Multi-omics reveals changed energy metabolism of liver and muscle by caffeine after mice swimming

Animal experiment and sample collection

The animal experiments were conducted in the Medical Laboratory Animal Center at the Chines PLA General Hospital, which was approved by the Animal Care and Use Committee of Chines PLA General Hospital (SQ2020030). Eighteen 6-week-old male C57BL/6J mice purchased from laboratory animal research center of our hospital were housed under specific pathogen-free condition. Data were collected as previously described in Han et al. (2022). Specifically, the housed temperature and humidity were controlled at 25 ± 2 °C and 50 ± 10%, respectively, and a 12:12 h light-dark cycle. The animals were fed a standard pellet diet and distilled water ad libitum. Each mouse was housed in a 30 cm high cage and the floor of the cage covered with an adequate depth of dust free Aspen wood chip. If mice are obviously unwell, the experiment should be terminated. We determined whether animals were unwell by observing behaviors such as silence, lethargy, inactivity, loss of appetite, and refusal to eat. Animals were not be euthanized until the end of the experiment. After the experiment, all animals were euthanized by intraperitoneal injection of pentobarbital sodium at a dose of 200 mg/kg.

Spontaneous wheel running exercise is not suitable for studies that require precise timing for exploring acute exercise adaptations, since the investigator cannot easily regulate the duration or intensity of the running behavior (Seo et al., 2014). What’s more, it has been proved that caffeine ingestion improves exercise performance in a broad range of exercise tasks (Grgic et al., 2020), and we focused on the ergogenic effects of caffeine on acute exercise performance. Therefore, the forced swim test was selected for our study. Animals were randomly divided into the discovery group (n =  18) and the validation group (n =  6). The discovery group includes the control group (n =  9) and the caffeine group (n =  9). Mice in the caffeine group were orally administered with one mL of 0.125 mg/mL caffeine solution before mice swimming. The control group was given an equal volume of normal saline. Both the control and caffeine groups of mice were placed in a pool with a temperature of 32 ±2 °C and a depth of over 40 cm, swimming for 30 min, ensuring constant movement throughout the procedure. After the first swimming experiment, mice rested for 4 hours before undergoing the same administration and swimming experiment. The validation group was also divided into the control (C_V, n =  3) and the caffeine groups (F_V, n =  3). The experiment of validation group was conducted independently, following the same procedure as that of the discovery group. Stress-related immobility in the mouse-forced swim test is labeled as a depression-like phenotype (de Kloet & Molendijk, 2016). To separate the effects of exercise from stress, we stirred near the mice with a stick to keep them in continuous motion while they were floating on the water with immobilized limbs.

In order to mitigate and abolish the pain and stress experienced by mice during blood collection, and to facilitate the experimental process while ensuring animal safety, after the second swimming experiment, the mice were intraperitoneally injected with a mixture of ketamine and xylazine to achieve sedative and analgesic effects. Once the mouse lost consciousness, blood was extracted from the orbit. To obtain serum, after standing for 40 min without adding anticoagulant, the blood was centrifuged at 4 °C and 1,900 ×g for 10 min, and the supernatant was taken and stored at −80 °C. Subsequently, the mice were dissected to obtain liver samples immediately including mice with (the discovery group: FL, the validation group: FL_V) or without (the discovery group: CL, the validation group: CL_V) caffeine, as well as gastrocnemius muscles including mice with (the discovery group: FM) or without (the discovery group: CM) caffeine. The tissues were washed with 4 °C cold saline and immediately cryopreserved in liquid nitrogen. Finally, the tissue samples were then utilized for proteome and metabolome analysis, while the blood samples were employed to detect biochemical indices.

Sample preparation for proteome analysis

Grinded frozen tissue samples were added to lysis buffer (8M urea, 1% protease inhibitor) and then lysed by sonication. The lysate was centrifuged at 12,000 g for 10 min at 4° C to remove cell debris, and the supernatant was transferred to a new tube for protein quantification using BCA kit (Beyotime, Haimen, China). Equal amounts of protein were extracted from each sample and the volume was adjusted to the same volume with lysis buffer. The 20% Trichloroacetic acid (Sigma-Aldrich, Shanghai, China) was added, mixed by vortex, and precipitated at 4° C for 2 h. The precipitated sample was centrifuged at 4,500 g for 5 min, the supernatant was discarded, and the precipitate was washed 2–3 times with pre-cooled acetone. Tetraethylammonium bromide (Sigma-Aldrich, Shanghai, China) with a concentration of 200 mM was added to the air-dried precipitate and then dispersed by ultrasonication. Trypsin was added at a ratio of 1:50 (protease : protein) for enzymatic hydrolysis overnight. After enzymatic hydrolysis, DL-Dithiothreitol (Sigma-Aldrich, Shanghai, China) was added to a final concentration of 5 mM, and reduced at 56 °C for 30 min. Iodoacetamide (Sigma-Aldrich, Shanghai, China) was added to a final concentration of 11 mM and incubated at room temperature for 15 min in the dark.

Proteome LC-MS/MS analysis

The peptides were separated by ultra-high-performance liquid chromatography and injected into NSI source for ionization, and then analyzed by Orbitrap Exploris™ 480 mass spectrometer. The ion source voltage was set to 2.3 kV, the FAIMS compensation voltage (CV) was set to −45 V and −65 V, and the peptide precursor ions and their secondary fragments were detected and analyzed using high-resolution Orbitrap. The scanning range of the primary mass spectrometer was set to 400–1,200 m/z, and the scanning resolution was set to 60,000. The fixed starting point of the scanning range of the secondary mass spectrometer was 110 m/z. The secondary scanning resolution was set to 15,000, and the TurboTMT was set to off. The data acquisition mode used a cycle time-based data-dependent scanning program, that is, the peptide precursor ions were selected according to the order of signal intensity to enter the HCD collision cell within a cycle period of 1.0 s, and 27% fragmentation energy was used. Following the same procedure, the secondary mass spectrometry analysis was performed. In order to improve the effective utilization of mass spectrometry, automatic gain control was set to 100%, signal threshold was set to 5E4 ions/s, maximum injection time was set to auto, and dynamic exclusion time of tandem mass spectrometry scanning was set to 20 s to avoid precursor ions repeated scans.

Data of the secondary mass spectrometry was searched in Uniport Mus_musculuc database (17063 sequences) using proteome discoverer software (v2.4.1.15). An anti-library was added to calculate the false positive rate. Due to random matches, a common contamination library was added to the database to eliminate the effect of contaminating proteins in the identification results. Finally, the proteome quantitation data of each sample was obtained for subsequent analysis.

Sample preparation for metabolome analysis

The frozen tissue samples were thawed on ice and 50 mg of the samples were weighed into a two mL EP tube, and 500 µL of −20 °C pre-cooled 70% methanol water internal standard extraction solution was added. A small steel ball was added to the sample, and the sample was homogenized 4 times at 30 Hz for 30 s each time. After homogenization, the samples were shaken at 1,500 r/min for 5 min, and allowed to stand on ice for 15 min. After that, the samples were centrifuged at 12,000 r/min for 10 min at 4 °C. 200 μL of the supernatant was loaded into the lined pipe for subsequent analysis.

Metabolome analysis

We selected five pathways involved in energy metabolism and performed widely targeted detection on 65 metabolites involved in them. Five energy metabolism pathways include glycolysis, citrate cycle, oxidative phosphorylation, amino acid metabolism and pentose phosphate pathway. The sample extracts from liver and muscle were analyzed using ultra-performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS/MS; UPLC, Shim-pack UFLC SHIMADZU CBM30A system, Shimadzu, Chengdu, China; MS, QTRAP^® System; Sciex, Shanghai, China).

The chromatographic column for the liquid phase was an SeQuant ZIC-pHILIC, 5 µm (2.1 mm × 100 mm). In the mobile phase, phase A was 0.3% ammonia solution to which 10 mmol/L ammonium acetate was added and phase B was 90% acetonitrile water. The proportion of phase A and phase B in the elution gradient was 5:95 (V/V) at 0 min, 50:50 (V/V) at 9.5 min, 5:95 (V/V) at 11.1 min and 5:95 (V/V) at 14 min. The injection volume was 2 µL, with the flow rate set to 0.4 mL/min, and the column temperature set to 40 °C. The effluent was connected to a triple quadrupole-linear ion trap mass spectrometer (QTRAP) equipped with an ESI Turbo Ion-Spray interface. After the mass spectrum analysis, metabolite quantitation data for each sample were acquired through multiple reaction monitoring analysis. The peak area of each chromatographic peak represents the relative content of the corresponding substance. The chromatographic peak area was calculated and corrected by using MultiQuant software (v3.0.2) and the chromatographic peaks detected in different samples for each metabolite are calibrated to ensure qualitative and quantitative accuracy.

Principal component analysis

The unsupervised principal component analysis (PCA) was performed on all detected proteins and metabolites using the statistical function prcomp in R (version 4.1.0; R Core Team, 2021). The 95% confidence ellipse was drawn according to the distribution of samples in different groups and colored by groups.

Differential features selection

Statistical analysis was performed to select differential proteins and metabolites. Student’s t-test was performed between the two groups. The p values of differential proteins were corrected using the Benjamin-Hochberg method for multiple hypothesis testing. The corrected p value (q value) <0.05 and the absolute value of log₂ FoldChange >0.5 were used as the threshold for significantly differential proteins. The significantly differential metabolites were filtered as the p value <0.05 and the absolute value of log₂ FoldChange(FC) >0.5. The variable importance in projection (VIP) value of differential metabolites presented in supplementary table was calculated through orthogonal partial least-squares discrimination analysis (OPLS-DA) analysis using R package MetaboAnalystR (v1.0.1). According to these thresholds, the differential proteins and metabolites were presented by volcano plot using R package EnhancedVolcano (v1.14.0). The heat map was used to display the relative content of differential proteins and metabolites in each sample and the z score was calculated by scaled as samples using R package pheatmap (v1.0.12). Hierarchical clustering was carried out on samples and features in heat map.

Correlation analysis

Network analysis was performed between all differential proteins and differential metabolites with p value <0.01 by calculating Spearman’s rank correlation coefficient (Rho) using R package psych (v2.1.9). Benjamin-Hochberg correction was performed for the p value of correlation analysis results. The correlations with adjusted p value (q value) <0.05 and rho >0.6 were retained and visualized by cytoscape software (v3.8.2). Linear correlations between the two metabolites were measured by calculating Pearson correlation coefficient (r) and visualized by using R package ggplot2 (v3.3.5).

Biochemical indexes test

Lactate levels were quantified using Lactate Scout 4 (EKF Diagnostics Holdings plc, Cardiff, UK). Glycogen levels of liver was detected using glycogen assay kit ab65620 (ab65620, Abcam) as the glycogen assay protocol. Phosphoenolpyruvate (PEP) levels of liver was detected using PEP assay kit ab204713 (ab204713; Abcam). Pyruvate carboxylase (PC) levels of liver was detected using mouse PC ELISA kit (JYM1189Mo; ELISA Lab). The Optical Density (O.D.) was determined using a microplate reader (Thermo Fisher Scientific™ Varioskan™ LUX; Thermo Fisher Scientific, Waltham, MA, USA) in our lab. Only twelve samples in the discovery group were tested for glycogen, PEP and PC levels of liver due to insufficient tissue samples. Student’s t-test was performed to acquire the statistical results between two groups.

Proteome of liver and gastrocnemius muscle

To investigate the proteome composition differences between groups, we initially conducted principal component analysis (PCA) to assess the clustering patterns of liver and gastrocnemius muscle samples (Fig. 1AB). Differential analysis revealed that there were 23 and 3 proteins exhibiting differential expression between CL vs FL and CM vs FM, respectively, with no interaction between them. Specifically, 13 proteins were found to be significantly up-regulated while 10 proteins were down-regulated in FL group (Fig. 1C, Table S1). Two proteins were up-regulated and one protein was down-regulated in the FM group compared to the CM group (Fig. 1D, Table S2). The relative abundance of differential proteins in each sample was displayed, and hierarchical clustering for samples was performed (Fig. 1EF). Based on 23 differential proteins, the CL and FL groups could be clearly clustered into two groups. Also, the CM and FM groups acquired a clear clustering result.

In the up-regulated proteins of the FL group, Rer1 exhibited a remarkable increase of 3.8-fold (q < 0.01) compared to CL. S100a9 and S100a8, both playing a prominent role in the regulation of inflammatory processes and immune response and were more expressed in the practitioners of physical exercise (Pacheco et al., 2022), were significantly elevated in FL group. The expression level of mitochondrial ribosomal protein L40 (Mrpl40) was twice that of CL. GA-binding protein alpha chain (Gabpa), a sport-related gene identified in horses by genome-wide association study (Schröder et al., 2012), was significantly up-regulated in FL group. The expression of insulin-like growth factor-binding protein 1 (Igfbp1), known for its protective function against cardiovascular pathologies and strongly increased after swimming in mice and healthy people (Hoene et al., 2010; Nishida et al., 2010; Rajwani et al., 2012), was notably up-regulated in FL. Syndecan-4 (Sdc4), which was increased in human plasma after swimming exercise and in the livers of exercise trained mice compared with sedentary ones (De Nardo et al., 2022; Lee et al., 2019b), also showed a significant increase in FL. A significant reduction in tissue factor (Tf) induced by running (Highton et al., 2019) was also observed in FL group. Additionally, there was a notable increase in myotubularin-related protein 1 (Mtmr1), a lipid phosphatase with high specificity for phosphatidylinositol 3-phosphate, and glycogenin-1 (Gyg1), which initiates glycogen synthesis, within FL group. The expression of diacylglycerol O-acyltransferase 2 (Dgat2), responsible for triacylglycerol synthesis, and insulin-induced gene 2 protein (Insig2), involved in cholesterol synthesis and obesity, were down-regulated in FL. Retinol-binding protein 4 (Rbp4), which is associated with improved insulin sensitivity and exercise training (Marschner et al., 2017), was also decreased in FL group.

Differential analysis results showed that only two proteins were up-regulated in FM group, while one protein was down-regulated. These indicate that caffeine has minimal impact on the protein levels of gastrocnemius muscle after swimming. Notably, we found a significant up-regulation of Psmd12 in FM, which is involved in cell cycle progression, apoptosis and DNA damage repair.

Energy metabolome of liver and gastrocnemiusmuscle

To investigate alterations of metabolites involved in energy metabolism, we conducted a comprehensive analysis of 65 metabolites associated with this process. Initially, PCA was performed to assess the metabolites detected in liver, revealing distinct separation between CL and FL (Fig. 2A). Similarly, PCA analysis of metabolites in gastrocnemius muscle indicated clear clustering between CM and FM (Fig. 2B). Subsequently, differential metabolite analyses were conducted for CL vs FL and CM vs FM separately, resulting in the identification of 19 and 22 significantly differentially expressed metabolites respectively (Fig. 2CD). Notably, nine of these differentially expressed metabolites were found to be common to both groups. The relative expression levels of these differential metabolites were displayed by a heat map and hierarchical clustering was applied to group samples accordingly (Fig. 2EF). The observed clustering pattern indicated that these specific metabolites effectively characterize each group.

The levels of amino acids, including asparagine, serine, tyrosine, cysteine, threonine and citrulline were significantly up-regulated in FL compared to CL group. Conversely, metabolites involved in glycolytic pathway such as glucose 6-phosphate, fructose 6-phosphate, pyruvic acid and lactate exhibited significant down-regulation in FL group indicating a decrease in glycolysis.

The differential analysis results between CM and FM showed a significant increase in dAMP, oxaloacetate, uracil, dUMP, phenyllactate and pyruvic acid levels in FM group. Conversely, metabolites associated with the pentose phosphate pathway such as glucose-6-phosphate, 6-phosphogluconic acid, ribulose-5-phosphate, xyluose-5-phosphate, sedoheptulose-7-phosphate and D-erythrose-4-phosphate were all found to be decreased in FM group. These findings suggest a down-regulation of the pentose phosphate pathway in FM group. Additionally, glycolysis pathway was also decreased in FM, due to the down-regulation of glucose, glucose-6-phosphate, fructose-6-phosphate and glyceraldehyde-3-phosphate.

Correlation analysis

To investigate the associations between proteins and metabolites, we performed Spearman’s rank correlation analysis separately for liver and muscle. Our results revealed that Sdc4, S100a9, S100a8, Gyg1 and Igfbp1 in liver were positively correlated with cysteine, threonine, ornithine, asparagine and serine (Fig. 3A). Furthermore, we observed a positive correlation between the reduction of glucose and proteins associated with alleviating obesity and type 2 diabetes, such as Insig2, Dgat2 and Rbp4. Additionally, Kpna2 exhibited a positive correlation with metabolites involved in glycolysis including D-glucose 6-phosphate, D-fructose 6-phosphate and pyruvic acid. Correlation analysis results for gastrocnemius muscle showed that Psmd12 was correlated with several significantly altered metabolites in FM group, such as lactate acetyl-CoA, glucose, D-glucose 6-phosphate and D-fructose 6-phosphate (Fig. 3B). Moreover, the up-regulated Lman2 showed a positive correlation with pyruvic acid and oxaloacetate.

To further investigate the associations between liver and muscle after caffeine administration, Pearson correlation analysis was performed for 38 metabolites and 26 proteins with significant differences between control and caffeine groups. Results showed that 38 metabolites were significantly associated with 25 proteins (Fig. 3C, r >  0.3, q value <0.05, Table S5). Among these associations, Tgfbi and Gyg1 in liver, as well as Lman2 in muscle, emerged as three nodes with the highest degree within the network. Notably, glucose in muscle exhibited the maximum degree among all metabolite nodes. Additionally, strong positive correlations (r > 0.9) were observed between L-Asparagine and Gabpa, Ctnnd1, Gyg1 and Eipr1. Furthermore, decreased levels of glucose in muscle were positively correlated with Ilvbl but negatively correlated with Gyg1 in liver.

Pathway analysis

To investigate changes in energy metabolism, we conducted pathways enrichment analysis on the differential metabolites. Our findings revealed significant alterations in glycolysis, citrate cycle and pentose phosphate pathways in the FL and FM groups. Glycolysis pathway annotated by KEGG with differential metabolites was presented (Fig. 4A). Notably, glucose, glucose 6-phosphate and fructose 6-phosphate were all significantly down-regulated in both FL and FM groups, indicating a substantial decrease in glycolysis within the liver and gastrocnemius muscle of caffeine-administered mice compared to the control group. Pyruvic acid and oxaloacetate levels were significantly decreased in FL, while increased in FM. As a result of down-regulated glycolysis, it is speculated that elevated pyruvic acid levels in gastrocnemius muscle may be produced by alternative pathways. Notably, serine and cysteine can be metabolized into pyruvic acid (Gray, Tompkins & Taylor, 2014), which is further catalyzed by pyruvate carboxylase to oxaloacetate. Our results indicate that serine and cysteine were significantly up-regulated in the FL group but not in the FM group. Furthermore, we compared the levels of pyruvate carboxylase (PC) between CL and FL groups. PC levels were measured in twelve samples from the discovery group, revealing a significant increase in FL compared to CL (p = 0.045, Fig. 4B). It was further validated in FL_V group compared to CL_V group (p = 0.015, Fig. 4B). These results suggest that the elevated levels of pyruvic acid and oxaloacetate in muscle may be synthesized through serine and cysteine in liver. Further analysis for amino acids revealed increased levels of tyrosine, threonine, ornithine and citrulline in the FL group. It is noteworthy that fumarate, succinyl-CoA and alpha-ketoglutaric acid involved in the citrate cycle could be synthesized through tyrosine, threonine, ornithine and citrulline metabolism, respectively. Interestingly, the levels of fumarate, argininosuccinic acid and malate were significantly up-regulated in FM group. However, succinyl-CoA and alpha-ketoglutaric acid were significantly decreased in the FL group. These findings suggest a potential correlation between elevated amino acid levels in liver and increased metabolites associated with the citrate cycle in gastrocnemius muscle.

To further confirm our hypotheses, we performed Pearson correlation analysis between amino acids in liver and metabolites involved in the citrate cycle in gastrocnemius muscle. Our results showed significant positive correlations between tyrosine, serine, cysteine, ornithine, citrulline and threonine in liver and oxaloacetate in muscle (p < 1e-4, r ≥ 0.8, Fig. 5A). Notably, these correlations were predominantly observed under the caffeine group rather than control group. Additionally, serine and cysteine in liver were significantly positively correlated with pyruvic acid in muscle (p < 0.05, r ≥ 0.5, Fig. 5B). Consistent with our previous speculation, a significantly positive correlation was observed between pyruvic acid and oxaloacetate in muscle (p < 0.01, r = 0.6, Fig. 5B). Furthermore, correlation analysis results provide evidence suggesting that amino acids in liver may serve as potential precursors for the synthesis of pyruvic acid and oxaloacetate in muscle. However, imperfect correlations between pyruvic acid and serine or cysteine imply the existence of alternative pathways associated with increased levels of pyruvic acid. To investigate the influence of the Cori cycle, which facilitates lactate transport and its conversion to glucose in liver for subsequent utilization by muscle, we performed correlation analysis between lactate and pyruvic acid in muscle and liver. Results showed that lactate level was negatively correlated with pyruvic acid in muscle (p = 0.0016, r =  − 0.69, Fig. 5C). Additionally, we observed a positive correlation between pyruvic acid levels in liver and lactate in muscle (p = 0.0013, r = 0.7, Fig. 5C). Furthermore, there was a significant positive association between the lactate levels in liver and lactate levels in muscle (p = 0.004, r = 0.64). Our results suggest that caffeine may contribute to elevated muscular pyruvate levels through its potential acceleration of the Cori cycle.

Additionally, we observed a significant decrease in the levels of metabolites generated through the pentose phosphate pathway, such as sedoheptulose-7-phosphate, D-erythrose-4-phosphate and xyluose-5-phosphate, in both the FL group and FM group. Furthermore, intermediates including D-ribulose-5-phosphate, 6-phosphogluconic acid, glyceraldehyde-3-phosphate were significantly down-regulated in FM group. This result suggests a substantial down-regulation of the pentose phosphate pathway in both liver and muscle after caffeine administration.

Caffeine promotes the utilization of lactate and glycogen

Energy metabolome analysis revealed that caffeine significantly decreased lactate levels in liver and muscle after mice swimming (Fig. 6A). We speculated that caffeine accelerates the consumption and the reduction of lactate. To further substantiate the role of caffeine in enhancing lactic acid utilization, we quantified the plasma lactate levels before and after swimming in control and caffeine groups. Remarkably, post-swimming plasma lactate levels exhibited a significant increase in control group (p < 0.001, Fig. 6B). However, in caffeine group, there were no significant changes observed in plasma lactate levels after swimming (p = 0.19, Fig. 6C). The difference in lactate levels before and after swimming (Δ Lactate) was found to be significantly different between control and caffeine groups (p = 0.01, Fig. 6D). Our results validate that caffeine enhances the utilization of post-exercise lactate, thereby maintaining its levels equivalent to pre-exercise conditions.

To further investigate the impact of caffeine on liver glycogen levels after swimming, we compared the glycogen levels between control and caffeine groups. Glycogen levels in the discovery group showed a significant decrease in the FL group compared to the CL group (p < 0.01, Fig. 6E). These results indicate that caffeine enhances glycogen utilization in liver after swimming. Previous studies have shown that exercise training increases hepatic gluconeogenesis after swimming to resist to exercise-induced hypoglycemia (Bergman et al., 2000; Donovan & Sumida, 1997). Oxaloacetate, an intermediate metabolite in gluconeogenic pathway, can be converted into phosphoenolpyruvate (PEP) and eventually glucose. Therefore, we examined PEP levels in liver between the CL and FL groups. PEP levels in the discovery group showed a significant increase in FL group (p < 0.001, Fig. 6F). This result was further validated by analyzing another independent sample set (p < 0.001, Fig. 6G). These results revealed that exercise with caffeine intake significantly improved exercise-induced hypoglycemia, but cannot explain its effect on liver gluconeogenesis (Gilglioni et al., 2016). Our results indicate that caffeine administration before exercise promotes increased conversion from oxaloacetate to phosphoenolpyruvate, potentially accelerating liver gluconeogenesis.