Molecular identification of succinate dehydrogenase complex subunit B in sarcopenia

doi:10.21203/rs.3.rs-3800225/v1

Download PDF

Research Article

Molecular identification of succinate dehydrogenase complex subunit B in sarcopenia

https://doi.org/10.21203/rs.3.rs-3800225/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

We aim to investigate the roles of succinate dehydrogenase complex subunit B (SDHB) in the pathogenesis of sarcopenia. We identified 1554 differentially expressed genes from 33824 background genes in sarcopenia versus non-sarcopenia control as well as SDHB-low group versus high group. The 1365 genes of the turquoise module were most strongly associated with sarcopenia and those with low SDHB expression, which were enriched in oxidative phosphorylation, thermogenesis, ribosome and tricarboxylic acid (TCA) cycle. The cross-talking pathways of SHDB in sarcopenia were determined, including oxidative phosphorylation, TCA cycle and thermogenesis. Low expressions of SDHB may be a potential pathogenic factor of sarcopenia, and may be related to oxidative phosphorylation, TCA cycle and thermogenesis.

Sarcopenia

SDHB

Oxidative phosphorylation

TCA cycle

Thermogenesis

As a common disease in the elderly, sarcopenia is characterized by age-related loss of muscle mass, reduced muscle strength, and/or low physical performance which may lead to functional decline, disability, frailty, and falls[1, 2]. According to the report of European Working Group on Sarcopenia in Older People (EWGSOP), the prevalence of sarcopenia in 60–70 years elderly is 5–13%, while the prevalence increases from 11 to 50% in those over 80 years of age[3, 4]. Sarcopenia eventually leads to loss of independence, elevates risk of morbidity and mortality, and becomes a massive economic burden. Despite these medical and socioeconomic costs, effective treatment options are still lacking, in part because research into the mechanisms of sarcopenia remains challenging[5]. Therefore, much effort will direct genomic identification as a new target, which may be the best method for early diagnosis and therapy of sarcopenia.

Sarcopenia is considered to be the result of an imbalance in protein synthesis and degradation. The mechanistic target of rapamycin complex 1 (mTORC1) plays an important role in controlling protein synthesis in muscle tissues and acts as a nutrient and growth factor response mediator for protein synthesis in skeletal muscle[6]. Genetic variation in mitochondrial alh-6/ALDH4A1 is considered as a new biomarker of muscle health associated with human age[7]. Succinate dehydrogenase (SDH), located in the inner mitochondrial membrane, is the only tricarboxylic acid (TCA) cycle enzyme that acts as complex II linking the TCA to the electron transport chain (ETC) [8]. Succinate dehydrogenase complex subunit B (SDHB) is a subunit of the SDH family, which consists two catalytic subunits, SDHA and SDHB and two membrane-spanning subunits, SDHC and SDHD[9, 10]. Some studies have shown that SDH dysfunction is associated with deletion of the SDHA or SDHB genes and mutations in SDHB, SDHC and SDHD[11, 12]. However, the mechanism of SDHB in the progression of sarcopenia is unclear. Our comprehensive genomic analysis of SDHB based on gene expression data and functional annotation in sarcopenia was performed to elucidate the potential role of SDHB in the pathogenesis of skeletal sarcopenia.

Data Resources

RNA expression data and clinical characteristics of human skeletal muscle biopsy specimens from 20 sarcopenic patients and 20 non-sarcopenic controls from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database in the GSE111016 profile group[13]. The two groups of patients were matched by age and sex according to the clinical data recorded in the previous study[14]. The mean age was 72.75 ± 4.18 years (range: 66–78 years) for sarcopenia and 70.2 ± 4 years (range: 65–79) for non- sarcopenia. This data set was produced on the GPL16791 platform using the Illumina HiSeq 2500 arrays. A gene corresponding to multiple probes retained the genes with the highest expression values. Gene expression data were normalized using the normalize between array function in the limma package of R software version 3.6.2[15].

Gene Set Enrichment Analysis (GSEA)

The biological process of screening gene ontology terms that may be related to sarcopenia and low SDHB expression through GSEA analysis[16, 17]. The packages of ggplot2, ClusterProfler, GSEABase, enrichplot were utilized to visualize enrichment data in GSEA analysis.1000 permutations were performed using default weight statistics, and the normalized P < 0.05 threshold was used for significant enrichment.

Identification of Differentially Expressed Genes (DEGs)

By adopting the limit and Bayes functions of the limma package, DEGs were created for the sarcopenia/control and SDHB-low/high groups, respectively. The included samples were dichotomized into SDHB-low and high cohorts according to the average expression value of SDHB as the cut-off point. In the analysis of differences in gene expression, a false discovery rate (FDR) adjusted for P < 0.05 and logFC (fold change) > 0.095 was considered statistically significant[15, 18].

Weight Gene Correlation Network Analysis (WGCNA)

WGCNA performed unbiased gene co-expression analysis of overlapping EDGs in the sarcopenia/control and SDHB low/high groups. The advantage of WGCNA was that it transformed complex data of gene expression into co-expressed gene modules, providing an in-depth understanding of the signal networks that may be responsible for the phenotypic features of interest[19]. It could contribute to comparing DEGs, but it also help identify gene co-expression modules specific to the disease[20]. A clustering tree was drawn to exclude outliers from the sample using the hclust function. The pick soft threshold function was used to filter the soft threshold and determine the appropriate power of 6 to keep the independence higher than 0.85 in the process of module construction. Using the WGCNA package to construct co-expression modules with different color labels and group non-clustered genes into gray modules[21]. For the reliability of the result, the minimum number of genes was set to 30 in each module. Functional enrichment analysis was performed using the cluster analysis software package to screen genes enriched in the Kyoto Encyclopedia of genes and genomes (KEGG) pathway. The enrichment of FDR < 0.05 was considered as statistically significant.

Construction of Global Regulatory Network and Cross-Talking Pathways of SDHB

Scatterplots of the membership modules (MM) and gene significance (GS) relationships were plotted using the verbose Scatterplot function, representing intra-module connectivity and genetic phenotypes, respectively[22]. The most relevant modules for building a global regulatory network was based on the STRING database (a search tool for retrieving interacting genes, https://www.string-db.org/)[23]. And then we used cytoscape software to visualize the global regulatory network as also as the cross-talking pathways of SDHB[24].

Analysis of Area Under the Curve (AUC)

Using pROC function to estimate the performance of SDHB in differentiating sarcopenia from non- sarcopenia. All P values were bilateral, and those less than 0.05 were considered statistically significant. An AUC value of 50% generally indicated a random selection, and a complete prediction was represented by 100%.

Identification of Differentially Expressed Genes

The conceptual level roadmap is shown in Fig. 1. In sarcopenia patients the mean expressions of SDHB (11.60 ± 0.21) were significantly lower than those in non-sarcopenia controls (11.84 ± 0.31; P < 0.01) according to the gene data of all samples (Fig. 2A). After removing the unannotated and duplicated genes, we finally selected a total of 33824 background genes in the differential expression analysis. Among them, 3144 DEGs were established in sarcopenia patients compared with the non-sarcopenia control group, of which 2725 were down-regulated and 419 were up-regulated (Fig. 2b). However, 5943 DEGs were identified in subjects with low and high expression of SDHB, of which 2848 were down-regulated and 2645 were up-regulated (Fig. 2C). There were 1554 overlapping DEGs in the sarcopenia /non-sarcopenia control group and the SDHB low/high group. The top 25 down-regulated and up-regulated overlapping DEGs associated with sarcopenia and low SDHB expression were presented as heat map (Fig. 2D).

Co-Expression Modules and Functional Enrichment Analysis

Our sample clustering results showed that all samples passed the predetermined cut-offs and were included in some clusters (Fig. 3A). WGCNA predicted four different colored modules based on the expression data of DEGs associated with SDHB and sarcopenia (Fig. 3B). The blue, brown and turquoise modules were composed of 85, 71 and 1365 DEGs, respectively. As shown in Fig. 3C, turquoise module was negatively correlated with sarcopenia (correlation coefficient = -0.52, P = 7e-04) and positively correlated with SDHB expression (correlation coefficient = 0.87, P = 8e-13), while the blue and brown modules both had a negative correlation with sarcopenia (blue: correlation coefficient = − 0.46, P = 0.003; brown: correlation coefficient = − 0.37, P = 0.02) and positive correlation with SDHB expression (blue: correlation coefficient = 0.55, P = 2e-04; brown: correlation coefficient = 0.66, P = 4e- 06). It was revealed by functional enrichment analyses (Fig. 3D) that the DEGs of the turquoise module were involved in KEGG pathways of oxidative phosphorylation, thermogenesis, ribosome and TCA cycle. The turquoise and brown modules DEGs were enriched in peroxisome; the DEGs of the blue module and the brown module participated in ras signaling pathway, PI3K-AKt signaling pathway and mitophagy, glutathione metabolism respectively.

Global Regulation Network and AUC Analysis of SDHB

It was found that the intramodular connectivity within the turquoise module most closely correlated with the genetic phenotypes in the scatter plot of the relationships between GS and MM (correlation coefficient = 0.77, P < 1e-200) (Fig. 4A).The cross-talking pathways of SDHB, such as oxidative phosphorylation, TCA cycle and thermogenesis were identified, and all the genes enriching these pathways were shown in Fig. 4C. AUC analysis (AUC = 74.9%) was used to measure the performance evaluation of SDHB in predicting sarcopenia (Fig. 4D).

GESA Verification in Biological Processes

The gene set enrichment analysis showed that biological processes of cellular respiration, cornification, keratinization, keratinocyte differentiation, mitochondrial gene expression, mitochondrial translation, oxidative phosphorylation, respiratory electron transport chain, translational elongation, translational termination were significantly enriched in sarcopenia patients compared to non-sarcopenia controls (Fig. 5A). Likewise, cellular respiration, cornification, electron transport chain, keratinization, mitochondrial gene expression, mitochondrial translation, oxidative phosphorylation, respiratory electron transport chain, translational elongation, translational termination was enriched in SDHB-low expression group versus high expression group significantly (Fig. 5B).

Herein, 40 subjects involving 33,824 genes were analyzed to investigate the relationship between SDHB and the incidence of sarcopenia in our study. The results of GSEA revealed that DEGs in both sarcopenia/non-sarcopenia control and SDHB-low/high cohorts enriched biological processes of cellular respiration, cornification, mitochondrial gene expression, mitochondrial translation, oxidative phosphorylation, respiratory electron transport chain, translational elongation, translational termination. It was reported that in NIH-3T3 fibroblasts, mitochondrial membrane potential, cellular respiration, proliferation, and mitochondrial architecture were associated with sarcopenia[25]. Also accumulation of mitochondrial (mtDNA) alterations in myofibres activates regeneration during ageing, mitochondrial oxidative phosphorylation and mitochondrial dynamics ultimately leading to sarcopenia[26–28]. SDH is a key enzyme in the tricarboxylic acid cycle and oxidative phosphorylation of the respiratory chain. The lack of SDH inhibits the tricarboxylic acid cycle and the transmission of electrons in the respiratory chain, resulting in the production of ROS and oxidative damage to cells[8, 11]. SDHB genes are normal or not will affect the function of SDH, which will eventually lead to excessive production of ROS in mitochondria[29]. Study has shown that deletion of the SDHB genes and point mutations of SDHB is associated with SDH dysfunction and increased production of reactive oxygen species (ROS) [12]. Of particular note, these processes may be associated with sarcopenia and low expression of SDHB. After that, the regulatory network and co-expression module of DEGs related to SDHB were constructed to further exploring, which could deepen the understanding of SDHB into the genome scale pathogenesis in sarcopenia.

Also in our results functional enrichment analysis showed that the turquoise module exhibited the greatest negative correlation with sarcopenia and positive correlation with SDHB in line with the GSEA results, hence supporting the involvement of DEGs in oxidative phosphorylation, thermogenesis, ribosome and TCA cycle. More specifically, TCA and oxidative phosphorylation took part in ROS and reactive nitrogen species (RNS) induced muscle atrophy[30] showing that TCA cycle and oxidative phosphorylation are involved in the pathogenesis of sarcopenia. Also the DEGs in the blue module and the brown module participated in KEGG pathway of ras signaling pathway, PI3K-AKt signaling pathway and mitophagy ,glutathione metabolism, supporting the fundamental process for sarcopenia development. Among them, Ras-extracellular signal-regulated kinase (ERK) pathway played critical roles in the inhibition of myocyte differentiation and muscle regeneration, which leads to muscle atrophy[31], also the aging phenotype was associated with repression of muscle-specific genes and activation of the epidermal growth factor receptor (EGFR)-Ras- ERK pathway[32]. And the PI3K/Akt pathway is known to regulate muscle protein synthesis and degradation[33]. Deactivation of the PI3K/Akt pathway inhibits protein synthesis and promotes protein degradation. It causes an imbalance in muscle mass maintenance and induces muscle loss[34]. An essential surveillance mechanism targeting defective and harmful mitochondria to degradation is the selective form of autophagy called mitophagy[35]. Impaired mitophagy is a primary pathogenic event underlying diverse aging-associated diseases such as sarcopenia[36].

The analysis of cross-talking pathways revealed that SDHB was jointly involved in oxidative phosphorylation, TCA cycle and thermogenesis. Less is known about the association of SDHB with thermogenesis. Brown adipose tissue (BAT) contains high mitochondrial content and generates heat via uncoupling protein 1 (UCP1)[37]. Uncoupling was essential for heat production in skeletal muscle and BAT[38]. In Kim’s research, the thermogenic effect in the skeletal muscle of the USP21-KO animals seemed to be at least partly to mitochondrial uncoupling activity[39]. Mitochondrial oxidative phosphorylation was a critical regulator of skeletal muscle mass and function. Muscle atrophy due to mitochondrial dysfunction was closely associated with the bone loss[27]. A comparison of the muscle transcriptome in sarcopenia men versus age‐matched controls from Singapore, Hertfordshire UK, and Jamaica demonstrated that the major transcriptional signature of sarcopenia was mitochondrial bioenergetic dysfunction in skeletal muscle, with down‐regulation of oxidative phosphorylation genes. Oxidative phosphorylation and mitochondrial energy homeostasis were the most perturbed biological processes associated with sarcopenia in all ethnicities, with bioenergetic alterations spanning across all mitochondrial respiratory complexes both at the level of expression and activity, also the activity of the two mitochondrial TCA cycle enzymes citrate synthase and SDH were strongly reduced in sarcopenia muscle, confirming that a global alteration of oxidative metabolism and energy production was perturbed in human sarcopenia muscle[40]. The unique property of SDH was that it participates in the oxidative phosphorylation pathway and the TCA cycle and thus has dual functions in carbon metabolism and mitochondrial respiration[41, 42]. To understand the consequences of SDHB deficiency on gene expression, a whole genome expression analysis was performed, notably, genes involved in glycolysis, hypoxia, cell proliferation, and cell differentiation were up regulated in this analysis, whereas genes involved in oxidative phosphorylation were down regulated[43]. And the other study showed that AMP-activated protein kinase (AMPK) was a known energy sensor and regulates the rate of protein synthesis rate phosphorylating mTORC1[6], SDHB silencing increased AMPK phosphorylation and enhanced levels of p‐p38 and p‐Hsp27[9]. The TCA cycle is an integral part of aerobic respiration, generating ATP and precursors for building macromolecules and reducing agents that decrease oxidative stress. SDH was the only TCA cycle enzyme connecting the TCA to the ETC as complex II[9]. SDH mediated the oxidation of succinate in the TCA cycle, which was coupled to the reduction of ubiquinone to ubiquinol in the electron transfer chain[8],and the up-regulation of SDH activity marked the acceleration of TCA and the increase of ATP[44]. TCA cycle regulator and oxidative phosphorylation gene sets were repressed in sarcopenic muscle with highly significant FDRs[40].

The turquoise module with the highest degree of correlation coefficient validated the strongest interaction between its DEG and SDHB expression according to the scatter plot between MM and GS. Subsequently, DEGs of the turquoise module were displayed in the global regulatory network to identify the cross-talking pathways of SDHB, which supported the pleiotropic roles of SDHB in sarcopenia pathophysiology responsible for oxidative phosphorylation[40], TCA cycle[40, 44] and thermogenesis. Because of the low expression of SDHB, the vulnerability of related pathways may be very obvious, leading to sarcopenia under the comprehensive pathogenic effect. The diagnostic performance of SDHB showed that the AUC value was 74.9%, suggesting a possible use of SDHB as a predictor of sarcopenia prevalence. Studies using reverse transcription polymerase chain reaction or Western blotting or a combination of both for gene expression confirmation are lacking. However, previous study using Western immunoblotting revealed that circulating small extracellular vesicles from 11 older adults with physical frailty and sarcopenia participants had lower amounts of adenosine triphosphate 5A, ubiquinone oxidoreductase subunit S3 and SDHB[45]. Future experiments are needed to validate the presented mechanisms of SDHB in sarcopenia.

In conclusion, the results from the current study supported that bioinformatic analysis was a promising approach to elucidate the complex pathway of pathogenesis of sarcopenia. The low expression of SDHB played an important role in the pathogenesis of sarcopenia and may be related to oxidative phosphorylation, TCA cycle and thermogenesis.

EWGSOP European Working Group on Sarcopenia in Older People

mTORC1 mechanistic target of rapamycin complex 1

SDH succinate dehydrogenase

SDHB succinate dehydrogenase complex subunit B

TCA tricarboxylic acid

ETC electron transport chain

WGCNA weight gene correlation network analysis.

AUC area under the curve

DEGs differentially expressed genes

GEO Gene Expression Omnibus

GS gene significance

GSEA gene set enrichment analysis

KEGG Kyoto Encyclopedia of Genes and Genomes

MAPK mitogen-activated protein kinase

MM module membership

mtDNA mitochondrial

ROS reactive oxygen species

ERK extracellular signal-regulated kinase

RNS reactive nitrogen species

UCP1 uncoupling protein 1

BAT Brown adipose tissue

Acknowledgements: Not applicable.

Authors’ contributions: FQ Chen contributed to conceptualization, formal analysis, writing the original draft, and review and editing of the paper. ZK Zhou contributed to conceptualization, formal analysis, investigation, methodology, project administration, supervision, review and editing of the paper. XW Yang contributed to investigation and methodology. YR Zheng contributed to investigation and methodology. All the authors contributed to the interpretation of the data and the critical revision and approval of the article. All authors read and approved the final manuscript.

Funding: Not applicable.

Availability of data and materials: The data-set generated and analyzed during the current study will be available from the corresponding author on a reasonable request.

Conflicts of Interest:The authors declare no conflict of interest

Ethics approval and consent to participate: Not Applicable.

Consent for publication：All authors support submission to this journal.

Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 21(3), 300–307 e302 (2020).
Chen F, Xu S, Wang Y, Chen F, Cao L, Liu T, Huang T, Wei Q, Ma G, Zhao Y et al. Risk Factors for Sarcopenia in the Elderly with Type 2 Diabetes Mellitus and the Effect of Metformin. J Diabetes Res, 3950404 (2020).
Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 39(4),412–423 (2010).
Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(1),16–31 (2019).
Giza S, Mojica-Santiago JA, Parafati M, Malany LK, Platt D, Schmidt CE, Coen PM, Malany S. Microphysiological system for studying contractile differences in young, active, and old, sedentary adult derived skeletal muscle cells. Aging Cell, e13650 (2022).
Higashida K, Inoue S, Nakai N. Iron deficiency attenuates protein synthesis stimulated by branched-chain amino acids and insulin in myotubes. Biochem Biophys Res Commun 531(2), 112–117 (2020).
Villa O, Stuhr NL, Yen CA, Crimmins EM, Arpawong TE, Curran SP. Genetic variation in ALDH4A1 is associated with muscle health over the lifespan and across species. Elif 11 (2022).
Fogarty MJ, Marin Mathieu N, Mantilla CB, Sieck GC. Aging reduces succinate dehydrogenase activity in rat type IIx/IIb diaphragm muscle fibers. J Appl Physiol (1985) 128(1), 70–77 (2020).
Saxena N, Beraldi E, Fazli L, Somasekharan SP, Adomat H, Zhang F, Molokwu C, Gleave A, Nappi L, Nguyen K et al. Androgen receptor (AR) antagonism triggers acute succinate-mediated adaptive responses to reactivate AR signaling. EMBO Mol Me 13(5):e13427 (2021).
Wu P, Chen J, Chen J, Tao J, Wu S, Xu G, Wang Z, Wei D, Yin W. Trimethylamine N-oxide promotes apoE(-/-) mice atherosclerosis by inducing vascular endothelial cell pyroptosis via the SDHB/ROS pathway. J Cell Physiol 35(10), 6582–6591 (2020).
Lei D, Hong T, Li L, Chen L, Luo X, Wu Q, Liu Z. Isobaric tags for relative and absolute quantitation-based proteomics analysis of the effect of ginger oil on bisphenol A-induced breast cancer cell proliferation. Oncol Lett 21(2), 101 (2021).
Goffrini P, Ercolino T, Panizza E, Giache V, Cavone L, Chiarugi A, Dima V, Ferrero I, Mannelli M. Functional study in a yeast model of a novel succinate dehydrogenase subunit B gene germline missense mutation (C191Y) diagnosed in a patient affected by a glomus tumor. Hum Mol Genet 18(10), 1860–1868 (2019).
Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, Zhang B, Zhang C, Lamb JR, Xie T et al. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol 10, 743 (2014).
Migliavacca E, Tay SKH, Patel HP, Sonntag T, Civiletto G, McFarlane C, Forrester T, Barton SJ, Leow MK, Antoun E, et al. Mitochondrial oxidative capacity and NAD + biosynthesis are reduced in human sarcopenia across ethnicities. Nat Commun. 20;10(1), 5808 (2019).
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7), e47 (2015).
Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34(3), 267–273 (2003).
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43), 15545–15550 (2005).
Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15(2), R29 (2014).
Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci U S A 103(46), 17402–17407 (2006).
Wan Q, Tang J, Han Y, Wang D. Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma. Exp Eye Res 166, 13–20 (2018).
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Fredlund E, Staaf J, Rantala JK, Kallioniemi O, Borg A, Ringner M. The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition. Breast Cancer Res 14(4), R113 (2012).
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45(D1), D362-D368 (2017).
Otasek D, Morris JH, Boucas J, Pico AR, Demchak B. Cytoscape Automation: empowering workflow-based network analysis. Genome Biol 20(1), 185 (2019).
Balasubramanian P, Schaar AE, Gustafson GE, Smith AB, Howell PR, Greenman A, Baum S, Colman RJ, Lamming DW, Diffee GM et al. Adiponectin receptor agonist AdipoRon improves skeletal muscle function in aged mice. Elife 11 (2022).
Kimoloi S, Sen A, Guenther S, Braun T, Brugmann T, Sasse P, Wiesner RJ, Pla-Martin D, Baris OR. Combined fibre atrophy and decreased muscle regeneration capacity driven by mitochondrial DNA alterations underlie the development of sarcopenia. J Cachexia Sarcopenia Muscle (2022).
Tian J, Chung HK, Moon JS, Nga HT, Lee HY, Kim JT, Chang JY, Kang SG, Ryu D, Che X et al. Skeletal muscle mitoribosomal defects are linked to low bone mass caused by bone marrow inflammation in male mice. J Cachexia Sarcopenia Muscle 13(3), 1785–1799 (2022).
Dulac M, Leduc-Gaudet JP, Cefis M, Ayoub MB, Reynaud O, Shams A, Moamer A, Nery Ferreira MF, Hussain SN, Gouspillou G. Regulation of muscle and mitochondrial health by the mitochondrial fission protein Drp1 in aged mice. J Physiol 599(17), 4045–4063 (2021).
Guzy RD, Sharma B, Bell E, Chandel NS, Schumacker PT. Loss of the SdhB, but Not the SdhA, subunit of complex II triggers reactive oxygen species-dependent hypoxia-inducible factor activation and tumorigenesis. Mol Cell Biol 28(2), 718–731 (2008).
Blottner D, Capitanio D, Trautmann G, Furlan S, Gambara G, Moriggi M, Block K, Barbacini P, Torretta E, Py G et al. Nitrosative Redox Homeostasis and Antioxidant Response Defense in Disused Vastus lateralis Muscle in Long-Term Bedrest (Toulouse Cocktail Study). Antioxidants (Basel) 10(3) (2021).
Masuzawa R, Takahashi K, Takano K, Nishino I, Sakai T, Endo T. DA-Raf and the MEK inhibitor trametinib reverse skeletal myocyte differentiation inhibition or muscle atrophy caused by myostatin and GDF11 through the non-Smad Ras-ERK pathway. J Biochem 171(1), 109–122 (2022).
Choi JY, Hwang CY, Lee B, Lee SM, Bahn YJ, Lee KP, Kang M, Kim YS, Woo SH, Lim JY et al. Age-associated repression of type 1 inositol 1, 4, 5-triphosphate receptor impairs muscle regeneration. Aging (Albany NY) 8(9), 2062–2080 (2016).
Vainshtein A, Sandri M. Signaling Pathways That Control Muscle Mass. Int J Mol Sci 21(13) (2020).
Shin JE, Jeon SH, Lee SJ, Choung SY. The Administration of Panax Ginseng Berry Extract Attenuates High-Fat-Diet-Induced Sarcopenic Obesity in C57BL/6 Mice. Nutrients 14(9) (2022).
Gambarotto L, Metti S, Chrisam M, Cerqua C, Sabatelli P, Armani A, Zanon C, Spizzotin M, Castagnaro S, Strappazzon F et al. Ambra1 deficiency impairs mitophagy in skeletal muscle. J Cachexia Sarcopenia Muscle (2022).
Srivastava V, Zelmanovich V, Shukla V, Abergel R, Cohen I, Ben-Sasson SA, Gross E. Distinct designer diamines promote mitophagy, and thereby enhance healthspan in C. elegans and protect human cells against oxidative damage. Autophagy 1–31 (2022).
Mi L, Zhao XY, Li S, Yang G, Lin JD. Conserved function of the long noncoding RNA Blnc1 in brown adipocyte differentiation. Mol Metab 6(1), 101–110 (2017).
Betz MJ, Enerback S. Targeting thermogenesis in brown fat and muscle to treat obesity and metabolic disease. Nat Rev Endocrinol 14(2), 77–87 (2018).
Kim A, Koo JH, Jin X, Kim W, Park SY, Park S, Rhee EP, Choi CS, Kim SG. Ablation of USP21 in skeletal muscle promotes oxidative fibre phenotype, inhibiting obesity and type 2 diabetes. J Cachexia Sarcopenia Muscle 12(6), 1669–1689 (2021).
Migliavacca E, Tay SKH, Patel HP, Sonntag T, Civiletto G, McFarlane C, Forrester T, Barton SJ, Leow MK, Antoun E et al. Mitochondrial oxidative capacity and NAD(+) biosynthesis are reduced in human sarcopenia across ethnicities. Nat Commun 10(1), 5808 (2019).
Van Vranken JG, Na U, Winge DR, Rutter J. Protein-mediated assembly of succinate dehydrogenase and its cofactors. Crit Rev Biochem Mol Biol 50(2), 168–180 (2015).
Lay S, Pearce X, Sanislav O, Fisher PR, Annesley SJ. Cytopathological Outcomes of Knocking down Expression of Mitochondrial Complex II Subunits in Dictyostelium discoideum. Int J Mol Sci 23(9) (2022).
Tabebi M, Kumar Dutta R, Skoglund C, Soderkvist P, Gimm O. Loss of SDHB Induces a Metabolic Switch in the hPheo1 Cell Line toward Enhanced OXPHOS. Int J Mol Sci 23(1) (2022).
Xu X, Dou D. The ginseng's fireness is associated with the lowering activity of liver Na(+)-K(+)-ATPase. J Ethnopharmacol 190, 241–250 (2016).
Picca A, Beli R, Calvani R, Coelho-Junior HJ, Landi F, Bernabei R, Bucci C, Guerra F, Marzetti E. Older Adults with Physical Frailty and Sarcopenia Show Increased Levels of Circulating Small Extracellular Vesicles with a Specific Mitochondrial Signature. Cells 9(4) (2020).

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Molecular identification of succinate dehydrogenase complex subunit B in sarcopenia

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data Resources

Gene Set Enrichment Analysis (GSEA)

Identification of Differentially Expressed Genes (DEGs)

Weight Gene Correlation Network Analysis (WGCNA)

Construction of Global Regulatory Network and Cross-Talking Pathways of SDHB

Analysis of Area Under the Curve (AUC)

Results

Identification of Differentially Expressed Genes

Co-Expression Modules and Functional Enrichment Analysis

Global Regulation Network and AUC Analysis of SDHB

GESA Verification in Biological Processes

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1