Changes in the urinary proteome in rats with regular swimming exercise

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Biochemistry, Biophysics and Molecular Biology

Introduction

Urine is a good source for biomarker discovery. Without homeostatic mechanisms, urine can sensitively reflect early pathophysiological changes in the body, and these changes might be useful disease biomarkers (Gao, 2013). Since the composition of urine is affected by various factors, such as age, sex, and diet (Gao, 2014; Guo et al., 2015; Wu & Gao, 2015; Li & Gao, 2016), animal models are an effective tool to minimize external influencing factors due to their similar genetic backgrounds and the same living environment. Thus, disease animal models can establish relationships between the disease and the corresponding changes in the urine proteome. Our laboratory found that changes in urinary proteins occurred before pathologic or clinical manifestations appeared in various types of animal models, such as subcutaneous tumor model (Wu, Guo & Gao, 2017), Alzheimer’s disease model (Zhang et al., 2018b), chronic pancreatitis model (Zhang, Li & Gao, 2018), liver fibrosis model (Zhang et al., 2018a), and myocarditis model (Zhao et al., 2018). Recent studies have shown that the urine proteome has potential for differential diagnosis. For example, early urinary proteins were different when the same tumor cells were grown in different organs (Wu, Guo & Gao, 2017; Wei et al., 2019; Zhang et al., 2019; Wang et al., 2020; Zhang, Gao & Gao, 2020) and when different cells were injected into the same organ (Zhang et al., 2018a; Zhang et al., 2018b; Zhang et al., 2021). Furthermore, several clinical studies performed urine proteomics to discover diagnostic biomarkers, such as for gastric cancer (Shimura et al., 2020) and familial Parkinson’s disease (Winter et al., 2021).

Physical exercise as a pathophysiological process that can improve health conditions and has a positive role in numerous chronic conditions (Pate et al., 1995; Haskell et al., 2007; Seo et al., 2014), including cancer and coronary heart diseases (Stewart et al., 2017; Hojman et al., 2018). Many studies have shown that exercise has a profound effect on the immune system. Furthermore, it has been demonstrated that physical exercise exerts a positive impact on the nervous system, learning and memory (Inoue et al., 2015; Faria et al., 2016; Faria et al., 2018). Urinary proteomics of athletes after training and competition were analyzed in previous studies (Kohler et al., 2009; McCullough et al., 2011). To the best of our knowledge, there are very few studies on global urinary proteomes after daily exercise. Swimming is a popular physical activity and an effective option for maintaining and improving cardiovascular health. Recent studies have shown that swimming is beneficial for mental health and cognitive ability (Hillman, Castelli & Buck, 2005; Da Silva et al., 2020). Rats have the innate ability to swim and are the first choice for swimming models (Souza et al., 2009). The purpose of this study was to explore the changes of urine proteome in rats with regular swimming exercise.

In this study, experimental rats were subjected to daily moderate-intensity swimming exercise for 40 min per day for 7 weeks. Urine samples were collected at weeks 2, 5, and 7. The urine proteome was analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The experimental design and workflow of the proteomics analysis in this study are shown in Fig. 1.

The experimental design and workflow of the proteomics analysis in this study.

Figure 1: The experimental design and workflow of the proteomics analysis in this study.

The experimental rats were subjected to daily moderate-intensity swimming exercise for 7 weeks. Urine samples were collected at weeks 2, 5, and 7 during swimming exercise. Urine proteins were identified by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS).

Materials & Methods

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).

Results

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.

Proteomic analysis of the urine samples of swimming exercise rats.

Figure 2: Proteomic analysis of the urine samples of swimming exercise rats.

(A) PCA analysis of all proteins from experimental and control urine proteome at week 2. (B) Cluster analysis of all the proteins from experimental and control urine proteome at week 2.

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.

Differential proteins identified between experimental and control group.

Figure 3: Differential proteins identified between experimental and control group.

(A) Volcano plots showing P values (−log10) versus protein ratios between experimental and control rats (log2) at week 2. (B) Volcano plots showing P values (−log10) versus protein ratios between experimental and control rats (log2) at week 5. (C) Volcano plots showing P values (−log10) versus protein ratios between experimental and control rats (log2) at week 7. (D) Overlap evaluation of differential proteins at three time points.
Table 1:
The details of differential proteins identified at three time points.
Uniprot ID Protein names Human ortholog P-value Fold change
Week 2 Week 5 Week 7
P20059 Hemopexin P02790 0.039679 2.501 1.896 3.514
P24268 Cathepsin D P07339 0.000272 0.605 0.420 0.453
P02767 Transthyretin P02766 0.049310 0.575 0.316 0.618
P20759 Ig gamma-1 chain C region P01859 0.003593 0.346 0.322 0.483
Q00657 Chondroitin sulfate proteoglycan 4 Q6UVK1 0.000090 0.235 0.254 0.419
Q63556 Serine protease inhibitor A3M P01011 0.021564 3.103 0.479
P27590 Uromodulin P07911 0.002444 2.642 0.453
Q6IFW6 Keratin, type I cytoskeletal 10 P13645 0.048471 2.172 2.019
O70534 Protein delta homolog 1 P80370 0.048375 1.828 0.463
P82450 Sialate O-acetylesterase Q9HAT2 0.007297 1.623 0.647
P47820 Angiotensin-converting enzyme P12821 0.018595 0.642 0.303
Q6AYS7 Aminoacylase-1A Q03154 0.023841 0.532 2.322
P02651 Apolipoprotein A-IV P06727 0.029010 0.508 0.362
P13635 Ceruloplasmin P00450 0.002251 0.479 0.501
Q64319 Neutral and basic amino acid transport protein rBAT Q07837 0.025707 0.465 2.484
B5DFC9 Nidogen-2 Q14112 0.005150 0.423 0.392
P20761 Ig gamma-2B chain C region NO 0.001365 0.338 1.785
P00689 Pancreatic alpha-amylase NO 0.015215 1.763 5.916
P29975 Aquaporin-1 P29972 0.016488 0.569 0.334
P52759 2-iminobutanoate/2-iminopropanoate deaminase P52758 0.006852 0.569 0.589
P08721 Osteopontin P10451 0.048467 0.541 0.247
O70513 Galectin-3-binding protein Q08380 0.000185 0.468 0.507
P01015 Angiotensinogen P01019 0.003462 0.440 2.158
P14046 Alpha-1-inhibitor 3 NO 0.040909 0.529 0.446
P10960 Prosaposin P07602 0.001299 0.491 0.449
Q99041 Protein-glutamine gamma-glutamyltransferase 4 P49221 0.028307 0.306 0.225
Q4V885 Collectin-12 Q5KU26 0.014972 0.273 0.305
P05544 Serine protease inhibitor A3L P01011 0.000894 3.651
P28648 CD63 antigen P08962 0.007863 3.366
P84039 Ectonucleotide pyrophosphatase/phosphodiesterase family member 5 Q9UJA9 0.028089 3.202
P05545 Serine protease inhibitor A3K P01011 0.003344 3.111
O89117 Beta-defensin 1 P60022 0.015008 2.999
P32038 Complement factor D P00746 0.021309 2.288
P15950 Glandular kallikrein-3, submandibular NO 0.010257 2.152
P20909 Collagen alpha-1 P12107 0.021608 2.129
Q63514 C4b-binding protein alpha chain P04003 0.008936 2.104
D3ZTX0 Transmembrane emp24 domain-containing protein 7 Q9Y3B3 0.011501 2.073
P49134 Integrin beta-1 P05556 0.011293 1.988
Q76HN1 Hyaluronidase-1 Q12794 0.011380 1.982
Q6RY07 Acidic mammalian chitinase Q9BZP6 0.028264 1.965
P05539 Collagen alpha-1 P02458 0.040399 1.950
Q6AXR4 Beta-hexosaminidase subunit beta P07686 0.018512 1.949
Q9R1T3 Cathepsin Z Q9UBR2 0.034738 1.942
Q5XIL0 E3 ubiquitin-protein ligase RNF167 Q9H6Y7 0.025554 1.891
P48199 C-reactive protein P02741 0.025494 1.716
P08649 Complement C4 P0C0L4 0.032173 1.686
P50430 Arylsulfatase B P15848 0.000462 1.575
Q6AYP5 Cell adhesion molecule 1 Q9BY67 0.020851 1.534
Q00238 Intercellular adhesion molecule 1 P05362 0.025199 0.662
Q920H8 Hephaestin Q9BQS7 0.003917 0.659
B0BND0 Glycerophosphocholine cholinephosphodiesterase ENPP6 Q6UWR7 0.030004 0.636
Q6Q0N1 Cytosolic non-specific dipeptidase Q96KP4 0.006146 0.636
P29598 Urokinase-type plasminogen activator P00749 0.019588 0.630
Q8R5M3 Leucine-rich repeat-containing protein 15 Q8TF66 0.043831 0.608
Q62638 Golgi apparatus protein 1 Q92896 0.014649 0.607
P31044 Phosphatidylethanolamine-binding protein 1 NO 0.046012 0.606
Q9QX79 Fetuin-B Q9UGM5 0.009853 0.602
Q5U367 Multifunctional procollagen lysine hydroxylase and glycosyltransferase LH3 O60568 0.020139 0.586
Q5U2Q3 Ester hydrolase C11orf54 homolog Q9H0W9 0.045189 0.584
P35704 Peroxiredoxin-2 P32119 0.000841 0.581
P46413 Glutathione synthetase P48637 0.021466 0.573
Q4FZV0 Beta-mannosidase O00462 0.000078 0.571
Q99MA2 Xaa-Pro aminopeptidase 2 O43895 0.005837 0.569
Q63530 Phosphotriesterase-related protein Q96BW5 0.026074 0.567
P48500 Triosephosphate isomerase P60174 0.004231 0.566
P19804 Nucleoside diphosphate kinase B P22392 0.023348 0.564
P27139 Carbonic anhydrase 2 P00918 0.008439 0.548
P51647 Retinal dehydrogenase 1 P00352 0.004435 0.538
P04639 Apolipoprotein A-I P02647 0.001486 0.524
P51635 Aldo-keto reductase family 1 member A1 P14550 0.021157 0.509
P69897 Tubulin beta-5 chain P07437 0.019827 0.504
P53813 Vitamin K-dependent protein S P07225 0.021142 0.503
P62963 Profilin-1 P07737 0.042120 0.501
P08650 Complement C5 NO 0.000712 0.496
Q9QXQ0 Alpha-actinin-4 O43707 0.013840 0.494
O55004 Ribonuclease 4 P34096 0.008646 0.492
P85971 6-phosphogluconolactonase O95336 0.013814 0.488
P19112 Fructose-1,6-bisphosphatase 1 P09467 0.029793 0.480
Q62930 Complement component C9 P02748 0.004371 0.464
P60711 Actin, cytoplasmic 1 P60709 0.002492 0.462
P22282 Cystatin-related protein 1 NO 0.043085 0.460
P42123 L-lactate dehydrogenase B chain P07195 0.001504 0.460
P08289 Alkaline phosphatase, tissue-nonspecific isozyme P05186 0.013575 0.460
P05964 Protein S100-A6 P06703 0.008557 0.459
P00884 Fructose-bisphosphate aldolase B P05062 0.035549 0.457
P50399 Rab GDP dissociation inhibitor beta P50395 0.006541 0.453
P02770 Albumin P02768 0.002136 0.451
Q63716 Peroxiredoxin-1 Q06830 0.001691 0.449
Q06496 Sodium-dependent phosphate transport protein 2A Q06495 0.013307 0.448
P41562 Isocitrate dehydrogenase [NADP] cytoplasmic O75874 0.014681 0.422
P01041 Cystatin-B P04080 0.012801 0.421
P04642 L-lactate dehydrogenase A chain P00338 0.000113 0.415
Q9Z339 Glutathione S-transferase omega-1 P78417 0.008351 0.415
D4ACX8 Protocadherin-16 Q96JQ0 0.039161 0.408
Q9WTW7 Solute carrier family 23 member 1 Q9UHI7 0.038746 0.402
P17475 Alpha-1-antiproteinase P01009 0.000491 0.396
P50115 Protein S100-A8 P05109 0.009132 0.389
Q6P734 Plasma protease C1 inhibitor P05155 0.000603 0.388
P34080 Aquaporin-2 P41181 0.010950 0.383
P20762 Ig gamma-2C chain C region NO 0.014244 0.382
Q5FVQ0 Metal cation symporter ZIP8 Q9C0K1 0.001720 0.376
P15978 Class I histocompatibility antigen, Non-RT1.A alpha-1 chain P01891 0.000287 0.370
P34058 Heat shock protein HSP 90-beta P08238 0.019878 0.344
P09006 Serine protease inhibitor A3N P01011 0.000536 0.341
P04276 Vitamin D-binding protein P02774 0.005304 0.335
Q66HG4 Galactose mutarotase Q96C23 0.000467 0.326
Q63772 Growth arrest-specific protein 6 Q14393 0.002087 0.323
P50116 Protein S100-A9 P06702 0.022965 0.290
P00697 Lysozyme C-1 P61626 0.013674 0.279
P12346 Serotransferrin P02787 0.000095 0.220
P01026 Complement C3 P01024 0.006223 0.200
P06866 Haptoglobin P00739 0.000036 0.193
Q64268 Heparin cofactor 2 P05546 0.000109 0.164
Q9EQV9 Carboxypeptidase B2 Q96IY4 0.000091 0.156
Q63313 Lipopolysaccharide-binding protein P18428 0.000977 0.141
P15399 Probasin NO 0.000027 0.115
Q6IFV1 Keratin, type I cytoskeletal 14 P02533 0.048522 2.314
Q642A7 Protein FAM151A Q8WW52 0.000571 0.641
P23680 Serum amyloid P-component P02743 0.001278 0.622
P70490 Lactadherin Q08431 0.002590 0.619
P15083 Polymeric immunoglobulin receptor P01833 0.018502 0.617
P16391 RT1 class I histocompatibility antigen, AA alpha chain NO 0.041460 0.590
P36373 Glandular kallikrein-7, submandibular/renal P06870 0.001770 0.582
Q05820 Putative lysozyme C-2 P61626 0.006730 0.575
Q63041 Alpha-1-macroglobulin NO 0.004656 0.574
P26051 CD44 antigen P16070 0.034146 0.560
P61972 Nuclear transport factor 2 P61970 0.029915 0.550
Q63621 Interleukin-1 receptor accessory protein Q9NPH3 0.031691 0.546
P97829 Leukocyte surface antigen CD47 Q08722 0.010609 0.543
Q6AYE5 Out at first protein homolog Q86UD1 0.033694 0.541
P13221 Aspartate aminotransferase, cytoplasmic P17174 0.000894 0.521
P54759 Ephrin type-A receptor 7 Q15375 0.004971 0.518
P16573 Carcinoembryonic antigen-related cell adhesion molecule 1 P13688 0.017399 0.506
P36376 Glandular kallikrein-12, submandibular/renal P06870 0.049426 0.500
Q9WUK5 Inhibin beta C chain P55103 0.003809 0.497
Q9EPB1 Dipeptidyl peptidase 2 Q9UHL4 0.000194 0.486
Q9R0D6 Transcobalamin-2 P20062 0.033115 0.484
P13852 Major prion protein P04156 0.033316 0.479
P43303 Interleukin-1 receptor type 2 P27930 0.002016 0.474
Q9R0T4 Cadherin-1 P12830 0.003331 0.473
Q794F9 4F2 cell–surface antigen heavy chain P08195 0.026565 0.450
Q64604 Receptor-type tyrosine-protein phosphatase F P10586 0.004355 0.444
Q6IUU3 Sulfhydryl oxidase 1 O00391 0.003978 0.412
Q7TPB4 CD276 antigen Q5ZPR3 0.000390 0.403
P11232 Thioredoxin P10599 0.009423 0.386
P98158 Low-density lipoprotein receptor-related protein 2 P98164 0.040045 0.373
P53369 7,8-dihydro-8-oxoguanine triphosphatase P36639 0.000018 0.354
P35859 Insulin-like growth factor-binding protein complex acid labile subunit P35858 0.026722 0.351
P07154 Procathepsin L P07711 0.033555 0.341
Q91XT9 Neutral ceramidase Q9NR71 0.039975 0.281
Q0PMD2 Anthrax toxin receptor 1 Q9H6X2 0.048291 0.276
Q30 kJ2 Beta-defensin 50 0.018575 0.251
P52796 Ephrin-B1 P98172 0.045230 0.224
P32736 Opioid-binding protein/cell adhesion molecule Q14982 0.038970 0.156
P97580 Beta-microseminoprotein P08118 0.025914 0.110
P06760 Beta-glucuronidase P08236 0.009520 0.095 0.208
P20760 Ig gamma-2A chain C region P01859 0.019815 3.050
P42854 Regenerating islet-derived protein 3-gamma NO 0.003409 2.815
P01836 Ig kappa chain C region, A allele P01834 0.004604 2.299
P20611 Lysosomal acid phosphatase P11117 0.016619 2.073
P01835 Ig kappa chain C region, B allele P01834 0.011652 1.900
Q920A6 Retinoid-inducible serine carboxypeptidase Q9HB40 0.020110 1.885
Q5XI43 Matrix remodeling-associated protein 8 Q9BRK3 0.018083 1.828
Q6P7S1 Acid ceramidase Q13510 0.042780 1.643
Q499T2 Gamma-interferon-inducible lysosomal thiol reductase P13284 0.019485 0.660
P08592 Amyloid-beta A4 protein P05067 0.045641 0.637
P07897 Aggrecan core protein P16112 0.010204 0.629
Q9JHY1 Junctional adhesion molecule A Q9Y624 0.016015 0.615
P04906 Glutathione S-transferase P P09211 0.016839 0.541
Q6MG71 Choline transporter-like protein 4 Q53GD3 0.004089 0.515
P0CG51 Polyubiquitin-B [Cleaved into: Ubiquitin] P0CG47 0.016453 0.504
P02650 Apolipoprotein E P02649 0.040693 0.477
Q6TUD4 Protein YIPF3 Q9GZM5 0.027382 0.472
Q05695 Neural cell adhesion molecule L1 P32004 0.027649 0.411
P10247 H-2 class II histocompatibility antigen gamma chain P04233 0.031337 0.362
Q9JJ19 Na(+)/H(+) exchange regulatory cofactor NHE-RF1 O14745 0.022332 0.297
Q62632 Follistatin-related protein 1 Q12841 0.007274 0.282
Q5M871 Fas apoptotic inhibitory molecule 3 O60667 0.027711 0.279
Q9WUC4 Copper transport protein ATOX1 O00244 0.008730 0.275
Q06880 Neuroblastoma suppressor of tumorigenicity 1 P41271 0.000421 0.254
Q63467 Trefoil factor 1 P04155 0.000552 0.234
P07171 Calbindin P05937 0.007365 0.193
Q09030 Trefoil factor 2 Q03403 0.003471 0.168
P97574 Stanniocalcin-1 P52823 0.023304 0.087
DOI: 10.7717/peerj.12406/table-1

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.

Tissue distribution of the human orthologs of differential proteins.

Figure 4: Tissue distribution of the human orthologs of differential proteins.

X-axis represents human tissues; Y- axis represents the number of differential proteins.

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.

Functional enrichment analysis of differential proteins in this study.

Figure 5: Functional enrichment analysis of differential proteins in this study.

(A) Biological process (B) Cellular component (C) Molecular function (D) Canonical pathways.

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.

Discussion

In this study, daily moderate-intensity swimming exercise rat model was established. Compared to the control group, a total of 112, 61 and 44 differential proteins were identified after 2, 5 and 7 weeks of swimming exercise, respectively. Randomized grouping statistical analysis showed that more than 85% of the differential proteins identified in this study were caused by swimming exercise rather than random allocation.

By biological process analysis, we found that some processes of differential proteins were consistent with previous researches. For example, some immune-related processes were enriched after swimming exercise. Exercise has a profound effect on immune system function, and studies have shown that regular moderate intensity exercise is beneficial for immunity (Pedersen & Hoffman-Goetz, 2000; Simpson et al., 2015). Furthermore, we found that positive regulation of cholesterol esterification was enriched after swimming exercise in this study. Regular physical exercise provides a wide range of cardiovascular benefits as a nonpharmacological treatment and promotes cholesterol esterification and transport from peripheral tissues to the liver (Simko & Kelley, 1979; Mann, Beedie & Jimenez, 2014).

Additionally, some pathways were previously reported to be associated with physical exercise. For example, sphingosine-1-phosphate (S1P) plays an important role in skeletal muscle pathophysiology, and S1P metabolism was found to be regulated by exercise (Hodun, Chabowski & Baranowski, 2021). The S1P content in plasma and its receptors in skeletal muscles were reported to be increased in the skeletal muscle of rats after resistance training (Banitalebi et al., 2013). Sphingosine and sphingosine-1-phosphate metabolism were enriched in the urine after swimming exercise in this study. Additionally, carbohydrates are the most efficient fuel for working muscles. The first metabolic pathways of carbohydrate metabolism are skeletal muscle glycogenolysis and glycolysis, and circulating glucose becomes an important energy source. Lactate was also reported to play a primary role as either a direct or indirect energy source for contracting skeletal muscle. We found that some glucose metabolism-related pathways were enriched in urine. Furthermore, glutamate has been implicated in exhaustive or vigorous exercise (Guezennec et al., 1998), and a study showed that glutamate increased significantly in the visual cortex following exercise (Maddock et al., 2016). In this study, we found that glutamate degradation II was enriched in urine following moderate-intensity exercise. Overall, the urine proteome can reflect changes associated with physical exercise.

This study was a preliminary study with a limited number of rats, and the differential proteins identified in this study require further verification in a large number of human urine samples. Urine proteomes after different lengths of exercise were different, suggesting that urine proteomics may distinguish long-term and short-term responses to exercise. Additionally, this is a starting point for further studies of urinary proteome after different types and intensities of exercise to monitor the amount of exercise and to develop an optimal exercise plan. Physical exercise may be an influencing factor in urine proteomics research. When using human urine samples to discover disease biomarkers, physical exercise-related effects can be excluded in future studies.

Conclusions

Our results revealed that the urinary proteome could reflect significant changes after swimming exercise. These findings may provide an approach to monitor the effects of exercise of the body.

Supplemental Information

Unsupervised clustering analysis of all of proteins identified at three time points

DOI: 10.7717/peerj.12406/supp-1

The variable isolation window of the DIA method with 36 windows was set for DIA acquisition

DOI: 10.7717/peerj.12406/supp-2

The identification and quantification details of proteins identified in this study

DOI: 10.7717/peerj.12406/supp-3

The results of randomized grouping statistical analysis

DOI: 10.7717/peerj.12406/supp-4

ARRIVE 2.0 checklist

DOI: 10.7717/peerj.12406/supp-5
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