire M, Scalzo P, D’Agostino J, et al (2018) Skeletal muscle ex vivo mitochondrial respiration parallels decline in vivo oxidative capacity, cardiorespiratory fitness, and muscle strength: The Baltimore Longitudinal Study of Aging. Aging Cell 17:. https://doi.org/10.1111/acel.12725
Goyns MH, Charlton MA, Dunford JE, et al (1998) Differential display analysis of gene expression indicates that age-related changes are restricted to a small cohort of genes. Mech Ageing Dev 101:73–90. https://doi.org/10.1016/s0047-6374(97)00166-8
Greenlund LJS, Nair KS (2003) Sarcopenia--consequences, mechanisms, and potential therapies. Mech Ageing Dev 124:287–299. https://doi.org/10.1016/s0047-6374(02)00196-3
Hackl M, Brunner S, Fortschegger K, et al (2010) miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging. Aging Cell 9:291–296. https://doi.org/10.1111/j.1474-9726.2010.00549.x
Hou C (2013) The energy trade-off between growth and longevity. Mech Ageing Dev 134:373–380. https://doi.org/10.1016/j.mad.2013.07.001
Ibáñez-Ventoso C, Yang M, Guo S, et al (2006) Modulated microRNA expression during adult lifespan in Caenorhabditis elegans. Aging Cell 5:235–246. https://doi.org/10.1111/j.1474-9726.2006.00210.x
Jang YC, Lustgarten MS, Liu Y, et al (2010) Increased superoxide in vivo accelerates age-associated muscle atrophy through mitochondrial dysfunction and neuromuscular junction degeneration. FASEB J 24:1376–1390. https://doi.org/10.1096/fj.09-146308
Jozsi AC, Dupont-Versteegden EE, Taylor-Jones JM, et al (2000) Aged human muscle demonstrates an altered gene expression profile consistent with an impaired response to exercise. Mech Ageing Dev 120:45–56. https://doi.org/10.1016/s0047-6374(00)00178-0
Kavazis AN, Talbert EE, Smuder AJ, et al (2009) Mechanical ventilation induces diaphragmatic mitochondrial dysfunction and increased oxidant production. Free Radic Biol Med 46:842–850. https://doi.org/10.1016/j.freeradbiomed.2009.01.002
Khatri B, Seo D, Shouse S, et al (2018) MicroRNA profiling associated with muscle growth in modern broilers compared to an unselected chicken breed. BMC Genomics 19:683. https://doi.org/10.1186/s12864-018-5061-7
Kujoth GC, Hiona A, Pugh TD, et al (2005) Mitochondrial DNA mutations, oxidative stress, and apoptosis in mammalian aging. Science 309:481–484. https://doi.org/10.1126/science.1112125
Lanza IR, Short DK, Short KR, et al (2008) Endurance Exercise as a Countermeasure for Aging. Diabetes 57:2933–2942. https://doi.org/10.2337/db08-0349
Larsson L, Grimby G, Karlsson J (1979) Muscle strength and speed of movement in relation to age and muscle morphology. J Appl Physiol Respir Environ Exerc Physiol 46:451–456. https://doi.org/10.1152/jappl.1979.46.3.451
Liang R, Bates DJ, Wang E (2009) Epigenetic Control of MicroRNA Expression and Aging. Curr Genomics 10:184–193. https://doi.org/10.2174/138920209788185225
Min K, Smuder AJ, Kwon O-S, et al (2011) Mitochondrial-targeted antioxidants protect skeletal muscle against immobilization-induced muscle atrophy. J Appl Physiol (1985) 111:1459–1466. https://doi.org/10.1152/japplphysiol.00591.2011
Mühlberg W, Sieber C (2004) Sarcopenia and frailty in geriatricpatients: Implications for training andprevention. Z Gerontol Geriat 37:2–8. https://doi.org/10.1007/s00391-004-0203-8
Mullighan CG, Goorha S, Radtke I, et al (2007) Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446:758–764. https://doi.org/10.1038/nature05690
Nie M, Deng Z-L, Liu J, Wang D-Z (2015) Noncoding RNAs, Emerging Regulators of Skeletal Muscle Development and Diseases. Biomed Res Int 2015:676575. https://doi.org/10.1155/2015/676575
Porter C, Hurren NM, Cotter MV, et al (2015) Mitochondrial respiratory capacity and coupling control decline with age in human skeletal muscle. American Journal of Physiology-Endocrinology and Metabolism 309:E224–E232. https://doi.org/10.1152/ajpendo.00125.2015
Rippo MR, Olivieri F, Monsurrò V, et al (2014) MitomiRs in human inflamm-aging: a hypothesis involving miR-181a, miR-34a and miR-146a. Exp Gerontol 56:154–163. https://doi.org/10.1016/j.exger.2014.03.002
Romanello V, Guadagnin E, Gomes L, et al (2010) Mitochondrial fission and remodelling contributes to muscle atrophy. EMBO J 29:1774–1785. https://doi.org/10.1038/emboj.2010.60
Schmidt M, Schüler SC, Hüttner SS, et al (2019) Adult stem cells at work: regenerating skeletal muscle. Cell Mol Life Sci 76:2559–2570. https://doi.org/10.1007/s00018-019-03093-6
Starling RD, Ades PA, Poehlman ET (1999) Physical activity, protein intake, and appendicular skeletal muscle mass in older men. Am J Clin Nutr 70:91–96. https://doi.org/10.1093/ajcn/70.1.91
Tonkonogi M, Fernström M, Walsh B, et al (2003) Reduced oxidative power but unchanged antioxidative capacity in skeletal muscle from aged humans. Pflugers Arch 446:261–269. https://doi.org/10.1007/s00424-003-1044-9
van Rooij E, Purcell AL, Levin AA (2012) Developing microRNA therapeutics. Circ Res 110:496–507. https://doi.org/10.1161/CIRCRESAHA.111.247916
Welle S, Bhatt K, Thornton CA (2000) High-abundance mRNAs in human muscle: comparison between young and old. J Appl Physiol (1985) 89:297–304. https://doi.org/10.1152/jappl.2000.89.1.297
Welle S, Brooks AI, Delehanty JM, et al (2003) Gene expression profile of aging in human muscle. Physiological Genomics 14:149–159. https://doi.org/10.1152/physiolgenomics.00049.2003
Yasuda N, Glover EI, Phillips SM, et al (2005) Sex-based differences in skeletal muscle function and morphology with short-term limb immobilization. J Appl Physiol (1985) 99:1085–1092. https://doi.org/10.1152/japplphysiol.00247.2005
Hydrogen peroxide stimulates ubiquitin-conjugating activity and expression of genes for specific E2 and E3 proteins in skeletal muscle myotubes | American Journal of Physiology-Cell Physiology. https://journals.physiology.org/doi/full/10.1152/ajpcell.00129.2003. Accessed 17 May 2021

No competing interests reported.

TableS1..xlsx
Supplemental Table 1. Gene analysis of GSE8479 and GSE1428 by imaGEO.
TableS2..xlsx
Supplemental Table 2. MiRNAs analysis of GSE23527 by G2R.
TableS3..xlsx
Supplemental Table 3. Genes (8017) selected for regulated genes by De-miRNAs.

Download PDF

Version 2

posted

You are reading this latest preprint version

Integrative analyses of genes and miRNAs associated with age-related sarcopenia

Status:

Version 2

Abstract

Figures

Introduction

Materials And Methods

2.1. Microarray Data Source

2.2. Data Processing and Pathway Enrichment Analysis of GSE8479 and GSE1428

2.3. Protein-Protein Interaction Network Analysis and Hub Gene Identification

2.4. Prediction of Potential miRNA and Target Genes

2.5. Statistics

Results

3.1. Identification and Enrichment of Differentially Expressed Genes in Young and Older Muscle Samples

3.2 PPI Network and Module Analysis

3.3 Prediction of Potential miRNAs and Target Genes

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 2