Research ArticleGUT MICROBIOTA

The gut microbiota influences skeletal muscle mass and function in mice

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Science Translational Medicine  24 Jul 2019:
Vol. 11, Issue 502, eaan5662
DOI: 10.1126/scitranslmed.aan5662

Of microbial metabolites and muscle

Skeletal muscle is important not only for locomotion but also for regulating metabolic function. Lahiri et al. studied the interactions between the gut microbiota and skeletal muscle in mice. They identified genes and signaling pathways involved in the regulation of skeletal muscle mass and function that responded to cues from the gut microbiota. Additional biochemical and functional analysis also revealed the influence of the gut microbiota on the function of neuromuscular junctions. These findings open the door to a better understanding of the role of the gut microbiota in the mechanisms underlying loss of muscle mass.

Abstract

The functional interactions between the gut microbiota and the host are important for host physiology, homeostasis, and sustained health. We compared the skeletal muscle of germ-free mice that lacked a gut microbiota to the skeletal muscle of pathogen-free mice that had a gut microbiota. Compared to pathogen-free mouse skeletal muscle, germ-free mouse skeletal muscle showed atrophy, decreased expression of insulin-like growth factor 1, and reduced transcription of genes associated with skeletal muscle growth and mitochondrial function. Nuclear magnetic resonance spectrometry analysis of skeletal muscle, liver, and serum from germ-free mice revealed multiple changes in the amounts of amino acids, including glycine and alanine, compared to pathogen-free mice. Germ-free mice also showed reduced serum choline, the precursor of acetylcholine, the key neurotransmitter that signals between muscle and nerve at neuromuscular junctions. Reduced expression of genes encoding Rapsyn and Lrp4, two proteins important for neuromuscular junction assembly and function, was also observed in skeletal muscle from germ-free mice compared to pathogen-free mice. Transplanting the gut microbiota from pathogen-free mice into germ-free mice resulted in an increase in skeletal muscle mass, a reduction in muscle atrophy markers, improved oxidative metabolic capacity of the muscle, and elevated expression of the neuromuscular junction assembly genes Rapsyn and Lrp4. Treating germ-free mice with short-chain fatty acids (microbial metabolites) partly reversed skeletal muscle impairments. Our results suggest a role for the gut microbiota in regulating skeletal muscle mass and function in mice.

INTRODUCTION

Skeletal muscle function is regulated by the central nervous system through neurotransmission at neuromuscular junctions (NMJs). NMJs are highly specialized chemical synapses formed between motor neurons and skeletal muscle fibers (1). Skeletal muscle displays marked plasticity, being able to respond to a variety of environmental cues, such as exercise and nutrition. Skeletal muscle is also the major site of insulin-stimulated glucose uptake and fatty acid oxidation emphasizing its key role in metabolism. Reduced skeletal muscle mass and function are associated with metabolic disorders (2) and sarcopenia (3), underscoring the role of skeletal muscle in maintenance of health.

It has been established that the gut microbiota influences host health in part due to its coevolvement with the host to meet mutually beneficial biochemical and biological needs (4). There are many studies investigating how the gut microbiota influences the liver and intestinal metabolism, immunity, and behavior (58). However, few studies have reported how the gut microbiota regulates skeletal muscle, one of the dominant metabolic organs in the body. Here, we present evidence from germ-free (GF) and pathogen-free (PF) mice that the gut microbiota influences skeletal muscle mass and function.

RESULTS

Altered skeletal muscle mass in GF mice

GF mice lacking a gut microbiota displayed reduced skeletal muscle weight compared to PF mice that had a gut microbiota (P < 0.01; Fig. 1A). Transplanting GF mice with the gut microbiota of pathogen-free mice [henceforth referred to as conventionalized GF (C-GF) mice] restored muscle mass in the transplanted C-GF animals (P < 0.05; Fig. 1A). Histological examination of the tibialis anterior, a fast oxidative muscle, revealed a trend toward fewer but larger muscle fibers (>2000- to 3000-μm2 cross-sectional fiber area) in GF mice compared to PF mice (fig. S1, A and B). In addition, reduced expression of the myosin heavy chain genes MyHCIIa, MyHCIIb, and MyHCIIx was observed in GF mouse tibialis anterior muscle compared to PF mouse tibialis anterior muscle (P < 0.05; Fig. 1B). A similar trend was also observed in the soleus, a slow oxidative muscle, and the extensor digitorum longus, a fast glycolytic muscle, in GF mice compared to PF mice (P < 0.01; fig. S1, C and D). Furthermore, we observed increased expression of Atrogin-1 and Murf-1 encoding E3 ubiquitin ligases, which are known to be involved in muscle atrophy, in the tibialis anterior muscle of GF mice compared to PF mice (P < 0.01; Fig. 1, C, E, and F). Reduced expression of Murf-1 and Atrogin-1 was observed in the anterior tibialis muscle of the transplanted C-GF animals in comparison to GF mice (P < 0.01; Fig. 1C). The expression of Atrogin-1 and Murf-1 is known to be regulated by FoxO transcription factors (9). Whereas elevated expression of FoxO3 was observed in the tibialis anterior muscle of GF mice in comparison to PF mice (P < 0.05; Fig. 1C), FoxO3 expression was normalized in the transplanted C-GF mice. The energy sensor adenosine 5′-monophosphate–activated protein kinase (AMPK) controls muscle fiber size by activating the FoxO-mediated protein degradation pathway (10). Further analysis of the tibialis anterior muscle from GF mice revealed an increase in phosphorylation of the AMPK catalytic domain (on Thr172) compared to PF mouse muscle (Fig. 1, E and F). It has been reported that Atrogin-1 expression regulates MyoD expression in skeletal muscle under atrophy conditions (11). We observed reduced expression of key muscle genes that regulate skeletal muscle differentiation, MyoD and Myogenin, in GF mouse skeletal muscle compared to PF mouse skeletal muscle (P < 0.01; Fig. 1D). Similar trends in increased expression of atrophy markers such as Atrogin-1 and Murf-1 with a concomitant down-regulation of MyoD expression were also observed in soleus (slow oxidative) muscle and extensor digitorum longus (fast glycolytic) muscle of GF mice compared to PF mice (P < 0.001; fig. S1, E to H).

Fig. 1 Skeletal muscle mass and function in GF mice.

(A) Weights of soleus, gastrocnemius, tibialis anterior (TA), quadriceps, and extensor digitorum longus (EDL) muscles from PF mice, GF mice, and C-GF mice. The number of mice used per experimental group is the following: Soleus muscle (PF, n = 13; GF, n = 14; C-GF, n = 10), gastrocnemius muscle (PF, n = 15; GF, n = 14; C-GF, n = 13), TA muscle (PF, n = 14; GF, n = 13; C-GF, n = 12), quadriceps muscle (PF, n = 15; GF, n = 14; C-GF, n = 13), and EDL muscle (PF, n = 13; GF, n = 12; C-GF, n = 13). (B) Shown are changes in expression of genes encoding myosin heavy chain (MyHC) isoforms in TA muscles of PF (n = 7), GF (n = 7), and C-GF (n = 10) mice. (C) Shown are changes in expression of Atrogin-1, Murf-1, and FoxO3 genes in TA muscles from PF (n = 7), GF (n = 7), and C-GF (n = 9) mice. (D) Shown are changes in expression of genes encoding the skeletal muscle–specific transcription factors MyoD and Myogenin in TA muscle samples from PF (n = 7), GF (n = 7), and C-GF (n = 9) mice. (E) Shown is immunoblot analysis of protein lysates from TA muscles harvested from PF, GF, and C-GF mice, indicating expression of Atrogin-1 (n = 4 mice per group), Murf-1 (n = 4 mice per group), and phosphorylated AMPK (p-AMPK; n = 5 mice per group). (F) The results in the histogram are expressed as the ratio of relative intensity of Atrogin-1 and Murf-1 protein expression normalized to tubulin as a loading control and the intensity of p-AMPK expression relative to total AMPK expression. Data are expressed as means ± SEM. Data were analyzed using ANOVA followed by Tukey’s post hoc test and were considered statistically significant at *P < 0.05, **P < 0.01, and ****P < 0.0001 between indicated groups.

Glucocorticoids are known to induce skeletal muscle atrophy under various pathological conditions (12). The transcription factor Kruppel-like factor 15 (KLF15) is one of the target genes activated by the glucocorticoid receptor and is involved in the regulation of several metabolic processes in skeletal muscle including the up-regulation of branched-chain aminotransferase 2 (BCAT2), which in turn induces the degradation of branched-chain amino acids (BCAAs) (13). A surge in serum corticosterone concentrations was observed in GF mice compared to PF mice (P < 0.05; Fig. 2A), along with up-regulation of Klf15 expression in tibialis anterior muscle of GF mice compared to PF mice (P < 0.01; Fig. 2B). The observed muscle atrophy in GF mice was also associated with activation of enzymes involved in the BCAA catabolic pathway. BCAAs are transaminated by BCAT2 to generate branched-chain α-keto acids, which in turn are subjected to oxidative decarboxylation by branched-chain α-keto acid dehydrogenase (BCKDH) to produce coenzyme A esters. BCKDH activity is negatively regulated by the BCKDH kinase (BCKDK). Increased gene expression of Bcat2 and Bckdh was observed in tibialis anterior muscle of GF mice compared to PF mice, whereas the expression of the inhibitor Bckdk was reduced, suggesting increased BCAA catabolism in skeletal muscle of GF mice (P < 0.05; Fig. 2C).

Fig. 2 Branched chain amino acid (BCAA) metabolism in skeletal muscle of GF mice.

(A) Shown are measurements of serum corticosterone concentrations in PF (n = 17), GF (n = 16), and C-GF (n = 10) mice. (B) Shown is expression of the Klf15 gene in TA muscles from PF (n = 7), GF (n = 7), and C-GF (n = 9) mice. (C) Shown are changes in expression of genes involved in BCAA catabolism (Bcat2, Bckdk, and Bckdh) in TA muscles of PF (n = 6), GF (n = 7), and C-GF (n = 9) mice. (D) Shown are changes in the expression of the genes Igf1 and Igf-binding proteins (Igfbps) in TA muscle of PF mice (n = 7), GF mice (n = 7), and C-GF (n = 9) mice. Data are expressed as means ± SEM. Data are analyzed using ANOVA followed by Tukey’s post hoc test and were considered statistically significant at *P < 0.05, **P < 0.01, and ***P < 0.001 between indicated groups.

Skeletal muscle mass is maintained by the balance between protein synthesis and protein degradation. To monitor a potential decline in the protein synthesis pathway, we assessed the Igf1-Akt-mTOR growth-promoting pathway. Whereas the expression of the insulin-like growth factor 1 gene (Igf1) was reduced in tibialis anterior muscle of GF mice compared to PF mice, Igf1 expression was normalized when GF mice were transplanted with the gut microbiota of pathogen-free mice (C-GF) (P < 0.01; Fig. 2D). The amount of Igf1 protein remained unaltered in the serum of GF mice (fig. S1I), suggesting additional Igf1 regulatory loops possibly involving Igf-binding proteins (IGFBPs) that are known to regulate Igf1 function. Of the six IGFBPs, we observed increased expression of the Igfbp3 gene in tibialis anterior muscle of GF mice compared to PF mice and C-GF mice (P < 0.01; Fig. 2D), in line with a previous observation (14). Increased expression of Igfbp3 is known to exert an inhibitory effect on skeletal muscle growth, suggesting one explanation for the reduced muscle mass of GF mice (15). However, the activation profile of Akt, the mammalian target of rapamycin (mTOR), and its downstream effector, the S6 ribosomal protein, were unaffected in GF mouse muscle (fig. S1, J and K).

Altered metabolism in skeletal muscle of GF mice

Given that skeletal muscle of GF mice showed signs of atrophy, we next investigated the oxidative metabolic capacity of skeletal muscle. Histological staining revealed reduced activity of the mitochondrial enzyme succinate dehydrogenase (SDH; Fig. 3A) and reduced expression of the Sdh gene (P < 0.05; Fig. 3) in GF mouse muscle compared to muscle of PF mice. Mitochondrial DNA content was also reduced in GF mouse muscle but was restored when GF mice were transplanted with the gut microbiota of pathogen-free mice (C-GF) (P < 0.05; Fig. 3C). In addition, we observed reduced gene expression of mitochondrial biogenesis markers, such as peroxisome proliferator–activated receptor γ coactivator 1α (Pgc1α) and mitochondrial transcription factor A (Tfam) in skeletal muscle of GF mice compared to PF mice (P < 0.05; Fig. 3D). Moreover, we noticed reduced expression of genes encoding different mitochondrial oxidative phosphorylation complexes in skeletal muscle of GF mice compared to PF mice, including genes encoding cytochrome oxidase subunits of complex IV (CoxVa, CoxVIIb, and CytC) known to be involved in the electron transport chain (P < 0.05; Fig. 3E). Similar observations supporting dysfunctional mitochondrial biogenesis and oxidative capacity were also observed in two other skeletal muscle subtypes, the soleus (oxidative) and extensor digitorum longus (glycolytic) muscle of GF mice compared to PF mice (P < 0.01; fig. S2, A to D). The protein expression profile of the different oxidative phosphorylation complexes of the electron transport chain (fig. S6A) was also altered in muscle from GF mice compared to PF mice. Despite a possible reduction in oxidative metabolic capacity, GF mice performed as well as PF mice when challenged to exercise until exhaustion (fig. S2E).

Fig. 3 Oxidative capacity of the skeletal muscle of GF mice.

(A) Representative images of TA muscle sections from PF, GF, and C-GF mice stained for the enzyme SDH. (B) Shown is expression of the Sdh gene in TA muscle from PF (n = 7), GF (n = 7), and C-GF (n = 9) mice. (C) Quantitative analysis of the ratio of mitochondrial DNA (mtDNA) to nuclear DNA in gastrocnemius muscles from PF (n = 4), GF (n = 5), and C-GF (n = 5) mice. (D and E) Shown are changes in expression of the Pgc1α and Tfam genes (D), and the CoxVa, CoxVIIb, and CytC genes (E) in TA muscles of PF (n = 6), GF (n = 7), and C-GF (n = 9) mice. (F) Shown are changes in expression of genes involved in glucose metabolism (Pfk, Pk, Ldh, and Pdh) in TA muscles of PF (n = 6), GF (n = 7), and C-GF (n = 9) mice. (G) Shown are changes in expression of genes involved in the fatty acid oxidation pathway (Lcad, Mcad, and Cpt1b) in TA muscles of PF (n = 7), GF (n = 7), and C-GF (n = 9) mice. (H) Shown is the amount of glycogen in quadriceps muscles of PF (n = 4), GF (n = 3), and C-GF (n = 4) mice. Data are expressed as means ± SEM. Data were analyzed using ANOVA followed by Tukey’s post hoc test and were considered statistically significant at *P < 0.05, **P < 0.01, and ***P < 0.001 between indicated groups.

These results prompted us to undertake further metabolic analyses in GF mice. Analysis of serum metabolic markers revealed reduced quantities of glucose and insulin in GF mice compared to PF mice (P < 0.05; fig. S2F). Further investigation of metabolic pathways for fatty acid and glucose metabolism in GF mouse muscle revealed reduced expression of glycolytic genes encoding the enzymes phosphofructokinase (Pfk), pyruvate kinase (Pk), lactate dehydrogenase (Ldh), and pyruvate dehydrogenase (Pdh) in tibialis anterior muscle from GF mice compared to PF mice (P < 0.05; Fig. 3F). Expression of the genes encoding these enzymes was restored when GF mice were transplanted with the gut microbiota of pathogen-free mice (C-GF) (P < 0.01; Fig. 3F). A similar trend of reduced expression of glycolytic genes was also observed in the soleus and extensor digitorum longus muscles of GF mice compared to PF mice (P < 0.01; fig. S2, G and H). No differences in expression of genes encoding medium-chain acyl–coenzyme A dehydrogenase (Mcad) and muscle-specific carnitine palmitoyltransferase 1b (mCpt1b) involved in fatty acid oxidation (Fig. 3G) or phosphorylation of acetyl–coenzyme A carboxylase (fig. S2, I and J) were observed in tibialis anterior muscle of GF mice compared to PF mice. Cholesterol and free fatty acids in serum also remained unaltered in GF mice compared to PF mice (fig. S2F). Further analysis of glucose uptake in skeletal muscles, using FDG-PET/MRI (fluorodeoxyglucose–positron emission tomography/magnetic resonance imaging) imaging, revealed no obvious differences in glucose uptake for the back and hindleg muscles of GF mice compared to PF mice (fig. S2, K and L). However, we observed an increased accumulation of glycogen in the quadriceps (fast glycolytic) muscles of GF mice compared to PF mice (P < 0.001; Fig. 3H), implying possible impaired utilization of glucose by the quadriceps muscle of GF mice.

To further corroborate our findings, we assessed whether disruption of the gut microbiota–host environment using antibiotics had effects on skeletal muscle. We monitored muscle samples from PF mice exposed to low-dose penicillin to examine consequences of disruption of host metabolic functions mediated by antibiotics through alterations in microbial community composition (16, 17). Whereas low-dose penicillin increased FoxO3 gene expression and reduced MyoD gene expression in skeletal muscle of PF mice (P < 0.05; fig. S3A), the expression of Atrogin-1, Murf-1, Pgc1α, and Tfam genes remained largely unaffected under these experimental conditions (fig. S3, A and B). However, electron transport chain genes (CoxVa, CoxVIIb, and CytC) were down-regulated in PF mice treated with low-dose penicillin compared to untreated animals (P < 0.05; fig. S3C), indicating reduced oxidative metabolic capacity in skeletal muscle.

Altered metabolites in skeletal muscle, liver, and serum of GF mice

To characterize the metabolic phenotype of GF mice in detail, we measured proton nuclear magnetic resonance (NMR) spectra at 600 MHz for the skeletal muscle, liver, and serum samples from GF, PF, and C-GF mice (Fig. 4). Cross-validated principal components analysis and orthogonal partial least squares analysis models revealed differences in 1H NMR metabolic profiles for the skeletal muscle, liver, and serum from GF mice compared to PF mice and GF mice compared to C-GF mice (fig. S4, A to C). Transplanting GF mice with gut microbiota of PF mice, as observed in C-GF mice normalized the metabolite profiles to those observed in PF mice.

Fig. 4 Metabolite analysis of the muscle, liver, and serum from GF mice.

(A to C) Shown is the average 1H NMR spectrum of hydrophilic phase after Folch extraction for 25 metabolites. The 1H NMR spectrum is shown for (A) gastrocnemius muscle from PF (n = 8), GF (n = 8), and C-GF (n = 10) mice; (B) liver tissue from PF (n = 7), GF (n = 8), and C-GF (n = 10) mice; (C) serum from PF (n = 8), GF (n = 8), and C-GF (n = 9) mice. 1, taurocholic acid; 2, bile acids; 3, low-density lipoprotein (LDL); 4, very-low-density lipoprotein (VLDL); 5, leucine; 6, 3-hydroxybutyrate; 7, alanine; 8, acetate; 9, glutamine; 10, glutamate; 11, pyruvate; 12, glutathione; 13, hypotaurine; 14, dimethylamine; 15, sarcosine; 16, trimethylamine; 17, dimethylglycine; 18, unknown (δ 3.11) (s); 19, choline; 20, glycerophosphorylcholine; 21, taurine; 22, betaine; 23, glycine; 24, unknown (δ 3.59) (d); and 25, unknown (δ 3.71) (s). Metabolites in red were found in higher concentrations in GF mice compared to C-GF or PF mice; metabolites in blue were found in lower concentrations in GF mice compared to C-GF or PF mice. s, singlet; d, doublet; a.u., arbitrary units.

Our metabolite profiling identified large quantities of amino acids such as alanine and glycine in skeletal muscle of GF mice (Fig. 4A and Table 1). The expression of the gene encoding alanine transaminase (Alt) that results in transamination of alanine was also increased in muscle of GF mice (P < 0.01; fig. S5A). Typically, the amino acids alanine and glycine, and their carbon skeletons, enter different metabolic pathways to generate energy. However, we did not observe any change in adenosine 5′-triphosphate (ATP) concentrations in the skeletal muscle of GF mice compared to PF mice or C-GF mice (fig. S5B). We next tested whether higher alanine concentrations in the muscle of GF mice served as a source for hepatic gluconeogenesis through the glucose-alanine shuttle. Reduced alanine (Fig. 4B and Table 1) along with low expression of the gene encoding glucose-6-phosphatase (G6Pase), a gluconeogenic marker, was observed in the liver of GF mice compared to PF mice (P < 0.05; fig. S5C).

Table 1 Differences in metabolite concentrations between GF, PF, and C-GF mice.

s, singlet; d, doublet; dd, doublet of doublets; t, triplet; q, quartet; m, multiplet.

View this table:

A large amount of glycine was observed in skeletal muscle of GF mice compared to PF mice (Fig. 4A and Table 1). Apart from a possible increase in protein degradation of muscle tissue, glycine can be derived from intermediates of glycolysis (18). However, no changes in expression of genes encoding key enzymes of this intermediate pathway, such as phosphoglycerate dehydrogenase (Phgdh) and serine hydroxymethyltransferases (Shmt1 and Shmt2) were observed in the muscle of GF mice (fig. S5D). Glycine can also be generated from choline via betaine, dimethylglycine, and sarcosine. Reduced quantities of choline and dimethylglycine along with higher amounts of glycine were found in the serum of GF mice compared to PF mice (Fig. 4C and Table 1). Intermediates in the choline metabolic pathway, glycerophosphocholine and betaine, were also reduced in the liver of GF mice (Fig. 4B and Table 1). However, no changes in expression of the genes encoding the enzymes involved in these intermediate steps: Choline dehydrogenase (Chdh), betaine homocysteine S-methyltransferase (Bhmt), Bhmt2, and sarcosine dehydrogenase (Sardh) were observed in the skeletal muscle of GF mice when compared to PF mice (fig. S5E).

The metabolite profile of the liver from GF mice was characterized by high taurine, hypotaurine, and tauro-conjugated bile acids (taurocholic acid), showing that bile acid metabolism was affected in GF mice compared to PF mice. We also observed increased dimethylamine, a product of choline degradation, in the liver of GF mice compared to PF mice (Fig. 4B and Table 1). Furthermore, glycerophosphocholine, betaine (trimethylglycine), and oxidized glutathione were also present in lower amounts in the liver of GF mice, indicating potential perturbation of the cysteine metabolic pathway although the amount of sarcosine remained high. In addition, glutamine, alanine, leucine, and valine along with glutamate were reduced in the liver of GF mice compared to PF mice. Pyruvate was lower in the liver of GF mice compared to PF mice. Expression profiling of genes encoding enzymes involved in mitochondrial oxidative and glucose metabolism revealed reduced expression of several genes involved in these pathways, indicating metabolic dysfunction in the liver of GF mice compared to PF mice (P < 0.01; fig. S5, F and G).

The amount of glycine was high in serum and muscle of GF mice compared to PF or C-GF mice (Fig. 4C and Table 1). However, the amounts of choline, trimethylamine, and dimethylglycine were lower in serum of GF mice compared to PF or C-GF mice. In addition, acetate, pyruvate, and 3-hydroxybutyrate were lower in serum of GF mice. We also observed differences in serum lipid profiles, with high amounts of very-low-density lipoproteins and low amounts of low-density lipoproteins in GF mice compared to PF mice (Fig. 4), in agreement with an earlier report (19). Overall, the metabolite data indicated disrupted energy homeostasis in GF mice compared to PF mice, with a marked perturbation of the amino acid metabolic pathway (Fig. 5).

Fig. 5 Multicompartment metabolic reaction network.

Metabolites are connected on the basis of the shortest paths of reactions that are mediated by enzymes encoded in the Mus musculus genome or on the basis of nonenzymatic reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Metabolites in orange were found in higher concentrations in GF mice compared to PF or C-GF mice. Conversely, metabolites in blue were found in lower concentrations in GF mice compared to PF and C-GF mice. Alanine was higher in the skeletal muscle (gastrocnemius) and lower in the liver. The background shading indicates the three different subnetworks for gastrocnemius muscle (purple), liver (green), and serum (pink). Overlap exemplifies similarity between affected metabolic compartments.

Altered expression of genes encoding NMJ proteins in GF mice

Given that we observed low quantities of choline in the serum of GF mice (Fig. 4C and Table 1) and that choline is a substrate for the derivation of the neurotransmitter acetylcholine and is essential for membrane integrity (20), we screened an array of molecules that affect NMJ development and function including acetylcholine receptors (21). We observed reduced expression of genes encoding different acetylcholine receptor subunits (Chrna1, Chrnb, Chrne, and Chrnd) in tibialis anterior muscle of GF mice compared to PF mice (P < 0.05; Fig. 6A), suggesting a potential impairment of acetylcholine receptor assembly. We therefore monitored the expression of genes associated with formation, maturation, and maintenance of NMJs, including Rapsyn, a 43-kDa receptor-associated synaptic protein (22) and the low-density lipoprotein receptor–related protein 4 (Lrp4), both reported to be important for the development and assembly of acetylcholine receptors in NMJs (23). Expression of both Rapsyn and Lrp4 genes was reduced in GF mice, and this change was reversed when GF mice were transplanted with the gut microbiota of PF mice (C-GF) (P < 0.05; Fig. 6B). Lrp4 associates with muscle-specific kinase (MuSK) to form a receptor complex necessary for agrin to bind (24). Whereas we noted elevated expression of the MuSK gene, no change in expression of the Agrn gene encoding agrin was observed in muscle of GF mice compared to PF mice (P < 0.05; Fig. 6B). The reduced expression of the gene encoding troponin in skeletal muscle of GF mice compared to PF mice and C-GF mice suggested possible impairment of myofiber contractility (P < 0.01; Fig. 6C). We therefore evaluated muscle strength in GF mice. GF mice displayed reduced muscle strength compared to PF mice when examined in a weights test (P < 0.05; Fig. 6D). GF mice also exhibited reduced locomotor and rearing activity compared to PF mice (P < 0.01 and P < 0.001; Fig. 6, E and F, respectively) and C-GF mice (P < 0.05 and P < 0.05; Fig. 6, E and F, respectively).

Fig. 6 Differential expression of mouse neuromuscular junction proteins between GF and PF mice.

(A) Shown are changes in expression of genes encoding acetylcholine receptor subunits (Chrn) in the TA muscle of GF, PF, and C-GF mice. Genes include Chrna1 (PF, n = 7; GF, n = 7; C-GF, n = 9), Chrnb (PF, n = 7; GF, n = 7; C-GF, n = 8), Chrnd (PF, n = 7; GF, n = 7; C-GF, n = 7), and Chrne (PF, n = 7; GF, n = 7; C-GF, n = 7). (B) Shown are changes in expression in TA muscle of PF, GF, and C-GF mice of genes encoding the receptor-associated protein of the synapse (Rapsyn; PF, n = 7; GF, n = 7; C-GF, n = 9), low-density lipoprotein receptor–related protein 4 (Lrp4), and Agrin (Agrn) (PF, n = 7; GF, n = 7; C-GF, n = 9). (C) Shown are changes in expression of the gene encoding fast-twitch troponin (Tnn) in TA muscle of PF (n = 6), GF (n = 7), and C-GF (n = 9) mice. (D) Analysis of hindlimb grip strength using the weights test in PF, GF, and C-GF mice (n = 6 per group). (E to F) Shown is the spontaneous activity of GF, PF, and C-GF mice in the open-field test measured by cumulative distance traveled (E) and cumulative vertical activities (F). PF (n = 8), GF (n = 9), and C-GF (n = 8) mice were monitored over a 2-hour period. All data are expressed as means ± SEM. Data were analyzed using ANOVA followed by Tukey’s post hoc test and were considered statistically significant at *P < 0.05, **P < 0.01, and ***P < 0.001 between indicated groups.

Bacterial metabolites influence skeletal muscle mass in GF mice

GF mice have elevated hypothalamic-pituitary–adrenal axis (HPA) activity as shown by increased serum corticosterone (Fig. 2A). We therefore used an established in vitro myotube culture model (25) to study the effects of microbial metabolites on muscle atrophy, induced by glucocorticoid treatment. Dexamethasone treatment induced Atrogin-1 expression in differentiated C2C12 myotubes in vitro (P < 0.01; Fig. 7A). A cocktail of short-chain fatty acids (SCFAs), such as those generated by microbial fermentation of dietary polysaccharides, reduced the effect of dexamethasone on Atrogin-1 expression (P < 0.01; Fig. 7A). We also observed that SCFAs enhanced the expression of Pgc1α and Tfam (P < 0.01; Fig. 7B) and CoxVa and CoxVIIb (P < 0.01; Fig. 7C) when administered to differentiated C2C12 myotubes in vitro. In addition, we monitored the effect of the SCFAs cocktail on both basal and maximal cellular respiration of C2C12 myotubes in vitro. Carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone (FCCP) stimulated respiration in mitochondria by uncoupling ATP synthesis from electron transport in C2C12 myotubes (fig. S6, B and C). Under FCCP-stimulation conditions, treatment with the SCFAs cocktail resulted in a decreased oxygen consumption rate (P < 0.01; fig. S6B) and an increased extracellular acidification rate (P < 0.001; fig. S6C) in C2C12 myotubes, implying a metabolic switch from oxidative phosphorylation to a glycolytic mode of energy production.

Fig. 7 Effects of bacterial metabolites on skeletal muscle of GF mice.

(A) Shown are the effects of SCFAs on dexamethasone (Dex)–induced muscle atrophy in C2C12 mouse myotubes in vitro. Differentiated myotubes were treated with a cocktail of SCFAs (10 mM) in the presence or absence of dexamethasone (1 mM) for 24 hours, and changes in expression of Atrogin-1 and Murf-1 were analyzed (n = 3 per group). For the control group, C2C12 myotubes were treated with solvent (0.1% dimethyl sulfoxide) only as vehicle. (B and C) Shown are the effects of SCFA treatment on the expression of genes encoding mitochondrial proteins in C2C12 myotubes in vitro. Differentiated myotubes were treated with a cocktail of SCFAs (10 mM) for 24 hours, and relative gene expression of (B) Pgc1α and Tfam (n = 3 per group) and (C) CoxVa, CoxVIIb, and CytC (n = 3 per group) was analyzed. (D) Shown are weights of soleus, gastrocnemius, TA, quadriceps, and EDL muscles from GF mice either untreated or treated with SCFAs (GF + SCFAs) (n = 6 mice per group). (E to G) Shown are changes in gene expression of Atrogin-1, Murf-1, and MyoD (E), Pgc1α and Tfam (F), and CoxVa, CoxVIIb, and CytC (G) in TA muscles from untreated and SCFA-treated GF mice (n = 6 per group). (H) Analysis of hindlimb grip strength in untreated and SCFA-treated GF mice using the weights test (n = 6 per group). (I) Changes in expression of Rapsyn, Lrp4, and Agrn genes in TA muscle from untreated and SCFA-treated GF mice (n = 6 mice per group). All data are expressed as means ± SEM. For (A), data were analyzed using ANOVA, followed by Tukey’s post hoc test. For all other panels, data were analyzed using two-tailed Student’s t test. Data were considered statistically significant at *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 between indicated groups.

We then treated GF mice in vivo with a cocktail of SCFAs. We observed a trend in increased skeletal muscle mass, particularly of the gastrocnemius muscle (P < 0.05; Fig. 7D), in SCFA-treated GF mice compared to untreated GF mice. Furthermore, in vivo treatment of GF mice with SCFAs reduced the expression of Atrogin-1 in tibialis anterior muscle (P < 0.0001; Fig. 7E), although the expression of Murf-1 was higher (P < 0.001; Fig. 7E). We also noted the increased expression of MyoD, a marker associated with myogenesis, in the tibialis anterior muscle of SCFA-treated GF mice compared to untreated GF mice (P < 0.05; Fig. 7E). However, we did not observe changes in expression of genes encoding the mitochondrial transcription factors Pgc1α and Tfam (Fig. 7F) or the electron transport chain proteins CoxVa, CoxVIIb, and CytC (Fig. 7G) in the tibialis anterior muscle of SCFA-treated GF mice compared to untreated GF mice. SCFAs treatment improved muscle strength of GF mice in the weights test compared to untreated GF mice (Fig. 7H). No alterations in expression of the NMJ-associated genes, Rapsyn and MuSK, were observed in tibialis anterior muscle of SCFA-treated GF mice compared to untreated GF mice (Fig. 7I).

DISCUSSION

Here, we show that the gut microbiota contributes to skeletal muscle mass and function in mice. GF mice displayed reduced muscle mass and signs of muscle atrophy indicated by increased expression of FoxO, Atrogin-1, Murf-1, and MyoD, and reduced muscle strength. Activation of AMPK in GF mouse muscle suggested that the AMPK-FoxO3-Atrogin cascade could be one possible signaling pathway to explain, at least in part, the atrophy observed in GF muscle tissues. Similar elevation in AMPK phosphorylation in gastrocnemius muscle of GF mice fed on a Western diet for 5 weeks was previously reported (26). In addition, our results suggested that alterations in IGF1 protein locally in the muscle of GF mice might have exerted effects on the muscle, rather than circulating IGF-1. The Akt-mTOR pathway appeared to be unaffected in muscle of GF mice. This suggested that protein degradation exceeded protein synthesis, which in part could contribute to the reduced skeletal muscle mass observed in GF mice. Our observations of increased corticosterone concentrations in serum, induction of Klf15 gene expression, and activation of the BCAA pathway indicated a possible HPA-glucocorticoid–driven atrophy of skeletal muscle mass in GF mice. Skeletal muscle atrophy observed in mice maintained under GF conditions thus might be the consequence of dysregulation in several signaling pathways controlling skeletal muscle mass. Reduced serum concentrations of choline, altered expression of NMJ-associated genes in GF mouse muscle, and defective locomotion and grip strength capacity are additional factors affecting muscle mass and function in GF mice. Furthermore, our observations from muscles of different metabolic subtypes suggested that the phenotypic changes observed in GF mice due to the absence of a gut microbiota were not unique to muscle with a specific metabolic characteristic but rather affected all types of skeletal muscle.

Gut microbes synthesize amino acids and make them available to the host (27). Analyses of the skeletal muscle, liver, and serum metabolites revealed altered amino acid metabolism in GF mice. Glycine and alanine were increased in the skeletal muscle of GF mice compared to PF mice. Glycine has important implications in skeletal muscle wasting (28), acts as an energy source for muscle (29), and is also induced in response to mitochondrial stress (30). BCAA catabolism, which is associated with skeletal muscle protein breakdown, correlated with low leucine and valine concentrations in liver tissue and serum of GF mice. Decreased quantities of BCAAs were observed in the liver and serum of GF mice and have been associated with severe catabolic conditions, such as cachexia in the terminal stages of cancer (31, 32). BCAAs not only serve as an alternate energy substrate but also improve nitrogen retention and protein synthesis (33). They represent the major nitrogen source for alanine synthesis in muscle. Before their use as fuel and being fed into the tricarboxylic acid cycle, BCAAs undergo a series of transamination steps eventually resulting in production of alanine. High quantities of alanine along with increased Alt gene expression observed in the skeletal muscle of GF mice thus might be a consequence of muscle protein breakdown resulting in increased bioavailability of amino acids to cope with the stress generated by the absence of a gut microbiota.

Many interactions between the host and its gut microbiota are mediated by SCFAs generated from the bacterial fermentation of dietary polysaccharides (3436). From our observations, SCFAs support, in part, skeletal muscle function by preventing atrophy and increasing muscular strength. However, the diverse biosynthetic activity of the gut microbiota beyond providing SCFAs to be used by the host as an energy source may explain why SCFA supplementation alone could not fully rescue the impaired muscle phenotype in GF mice. Microbes and their metabolites are likely to engage multiple converging pathways to regulate host muscle growth and function. Microbial products other than SCFAs might also be involved in skeletal muscle growth and function. Our study has elucidated several underlying molecular mechanisms supporting regulation of skeletal muscle mass and function in GF mice.

GF mice as an animal model for studying microbe-host interactions are an artificial system. Devoid of microbes these mice do, however, provide opportunities to study microbe-host interactions in vivo. Deficits in physiological and metabolic processes due to maintaining mice under GF conditions are well documented. The use of low-dose antibiotic treatment of PF mice provided support for our observations in GF mice that the lack of a gut microbiota correlated with reduced muscle mass and function. A recent study showing that mice treated with the antibiotic metronidazole displayed skeletal muscle atrophy and changes in expression of metabolic genes further supports our observations (37). However, the muscle tissue samples collected from the antibiotic treatment experiment were obtained from PF mice raised in a different animal facility, and hence, our findings should be interpreted cautiously. Although our observations also suggested that the communication between muscle and nerve cells at NMJs may be impaired under GF conditions, further experiments, including physiological experiments, are required to reach a definitive conclusion. Additional studies are also warranted to identify specific bacterially produced metabolites or other microbial products that influence skeletal muscle growth and function. In conclusion, we have demonstrated the existence of a gut microbiota–skeletal muscle axis that opens the way for further mechanistic and physiological studies that will lead to a better understanding of mechanisms regulating this important metabolic organ.

MATERIALS AND METHODS

Study design

The objective of this study was to elucidate the functional interactions between the gut microbiota and the host in the regulation of skeletal muscle mass and function. Skeletal muscle physiology was assessed in GF and PF mice, maintained on autoclaved R36 Lactamin chow (Lactamin), provided with sterile drinking water ad libitum, and kept on a 12-hour light/dark cycle. Skeletal muscle mass was examined, and differences in expression of genes encoding different cell signaling molecules that regulate muscle mass were characterized. Detailed metabolic characterization was also performed using NMR spectrometric analyses of the skeletal muscle, liver, and serum samples. The influence of the gut microbiota on NMJs was also examined by analyzing expression of genes encoding NMJ proteins and testing mouse muscle strength in the grip strength test for GF and PF mice. The gut microbiota’s role in muscle mass and function was further analyzed by transplanting GF mice with the gut microbiota of PF mice (C-GF) or by treating them with microbial metabolites (SCFAs). We examined the effects on skeletal muscle of disrupting the gut microbiota from PF mice using low-dose penicillin. Three independent cohorts of PF, GF, and C-GF mice were used with a minimum of five mice in each experimental group. Researchers were blinded to group allocation, and mice were randomized to groups.

Animals

GF and PF C57BL/6J male mice (6 to 8 weeks old) were raised in the Core Facility for GF Research, Karolinska Institutet, Sweden and Lee Kong Chian (LKC) School of Medicine GF Research facility, Singapore, in accordance with the regulatory standards of each institution. All experiments were approved by respective local ethical committees. GF mice were raised in special sterile isolators. Isolator sterility was analyzed weekly by plating fecal homogenates onto different types of agar plates to detect aerobic and anaerobic bacteria and fungi. To transplant GF with gut microbiota from PF mice, GF mice were conventionalized (C-GF) after weaning by gavage of 100 μl of homogenate generated by dissolving two fecal pellets from PF mice in 1 ml of phosphate-buffered saline (PBS). After gavage, C-GF mice were cohoused with PF mice for 21 days before they were euthanized. The control group was gavaged with sterile PBS. Mice were euthanized under isoflurane anesthesia. All animals were maintained on autoclaved R36 Lactamin chow (Lactamin), provided with sterile drinking water ad libitum, and kept under 12-hour light/dark cycles. Three independent cohorts of PF, GF, and GF mice transplanted with C-GF mice were used with a minimum of five mice in each group. The behavioral assays were conducted in accordance with national guidelines for the care and use of laboratory animals for scientific purposes, with approved protocols from the Institutional Animal Care and Use Committees of Duke–National University of Singapore (NUS) Graduate Medical School, Singapore.

SCFA treatment of GF mice

GF mice (6 to 8 weeks of age) were treated with a cocktail of SCFAs as per a previously published protocol (38). Briefly, mice (n = 6 per group) were fed a mix of SCFAs (67.5 mM sodium acetate, 40 mM sodium butyrate, and 25.9 mM sodium propionate) in their drinking water for a period of 4 weeks. No decrease in water intake was noted, and mice were monitored for signs of dehydration. Sterility of the GF and SCFA-treated GF cohort was assessed twice a week and maintained throughout this study. After 4 weeks, mice were euthanized by inhalation of isoflurane, and muscles were harvested. Tibialis anterior (TA) muscles were used for quantitative polymerase chain reaction (qPCR) analyses.

Antibiotic treatment

C57BL/6J mice were given low-dose penicillin (1 μg/g body weight) via drinking water or no antibiotics (control) for 4 weeks after weaning as previously reported (16).

Weights test

Weights tests were performed to measure muscular strength as described previously (39). Briefly, each mouse was held by the middle of the tail and slowly lowered to grasp the first weight (26 g) in a well-lit fume hood. Upon grasping the wire scale, mice were raised until the weight was fully raised above the bench. The criterion was met if the mouse could hold the weight for 3 s. If the weight was dropped in before 3 s, then the time was noted, and the mouse rested for 10 s before performing a repeat trial. Each mouse was allowed a maximum of five trials before being assigned the maximum time achieved; if it successfully held the weight for 3 s, then it was allowed to progress to the next heaviest weight. The apparatus comprised six weights, weighing 26, 33, 44, 63, 82, and 100 g. All cage mates of each group were tested using a given weight before progressing to the next heaviest weight. The total score for each mouse was then calculated as the product of the number of links in the heaviest chain held for the full 3 s, multiplied by the time (seconds) it was held.

Activity in the open-field task

Locomotor and rearing activities were monitored for individual mice using an automated Omnitech Digiscan apparatus (AccuScan Instruments, Columbus, OH, USA), as described previously (40). Mice were monitored in the open field for 60 min. Locomotion was measured as total distance traveled and rearing as vertical activity.

Treadmill running task

Eight-week-old mice were subjected to exercise on a treadmill (Columbus, OH, USA) using a previously reported exercise regimen (41). Mice in all groups were acclimated to moderate treadmill running (10 m/min for 15 min; inclined at 5 degrees) for 2 days. After acclimation, for the exercise test, mice ran on a treadmill set at a gradually increasing speed from 0 to 15 m/min and then maintained at a constant speed until exhaustion. Mice were removed from the treadmill upon exhaustion and had free access to food and water after the running protocol was completed. Mice were euthanized 3 hours after the exercise test.

Positron emission tomography/magnetic resonance imaging

Mice were fasted for 4 hours before the imaging session. They were imaged under anesthesia using a nanoScan PET/MRI scanner (Mediso, Hungary) under anesthesia (42). The whole-body MRI images were acquired with the 1-T MRI component of the nanoScan PET/MRI scanner. All images and data analyses for the FDG PET images were performed using PMOD (version 3.5; PMOD Technologies). PET acquisition: A 40-min-long three-dimensional (3D) static PET scan was performed after intravenous injection of FDG; the animals were moving freely during the waiting period. The injected radioactivity was 10.0 to 15 megabecquerels, and the maximum injected volume was 0.1 ml.

MRI acquisition. A 35-mm mouse whole-body coil was used for the MRI scan. Slices (0.8 mm) were obtained using a 2D T1 SD coronal sequence with 100-mm-square field of view, 256 × 256 matrix, 1034-ms repetition time, 12.4-ms echo time, and 90° flip angle.

Image analysis. The PET images were first automatically registered to the MRI images using the Fusion tool in PMOD. The MRI images were then used for the manual delineation of volumes of interest (VOIs). VOIs for hindleg muscle and back muscle were used for the analysis.

Cell culture and treatments

Murine C2C12 myoblasts (American Type Culture Collection, USA) were cultured and were considered myotubes after 96 hours of differentiation. Differentiated cells were then treated with 1 mM dexamethasone (Sigma-Aldrich, USA) for 24 hours, along with a cocktail of SCFAs, such as acetate, propionate, and butyrate, based on the molar ratio of their availability in the plasma (60:25:15 for acetate:propionate:butyrate) (43).

In vitro cellular respiration assay

Oxygen consumption in differentiated C2C12 cells in response to bacterial metabolites (i.e., SCFAs) was measured using XF96 equipment (Seahorse Bioscience), as described previously (44).

Metabolite analyses of skeletal muscle, liver, and serum

1H NMR spectroscopy was performed on the aqueous-phase extracts from the skeletal muscle (gastrocnemius), liver, and serum at 300 K on a Bruker 600-MHz spectrometer (Bruker BioSpin, Karlsruhe, Germany) as described in (45). Metabolic network was generated using the freely available MetaboNetworks software (46).

Sample treatment

Hydrophilic metabolites were extracted from the liver and skeletal muscle (gastrocnemius). Samples were homogenized (64,000 rpm for 2 min) in 7.5 ml of chloroform:methanol (2:1). The homogenate was combined with 1 ml of water, mixed by vortexing, and centrifuged at 13,000g for 5 min. The upper aqueous phase was separated from the lower organic phase and dried using nitrogen gas. Frozen serum samples (−80°C) were thawed and then centrifuged at 2700g for 10 min to remove particulates and precipitated proteins. A total of 300 μl of individual serum samples was prepared with pH 7.4 phosphate buffer, as described previously for high-resolution 1H NMR spectroscopy (45).

1H NMR metabolic profiling analysis

The hydrophilic phase of the liver and skeletal muscle was reconstituted in 540 μl of D2O and 60 μl of phosphate buffer (pH 7.4, 80% D2O) containing 1 mM of the internal standard, 3-(trimethylsilyl)-[2,2,3,3,-2H4]-propionic acid (TSP). 1H NMR spectroscopy was performed on the aqueous phase extracts at 300 K on a Bruker 600-MHz spectrometer (Bruker BioSpin, Karlsruhe, Germany) using the following standard 1D pulse sequence with saturation of the water resonance: relaxation delay (RD) – gz,1 – 90° – t – 90° – tm – gz,2 – 90° – acquisition time (ACQ), 90° represents the applied 90° radio frequency pulse, t1 is an interpulse delay set to a fixed interval of 4 μs; RD was 2 s, and tm (mixing time) was 100 ms (45). Water suppression was achieved through irradiation of the water signal during RD and tm. For the liver and muscle samples, each spectrum was acquired using four dummy scans followed by 32 scans and collected into 64K data points. A spectral width of 20,000 Hz was used for all samples. Before Fourier transformation, the free induction decays (FIDs) were multiplied by an exponential function corresponding to a line broadening of 0.3 Hz. 1H NMR spectra were manually corrected for phase and baseline distortions and referenced to the TSP singlet at δ 0.0. Spectra were digitized using an in-house MATLAB (version R2014a, MathWorks Inc.; Natwick, MA, USA) script. Spectra were subsequently referenced to the internal chemical shift reference (TSP) at δ 0.0. Spectral regions corresponding to the internal standard (δ −0.5 to 0.5) and water (δ 4.5 to 5.5) were excluded.

The serum samples were analyzed using 1H NMR spectroscopy with two different pulse sequences (45). The first pulse sequence, the so-called NOESY preset sequence, provides an overview of all proton-containing species and yields sharp peaks for small molecule species, broad bands from the lipoproteins (used later for lipoprofile analysis, see below), and a broad largely featureless background from proteins, the most abundant being albumin. The second pulse sequence, the so-called Carr-Purcell-Meiboom-Gill (CPMG) sequence, takes advantage of the different nuclear spin relaxation times between large and small molecules to attenuate the peaks from large molecules (those with shorter spin-spin relaxation times) to leave mainly small-molecule metabolite peaks. The latter have been used for the main analysis. Experiments were performed using a standard 1D 1H NMR and the CPMG pulse sequence [with the form RD-90°-(t-180°-t)n-ACQ] to attenuate peaks from larger and more slowly moving molecules (such as lipoproteins and lipids) with a view to identifying low–molecular weight metabolites. An RD of 4 s, a tm of 0.01 s, a spin-echo delay of 0.3 ms, 128 loops, and an FID ACQ of 3.067 s were applied. A total of 32 scans were recorded into 73K data points with a spectral width of 20 parts per million (ppm). Each spectrum was normalized to probabilistic quotient normalization. Multivariate statistical analysis based on pattern recognition techniques such as principal components analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) was performed with Pareto scaling in SIMCA-P+14 applied to all data variables. OPLS-DA was used to compare independently each dataset of 1H NMR metabolic profiles. The robustness of the models was evaluated on the basis of R2 (explained variance) and Q2 (capability of prediction) values as well as sevenfold cross-validation and class permutation validation.

Metabolite identification

Confirmation of NMR metabolite identities was obtained using 1D and 2D NMR experiments [spike-in of chemical standards, J-resolved spectroscopy (known as JRES), TOtal Correlation SpectroscopY (known as TOCSY), and heteronuclear single quantum coherence (known as HSQC) spectroscopy].

Histological assessment of skeletal muscle fiber morphology and oxidative capacity

For histological staining, serial cross sections (10 μm) were cut from the midpart of the tibialis anterior muscle, fixed in optimal cutting temperature (OCT) compound and frozen in isopentane cooled by liquid nitrogen. Hematoxylin and eosin staining was used for muscle fiber cross-sectional area measurements. The histochemical assay for SDH activity on the tibialis anterior muscle sections was used to distinguish between oxidative and nonoxidative fibers, as described previously (47).

Glycogen measurements in muscle

Glycogen content was estimated in quadriceps muscle tissues using a glycogen assay kit (BioVision, USA) and following the manufacturer’s instructions.

Serum analyses

Serum was prepared from blood collected and stored at −70°C until assayed using the Roche/Hitachi 902 robot system (Roche Diagnostics, Rotkreutz, Switzerland) following the manufacturer’s instructions.

Corticosterone estimation

Corticosterone was estimated in serum using the corticosterone enzyme-linked immunosorbent assay kit (Abcam) following the manufacturer’s instructions.

Gene expression analysis by real-time quantitative RT-PCR

Total RNA was extracted from cells and muscle tissues using TRIzol reagent following the manufacturer’s instructions. For SYBR Green–based quantitative reverse transcription PCR (qRT-PCR), isolated RNA was reverse-transcribed using SuperScript II and random primers (Invitrogen). qRT-PCR assays were performed in triplicate, and data were normalized to Rpl27 and Rpl13 and analyzed using qBase software. Details regarding gene-specific primers are provided in table S1.

Mitochondrial DNA copy number quantification

The relative copy number of mitochondrial DNA per nuclear DNA ratio was measured by qPCR.

Western blot analysis

Tissue extracts (50 μg per lane) were prepared in lysis buffer [10 mM tris-HCl (pH 8), 150 mM NaCl, 1% Triton X-100, and protease inhibitors and PhosSTOP (Roche)] and subjected to Western blotting. The Western blot was probed with anti–p-AMPK and anti-AMPK, p-ACC and ACC, p-mTOR and mTOR, p-S6 and S6 (Cell Signaling), and tubulin antibodies. Immunodetection with an appropriate secondary peroxidase-conjugated antibody (DAKO) was followed by chemiluminescence (Amersham). Protein band quantification was performed using LabImage software.

Statistical analysis

Statistical significance was determined using GraphPad Prism 7 statistical software. For all experiments that required analyses between multiple groups (GF, PF, and C-GF), one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was performed. Two-tailed Student’s t test was performed when observations between only two groups were compared. F test was performed before Student’s t test to determine equal variance between the two groups. P < 0.05 was considered statistically significant unless otherwise stated. Values are expressed as means ± SEM.

SUPPLEMENTARY MATERIALS

stm.sciencemag.org/cgi/content/full/11/502/eaan5662/DC1

Fig. S1. The gut microbiota affects skeletal muscle function in mice.

Fig. S2. The gut microbiota influences skeletal muscle oxidative capacity in mice.

Fig. S3. A subtherapeutic dose of antibiotics affects skeletal muscle mass and function in mice.

Fig. S4. Metabolite analyses in mouse muscle, liver, and serum.

Fig. S5. Effect of the gut microbiota on metabolic pathways in mice.

Fig. S6. SCFAs influence oxidative capacity of mouse skeletal muscle.

Table S1. List of primer sequences.

Data file S1. Source data for Figs. 1, 2, 3, 6, and 7.

REFERENCES AND NOTES

Acknowledgments: We thank V. Arulampalam and T. Mak for helpful suggestions. We also thank T. W. Ling, A. A. Amoyo-Brion, S. S. Wee, P. Wong, A. Samuelsson, and J. Aspsäter for technical assistance. We thank the Center for Germ-Free Research, Karolinska Institutet, Stockholm, Sweden; Mouse Metabolic Evaluation Facility, Genomics Technology Facility, Center for Integrative Genomics, Lausanne, Switzerland; and Duke-NUS Behavioral Phenotyping Core Facility, Duke-NUS vivarium, and SingHealth Experimental Medical Centre for technical help and support. Funding: This project was supported by grants from the Seventh European Union (EU) program TORNADO (S.P. and W.W.); the Swiss National Science Foundation (SNSF) (W.W. and J.A.); Fondation Suisse de Recherche sur les Maladies Musculaires and Fondation Marcel Levaillant (J.A.); Vetenskapsrådet, EU project “Molecular Targets Open for Regulation by the Gut Flora—New Avenues for Improved Diet to Optimize European Health,” Hjärnfonden, Mérieux Institute, and Singapore Millennium Foundation (S.P.); and the startup grant from the Lee Kong Chian School of Medicine, Nanyang Technological University (S.P. and W.W.). We also thank the SNSF (P300P3_151157) and European Society for Clinical Nutrition and Metabolism for providing grant support to S.L. I.G.-P. is supported by an NIHR career development research fellowship (NIHR-CDF-2017-10-032). M.J.B. was supported in part by NIH grant number R01-DK090989. J.M.P. is supported by a Rutherford Fund Fellowship at Health Data Research, UK (MR/S004033/1). E.H. is supported by the U.K. Dementia Research Institute at Imperial College, London. The views expressed are those of the authors and not necessarily those of the funders, UK National Health Service, the NIHR, or the U.K. Department of Health. Author contributions: S.L., W.W., and S.P. conceived and designed the project. S.L. performed experiments to analyze muscle atrophy and analyzed the skeletal muscle and liver using Western blot and qPCR. S.L. also performed glucocorticoid treatment of C2C12 myotube cultures. I.G.-P. and E.H. performed NMR spectroscopic experiments, which were designed by J.N. J.M.P. prepared the MetaboNetworks map. S.L., H.K., and M.M.R. performed NMJ experiments. H.K. and M.M.R. performed SCFA treatment experiments. K.A.M. performed the weights test in mice. J.S. and K.A.M. performed the open-field activity test in mice. P.K. performed AMPK Western blotting. L.M.C. and M.J.B. performed antibiotic treatment experiments. S.L. and H.Z. performed cellular respiration experiments in C2C12 myotube cultures. P.P. and B.G. performed PET-MRI experiments. C.M. performed histology and histochemical assays. W.W. and S.P. gave input to analyses of the data. S.L. and S.P. wrote the manuscript with input from J.A., E.H., J.N., and W.W. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data associated with this study are present in the paper or the Supplementary Materials. NMR data has been deposited in Figshare with DOI 10.6084/m9.figshare.8982233.
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