Changes in the urinary proteome in rats with regular swimming exercise

Experimental animals

Male SD rats (seven days old) were supplied by the Department of Neurobiology, School of Basic Medical Sciences, Peking University. All animals were housed with free access to a standard laboratory diet and water with a 12-h light-dark cycle under standard conditions (indoor temperature 22 ± 1 °C, humidity 65–70%). The experiment was approved by the Institute of Basic Medical College (ID: ACUC-A02-2014-007). The study was performed according to guidelines developed by the Institutional Animal Care and Use Committee of Peking Medical College. After the experiment, all the animals were euthanized by intraperitoneal injection of barbiturates.

Swimming exercise

A large pool (diameter: 1,500 mm, height: 500 mm) served as the swimming pool. The water temperature was maintained at 36 °C. For the adaptation phase, rats swam for increasing amounts of time, from 2 min to 10 min over three days. For the exercise phase, the intensity of exercise in the first week gradually increased from 15 min to 40 min, and the intensity at 40 min lasted for 6 weeks, which is considered to be moderate exercise (Seo et al., 2014). The animals were quickly and gently dried after each training session. The rats (n = 10) were randomly divided into the following two groups: experimental rats (n = 6) and control rats (n = 4). In the experimental group, the rats underwent the 7-week swimming exercise program. The control rats did not swim.

Urine collection and sample preparation

Urine samples were collected from the experimental and control groups at weeks 2, 5 and 7 during the swimming exercise. The animals were individually placed in metabolic cages for 10 h to collect urine samples without any treatment. After collection, the urine samples were stored at −80 °C. The urine samples (n = 30) were centrifuged at 12,000 g for 40 min at 4 °C to remove cell debris. The supernatants were precipitated with three volumes of ethanol at −20 °C overnight and then centrifuged at 12,000 g for 30 min. Then, lysis buffer (8 mol/L urea, 2 mol/L thiourea, 50 mmol/L Tris, and 25 mmol/L DTT) was used to dissolve the pellets. The protein concentration of the urine samples was measured by the Bradford assay.

Tryptic digestion

Urinary proteins (100 µg of each sample) were digested with trypsin (Trypsin Gold, Mass Spec Grade, Promega, Fitchburg, WI, USA) using filter-aided sample preparation (FASP) methods (Wisniewski et al., 2009). These peptide mixtures were desalted using Oasis HLB cartridges (Waters, Milford, MA) and dried by vacuum evaporation (Thermo Fisher Scientific, Bremen, Germany). The digested peptides (n = 30) were redissolved in 0.1% formic acid to a concentration of 0.5 µg/µL. The iRT reagent (Biognosys, Switzerland) was spiked at a concentration of 1:10 v/v into all samples for calibration of the retention time of the extracted peptide peaks. For analysis, 1 µg of peptides from an individual sample was analyzed by LC-DIA-MS/MS.

Reversed-phase fractionation spin column separation

A total of 90 µg of pooled peptides was generated from 6 µl from each sample and then separated by a high-pH reversed-phase peptide fractionation kit (Thermo Pierce, Waltham, MA, USA) according to the manufacturer’s instructions. A step gradient of increasing acetonitrile concentrations (5, 7.5, 10, 12.5, 15, 17.5, 20 and 50%) was applied to the columns to elute the peptides. Ten different fractionated samples (including the flow-through fraction, wash fraction, and eight step gradient sample fractions) were collected and dried by vacuum evaporation. The ten fractions were resuspended in 20 µl of 0.1% formic acid, and 1 µg of each of the fractions was analyzed by LC-DDA-MS/MS.

LC-MS/MS analysis

A total of 30 peptide samples were analyzed in an EASY-nLC 1200 chromatography system (Thermo Fisher Scientific) and an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific). The samples were loaded onto a trapping column (75 µm × 2 cm, 3 µm, C18, 100 Å) and separated by a reverse-phase analysis column (75 µm × 25 cm, 2 µm, C18, 100 Å). The eluted gradient was 4%–35% buffer B (0.1% formic acid in 80% acetonitrile) at a flow rate of 300 nL/min for 90 min.

To generate the spectral library, 1 µg of each of ten fractions was analyzed in DDA mode. The parameters were set as follows: the full scan ranged from 350 to 1500 m/z with a resolution of 120,000; MS/MS scans were acquired with a resolution of 30,000; the cycle time was set to 3 s; the HCD energy was set to 30%; the autogain control (AGC) target was set to 4e5; and the maximum injection time was set to 50 ms. In DIA mode, 1 µg of each sample was analyzed. The variable isolation window of the DIA method with 36 windows was set (Table S1). The parameters were set as follows: the full scan ranged from 350 to 1,500 m/z with a resolution of 60,000; the DIA scan was acquired from 200 to 2,000 m/z with a resolution of 30,000; the HCD energy was set to 32%; the AGC target was set to 1e6; and the maximum injection time was set to 100 ms. During the samples analysis, a mixture from each sample was analyzed after every six samples for quality control (QC).

Data analysis

The DDA data of ten fractions were searched against the Swiss-Prot rat database (released in 2017, including 7,992 sequences) appended with the iRT peptide sequence using Proteome Discoverer software (version 2.1, Thermo Scientific). The search parameters were set as follows: two missed trypsin cleavage sites were allowed; the parent ion mass tolerances were set to 10 ppm; the fragment ion mass tolerances were set to 0.02 Da; the carbamidomethyl of cysteine was set as a fixed modification; and the oxidation of methionine was set as a variable modification. The false discovery rate (FDR) of the proteins was less than 1%. A total of 873 protein groups, 4098 peptide groups and 37555 peptide spectrum matches were identified. The search results were used to set the DIA method. The DDA raw files were processed using Spectronaut’s Pulsar database (Biognosys, Switzerland) with the default parameters to generate the spectral library. The DIA raw files were processed using Spectronaut for analysis with the default setting. All of the results were filtered by a Q value cutoff of 0.01 (corresponding to an FDR of 1%). Peptide intensity was calculated by summing the peak areas of their respective fragment ions of MS2, and the protein intensity was calculated by summing the intensities of their respective peptides.

Statistical analysis

The k-nearest neighbor (K-NN) method was used to fill the missing values of protein abundance (Armitage et al., 2015). Comparisons between experimental and control groups were performed by one-way ANOVA. The differential proteins at weeks 2, 5 and 7 were screened by the following criteria: fold change ≥ 1.5 or ≤ 0.67; and P < 0.05 by independent sample t-test. Group differences resulting in p < 0.05 were considered statistically significant.

Functional annotation of the differential proteins

DAVID 6.8 (https://david.ncifcrf.gov/) was used to perform the functional annotation of the differential proteins between the experimental and control groups. The canonical pathways were analyzed with IPA software (Ingenuity Systems, Mountain View, CA, USA).

Urine proteome changes in the swimming exercise rats

In this study, thirty urine samples from three time points (weeks 2, 5, and 7) from six experimental rats and four control rats were used for LC-DIA-MS/MS quantitation. A total of 729 proteins and 5,265 peptides were identified in all urine samples. A quality control sample of a mixture from each sample was analyzed after every six samples. A total of 518 proteins were identified that had a coefficient of variation (CV) of the QC samples below 30%, and all of the identification and quantification details are listed in Table S2.

Unsupervised clustering analysis of all of proteins identified at three time points was performed (Fig. S1). We found that the samples at week 2 were clustered together, indicating that swimming exercise has a great impact on urine after 2 weeks. It is speculated that the clustering effect of the samples at weeks 5 and 7 was poor because the body had adapted to long-term exercise. To further characterize the effects of 2 weeks of swimming exercise, all urinary proteins from 10 urine samples between the two groups at week 2 were analyzed by principal component analysis (PCA). As shown in Fig. 2A, the swimming exercise rats were differentiated from the control rats. Meanwhile, unsupervised clustering analysis of all urinary proteins from 10 urine samples between two groups at week 2 was performed. As shown in Fig. 2B, the proteomics profiles of the swimming group were distinctively different from those of the control group. These results demonstrated that the urinary proteome changed significantly after swimming exercise.

The differential proteins were screened with a p value < 0.05 by two-sided, unpaired t-test and a fold change ≥ 1.5 compared with controls. Compared to the control group, 112 differential proteins were identified after 2 weeks of swimming exercise, among which 28 proteins were upregulated and 84 proteins were downregulated (Fig. 3A); 61 differential proteins were identified after 5 weeks of swimming exercise, among which 6 proteins were upregulated and 55 proteins were downregulated (Fig. 3B); and 44 differential proteins were identified after 7 weeks of swimming exercise, among which 11 proteins were upregulated and 33 proteins were downregulated (Fig. 3C). The details of these differential proteins are presented in Table 1. Among these differential proteins, 171 proteins had human orthologs. The overlap of these differential proteins is shown by the Venn diagram in Fig. 3D. Five proteins were commonly identified at three time points (Fig. 3D), including Ig gamma-1 chain C region, hemopexin, transthyretin, cathepsin D and chondroitin sulfate proteoglycan 4.

Tissue distribution of the human orthologs of the differential proteins

To investigate the expression levels of the differential proteins in different tissues and organs, 171 differential proteins that had human orthologs were searched from the Human Protein Atlas. According to the Tissue Atlas, 31 tissues were identified (Fig. 4). The differential proteins were strongly expressed in the liver, kidney, intestine, and blood, indicating that these organs may be affected after swimming exercise. Swimming exercise can recruit a large volume of muscle mass. Notably, two proteins, triosephosphate isomerase (TIPSS) and aspartate aminotransferase (AATC), were strongly expressed in skeletal muscle, indicating that moderate-intensity swimming exercise might have an effect on the muscles of rats.

Randomized grouping statistical analysis

Considering that omics data are large but the sample size is limited, the differences between the two groups may be randomly generated. To confirm whether the differential proteins were indeed due to swimming exercise, we performed a randomized grouping statistical analysis. We randomly allocated the proteomic data of 10 samples (6 for experimental and 4 for control samples) at each time point and screened for the differential proteins with the same criteria. Then, the average number of differential proteins in all random combinations was calculated, which was the false positive in the actual grouping. There were 210 random allocations at each time point, and the average number of differential proteins in all random combinations at each time point was 15, 5 and 6. The results showed that the false-positive rates were 13.4%, 5% and 13.6% at weeks 2, 5 and 7, respectively. Therefore, most of the differential proteins identified at each time point in this study were caused by swimming exercise rather than random allocation. The details are presented in Table S3. These results suggested that the sample size of this study was sufficient to prove the significant difference in the urine proteome between the swimming group and the control group.

Functional annotation analysis of the differential proteins

Functional annotation of differential proteins at weeks 2, 5 and 7 was performed by DAVID (Huang, Sherman & Lempicki, 2009). The differential proteins identified at three time points were classified into three categories: biological process, cellular component and molecular function.

In the biological process category (Fig. 5A), negative regulation of endopeptidase activity and carbohydrate metabolic process were overrepresented at weeks 2 and 5; complement activation, classical pathway, innate immune response and positive regulation of cholesterol esterification were overrepresented at weeks 2 and 7. Response to lipopolysaccharide and positive regulation of cholesterol esterification were only overrepresented at week 2; B cell receptor signaling pathway and positive regulation of B cell activation were only overrepresented at week 7.

In the cellular component category (Fig. 5B), the majority of these differential proteins were from extracellular exosome and extracellular space. In the molecular function category (Fig. 5C), metallodipeptidase activity was overrepresented at weeks 2 and 5; serine-type endopeptidase inhibitor activity and antigen binding were overrepresented at weeks 2 and 7.

To characterize the canonical pathways involved with these differential proteins, IPA software was used for analysis. As shown in Fig. 5D, LXR/RXR activation and FXR/RXR activation were enriched at three time points. Sphingosine and sphingosine-1-phosphate metabolism, ceramide degradation, lactose degradation III, and thyroid hormone biosynthesis were enriched at weeks 5 and 7. Complement system, sucrose degradation V, IL-12 signaling and production in macrophages, and glycolysis I were enriched at week 2. Glutamate degradation II was enriched at week 5. Aryl hydrocarbon Receptor signaling was enriched at week 7.