Molecular and Functional Networks Linked to Sarcopenia Prevention by Caloric Restriction in Rhesus Monkeys

Abstract

Caloric restriction (CR) improves survival in nonhuman primates and delays the onset of age-related morbidities including sarcopenia, which is characterized by the age-related loss of muscle mass and function. A shift in metabolism anticipates the onset of muscle-aging phenotypes in nonhuman primates, suggesting a potential role for metabolism in the protective effects of CR. Here, we show that CR induced profound changes in muscle composition and the cellular metabolic environment. Bioinformatic analysis linked these adaptations to proteostasis, RNA processing, and lipid synthetic pathways. At the tissue level, CR maintained contractile content and attenuated age-related metabolic shifts among individual fiber types with higher mitochondrial activity, altered redox metabolism, and smaller lipid droplet size. Biometric and metabolic rate data confirm preserved metabolic efficiency in CR animals that correlated with the attenuation of age-related muscle mass and physical activity. These data suggest that CR-induced reprogramming of metabolism plays a role in delayed aging of skeletal muscle in rhesus monkeys.

Keywords: caloric restriction; metabolic networks; rhesus monkeys; sarcopenia; skeletal muscle aging.

Conflict of interest statement

Declaration of Interests The authors declare no competing interests.

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1.. Gene Expression Analysis of mRNA and miRNA from Skeletal Muscle

(A) Volcano plot displaying identified probes from the microarray (n = 5 or 8,control or CR, respectively). Probes passing an uncorrected p value threshold of p </P>.</P>(B) PAGE analysis revealed GO terms that were rank ordered based on Z score and manually categorized as proteolysis related, gene expression related, immune and/or inflammation related, or mitochondria and/or energy metabolism related</P>. </P>(C) BAM analysis yielded 358 statistically significant genes between control and CR, which were non-exclusively categorized via GOSlim terms</P>.</P>(D) Ingenuity pathway analysis revealed the top 10 up- and downregulated analysis-ready molecules, which were categorized as related to gene expression regulation, lipid and fatty acid metabolism, noncoding RNA, detoxification, or miscellaneous</P>.</P>(E) Comparison of aggregate expression of microRNAs grouped together either due to genome location or miR family (n = 5 or 8, control or CR, respectively) (box, interquartile range; whiskers, minimum and maximum values; and bar, median)</P>. </P>(F) Network plot of selected miR members of chr7, miR-493–5p, and miR-4103p and putative targets present among the statistically significant transcripts changing as a result of CR. Edge length is an inverted, weighted score composite of transcript fold change and cumulative weighted context++ (TargetScan), describing the strength of the miR-target interaction. Orange targets are congruent in predicted direction with the direction of expression fold change of the miR (downregulated miRs = upregulated targets), whereas incongruent targets are blue and transparent. Targets are classified by function: growth, cytoskeletal and/or contractile function, Wnt signaling, or RNA processing. See also Figure S2 and Tables S1 and S2.

Figure 2.. Small-Molecule Indices of Metabolic Changes

</P>(A) Schematic depicting the details of the fluorescence imaging of NAD(P)H; shown are a theoretical energy diagram (left) and fluorescence decay curve (right) that describe the excitation and emission of photons from the NAD(P)H molecule that forms the basis for the technique</P>.</P>(B) Sections of vastus lateralis prepared from control and CR rhesus monkeys were imaged using excitation wavelengths of 780 nm. Pulsed laser excitation and high-discrimination photon counting were used to generate aggregate decay curves from which fluorescent lifetime was calculated (n = 4 or 7, control or CR; 3 images per animal, representative images are shown). The weighted average of both short and long decay components (tm) in picoseconds were color mapped according to the scale shown. Scale bar, 50 μm</P>.</P>(C) Individual components of the exponential decay function for NAD(P)H in tissues from control (n = 4, 3 images per animal) and CR (n = 7, 3 images per animal) animals are shown in boxplots (box, interquartile range; whiskers, minimum and maximum values; bar, median; and small box, mean) (*p < 0.05)</P>.</P>(D) The total photon intensity at 780 nm (NAD(P)H). Boxplot is same as in (C)</P>.</P>(E) Representative intensity image highlighting the presence of the bright ring phenotype (left). Quantification (right)</P>.</P>(F) Representative images of control (left) and CR (right) muscle stained with oil red O. Scale bar, 50 μm</P>.</P>(G) Total stain intensity quantification</P>. </P>(H) Quantification of the total number of droplets (mean ± SEM; n = 5 for control or 8 for CR)</P>.</P>(I) Distribution of droplets into defined size bins. Asterisk indicates bins for which there is a statistically significant difference (p < 0.05) between diet groups(mean ± SEM). See also Figure S3 and Table S3</P>.

Figure 3.. Histological Assessments of Skeletal Muscle Composition and Mitochondrial Activity

</P>(A) Representative images from control and CR muscle of histochemical stains for myosin isoform type I, cytochrome c oxidase, and myosin isoform type II. Equivalent fibers across adjacent stained sections are indicated as follows: a, pure type I; b, pure type II; and c, mixed myosin fibers (MMFs). Scale bar, 50 μm</P>.</P>(B) Quantification of fiber size distributions fortype I, II, and MMF (control n = 4 and CR n = 5; 6–16 fibers per type per animal) (box, interquartile range; whiskers, minimum and maximum values; bar, median, and small box, mean)</P>. </P>(C) Proportion of fibers at a given cytochrome c oxidase stain intensity for type I, II, and MMF (control n = 4 and CR n = 5; 6–16 fibers per type per animal)</P>. </P>(D) Assessment of mitochondrial activity and localization across fiber area. Left, 3D representation of cytochrome c oxidase staining across fiber area, highlighting heavier perimeter staining especially in CR animals. Right, quantification of cytochrome c oxidase intensity (control n = 4 and CR n = 5; 2 regions of each location type, 5 measurements per region, 1–5 fibers per image, 2–4 images per animal; total measurements n = 450 control and n = 710 CR) and VDAC (control n = 3, CR n = 4, 6 fibers per image, 2–4 images per animal; total measurements n = 108 control, n = 120 CR) in perimeter versus mid fiber. Boxplots are the same as in (B)</P>.</P>(E) Quantification (left) and representative images(right) of H&E staining of skeletal muscle highlighting the distribution of fiber contractile tissue and fibrotic non-contractile material (n = 5 control and n = 7 CR; 3–6 images per animal). Boxplots are the same as in (B). Scale bar, 200 μm See also Table S4.</P>.

Figure 4.. Whole-Body Measures of Muscle and Metabolism

</P>(A) Indices of skeletal muscle mass (n = 5 control and n = 8 CR; *p < 0.05): ESM at peak, ESM as a % of peak, UL at peak, and UL lean mass as a % of peak</P>. </P>(B) Linear regressions of skeletal muscle mass measures with metabolic activity measures; ESM % of peak versus metabolic cost of movement (left) and basal insulin versus ESM % of peak (right)</P>.</P>(C) Multiple factor analysis of biometric indices: ESM % of peak; UL % of peak; accelerometer activity counts for morning, afternoon, day, night, and 24 h; ESM; UL Lean; ESM peak; UL lean peak; % abdominal fat; % fat; body weight; total body lean mass; insulin sensitivity index; basal serum glucose; and basal serum insulin. Left, variable correlation plot depicting the relationship between each variable and the first two principal components. Right, individuals map depicting principal component scores for control and CR animals (n = 5 control and n = 8 CR). See also Figures S4 and S5 and Table S5</P>.