Common Muscle Metabolic Signatures Highlight Arginine and Lysine Metabolism as Potential Therapeutic Targets to Combat Unhealthy Aging - PubMed (original) (raw)
Common Muscle Metabolic Signatures Highlight Arginine and Lysine Metabolism as Potential Therapeutic Targets to Combat Unhealthy Aging
Janina Tokarz et al. Int J Mol Sci. 2021.
Abstract
Biological aging research is expected to reveal modifiable molecular mechanisms that can be harnessed to slow or possibly reverse unhealthy trajectories. However, there is first an urgent need to define consensus molecular markers of healthy and unhealthy aging. Established aging hallmarks are all linked to metabolism, and a 'rewired' metabolic circuitry has been shown to accelerate or delay biological aging. To identify metabolic signatures distinguishing healthy from unhealthy aging trajectories, we performed nontargeted metabolomics on skeletal muscles from 2-month-old and 21-month-old mice, and after dietary and lifestyle interventions known to impact biological aging. We hypothesized that common metabolic signatures would highlight specific pathways and processes promoting healthy aging, while revealing the molecular underpinnings of unhealthy aging. Here, we report 50 metabolites that commonly distinguished aging trajectories in all cohorts, including 18 commonly reduced under unhealthy aging and 32 increased. We stratified these metabolites according to known relationships with various aging hallmarks and found the greatest associations with oxidative stress and nutrient sensing. Collectively, our data suggest interventions aimed at maintaining skeletal muscle arginine and lysine may be useful therapeutic strategies to minimize biological aging and maintain skeletal muscle health, function, and regenerative capacity in old age.
Keywords: aging; diet; exercise; lifestyle; metabolic signatures; metabolomics; skeletal muscle.
Conflict of interest statement
The authors declare no conflict of interest.
Figures
Figure 1
The skeletal muscle metabolome in relation to age, diet, and lifestyle. (A) Scheme of experimental design. Figure elements modified from SMART (Servier Medical Art), licensed under a Creative Common Attribution 3.0 Generic License.
accessed on 9 June 2021. (B) Classification of 427 detected metabolites. The number of metabolites per class is given in parentheses.
Figure 2
The skeletal muscle metabolome reflects changes associated with age. (A) Bodyweight, muscle weight (gastrocnemius), muscle weight relative to bodyweight (BW), and glycemia after a 4 h fast of young (2-month) and old (21-month) male C57BL/6N mice (mean ± SEM; n = 5; *** p < 0.0001, ** p = 0.0011, n.s. not significant, Student’s _t_-test). (B) Principal component analysis of the entire metabolomics data set. (C) Heatmap of metabolites selected by random forest shown for individual samples. Metabolites labeled with an asterisk have not been confirmed with a standard, but we are confident of their identity. (D) Classification of random forest selected metabolites to their metabolite class. The number of metabolites per class is given in parentheses. (E) Quantitative enrichment analysis showing the top enriched pathways with FDR < 0.01 based on the metabolites selected by random forest.
Figure 3
The skeletal muscle metabolome reflects changes associated with diet. (A) Intraperitoneal glucose tolerance test (ipGTT) performed one week prior to sac in the afternoon, after a 6 h fast in seven-month-old C57BL/6J male mice on a low fat diet (LFD) or a high fat diet (HFD) (mean ± SEM; n = 6; * p < 0.05, *** p < 0.001, 2-way ANOVA with Bonferroni correction; diet effect F = 26.60, p < 0.0004; time effect F= 39.06, p = 0.0001; diet × time interaction F = 7.82, p < 0.0001). (B) Bodyweight, muscle weight (gastrocnemius), muscle weight relative to bodyweight (BW), and glycemia after a 4 h fast (mean ± SEM; n = 5; *** p < 0.0001, * p < 0.05, Student’s _t_-test). (C) Principal component analysis of the entire metabolomics data set. (D) Heatmap of metabolites selected by random forest shown for individual samples. Metabolites labeled with an asterisk have not been confirmed with a standard, but we are confident of their identity. (E) Classification of random forest selected metabolites according to metabolite class. The number of metabolites per class is shown in parentheses. (F) Quantitative enrichment analysis showing the top enriched pathways with FDR < 0.05 based on the metabolites selected by random forest.
Figure 4
The skeletal muscle metabolome reflects changes associated with lifestyle. (A) Bodyweight, muscle weight (gastrocnemius), muscle weight relative to bodyweight (BW), and glycemia at zeitgeber time 12 (ZT12, 6 pm) of male C57BL/6J mice after five weeks of daily endurance training (1 h on a motorized treadmill) and eating a chow diet, or sedentary littermates after three months eating a high fat diet (HFD) (mean ± SEM; n = 4; ** p < 0.01, * p < 0.05, n.s. not significant, Student’s _t_-test). (B) Principal component analysis of the entire data set. (C) Heatmap of metabolites selected by random forest shown for individual samples. Metabolites labeled with an asterisk have not been confirmed with a standard, but we are confident of their identity. (D) Classification of random forest selected metabolites to their metabolite class. The number of metabolites per class is given in parentheses. (E) Quantitative enrichment analysis showing the top enriched pathways with FDR < 0.01 based on the metabolites selected by random forest.
Figure 5
Common muscle metabolite signature distinguishes ‘desirable’ from ‘undesirable’ metabolic states. (A) Principal component analysis of all 427 metabolites in all three data sets. (B) Principal component analysis of 50 common metabolites identified by meta-analysis. (C) Heatmap of 50 common metabolites identified by meta-analysis. Group averages are presented. Metabolites labeled with an asterisk have not been confirmed with a standard, but we are confident of their identity. Metabolites localized in mitochondria are labelled red. (D,E) Classification of metabolites decreased (D) or increased in the ‘undesirable’ metabolic state (E) according to the super pathway (outer circle) and the sub pathway (inner circle). The number of metabolites per class is given in parentheses.
Figure 6
Relationships between metabolites associated with healthy and unhealthy aging trajectories and aging hallmarks.
References
- United Nations . World Population Prospects 2019: Highlights. UN; New York, NY, USA: 2019. Statistical, Papers; United Nations (Ser. A), Population and Vital Statistics Report.
- World Health Organization . World Report on Ageing and Health. World Health Organization; Geneva, Switzerland: 2015.
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