Urinary Proteome Changes in Global Cerebral Ischemia-Reperfusion Injury Rat Model Using Proteomics

Urinary Proteome Changes in Global Cerebral Ischemia-Reperfusion Injury Rat Model Using Proteomics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Urinary Proteome Changes in Global Cerebral Ischemia-Reperfusion Injury Rat Model Using Proteomics Xiaopeng Sun, Qiujie Li, Mingshan Wang, Weiwei Qin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1006901/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cerebral ischemia-reperfusion (I/R) injury is the leading cause of death in severe hypotension caused by cardiac arrest, drowning, and excessive blood loss. Urine can sensitively reflect pathophysiological changes in the brain even at an early stage. Methods In this study, a rat model of global cerebral I/R injury was established via Pulsinelli’s four-vessel occlusion (4-VO) method. The proteomics techniques of data-independent acquisition (DIA) and parallel reaction monitoring (PRM) were applied to profile the urinary proteome. The differentially expressed proteins were subjected to Gene Ontology (GO) and protein-protein interaction (PPI) analysis. Results One hundred and sixty-four proteins significantly differed in the 4-VO rat urine samples compared to the control samples (1.5-fold change, p<0.05). GO analysis showed that the acute-phase response, the ERK1 and ERK2 cascade, endopeptidase activity, blood coagulation, and angiogenesis were overrepresented. After PRM validation, fifteen differentially expressed proteins were identified, and their expression was consistent with the DIA quantification. The abundance of FGG, COMP, TFF2, and HG2A was significantly changed only at 12 h after I/R injury. APOE, FAIM3, FZD1, IL1R2, UROK and CD48 were upregulated only at 48 h after I/R injury. KNG1, CATZ, PTGDS, PRVA and HEPC showed an overall trend of upregulation or downregulation at 12 and 48 h after I/R injury, reflecting the progression of cerebral I/R injury. Conclusion In this study, fifteen differentially expressed urinary proteins were identified and validated in a 4-VO rat model. Eight of these proteins were reported to be associated with cerebral I/R injury. These findings provide important clues to inform the monitoring of cerebral I/R injury and further the current understanding of its molecular biological mechanisms. Neurobiology of Disease General Biochemistry global cerebral ischemia-reperfusion 4-VO rats urine proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Global cerebral ischemia-reperfusion (I/R) injury is the leading cause of death in severe hypotension caused by cardiac arrest, drowning, and excessive blood loss [1]. Because the brain is very sensitive to hypoxia, the brain tissue sustains irreversible damage 4 to 6 min after the loss of circulation, especially in highly metabolically active regions such as the hippocampus, thalamus, cerebral cortex, and striatum [2]. Global cerebral I/R injury is closely associated with neurological dysfunction, from mild cognitive impairment to a minimally conscious state or even a persistent vegetative state [3]. According to reports, up to 80% of survivors are in a vegetative state, and patients who recover well often have serious psychological complications such as depression, anxiety, and posttraumatic stress disorder, which seriously affect their quality of life [4]. To date, the diagnosis and evaluation of global cerebral I/R injury have mainly relied on clinical symptoms, neuroimaging, electrophysiology, and biochemical examination of blood or cerebrospinal fluid. However, hemodynamic instability, sedation and hypothermia are common in these patients, limiting the application of these tests [5]. The main clinical treatment methods include supportive therapy, symptomatic treatment, mild hypothermia and hyperbaric oxygen therapy. There is still a lack of specific, effective neuroprotective strategies and drugs. Therefore, early and accurate assessment of the degree of brain injury and prognosis is critical for diagnosis and treatment. Currently, blood and cerebrospinal fluid are the main sources of samples for the study of brain injury biomarkers. Although many candidate protein biomarkers have been reported in blood and cerebrospinal fluid, they have not been further applied [6, 7]. There are several drawbacks to focusing on these fluids: 1) An invasive sampling process is necessary to obtain blood or cerebrospinal fluid, and the latter is especially difficult to obtain. 2) Blood and cerebrospinal fluid are important components of the internal environment; therefore, they are strictly controlled by homeostatic mechanisms to maintain the relative stability of their components. When a certain change is introduced into the internal environment, the body will reduce this change as much as possible and eliminate it through various mechanisms to keep its composition relatively stable. Therefore, changes in biomarkers in blood and cerebrospinal fluid are not sufficiently sensitive. 3) Due to technical limitations, the sample size and the extent of protein identification in previous studies were relatively small, and the conclusions have not been extensively validated. Thus, simple, noninvasive, and sensitive biomarkers are needed. According to the largest human urine proteome database including 6085 sequence of protein information, some of these proteins have been reported to be enriched in 32 tissues and organs, such as the brain, gastrointestinal tract, kidneys and so on [8]. Among these proteins, 1956 that were enriched in brain tissue were identified in urine; this number ranked first among the 32 tissues and organs evaluated. Several studies have shown that urine can reflect the pathophysiology of some neurological diseases, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and neuroendocrine tumors [9]. In addition, in a glioblastoma rat model and a Walker 256 lateral ventricle inoculation rat model, the urine proteome changed significantly before clinical symptoms and brain histopathological changes appeared [10, 11]. Overall, urine is a good source of specimens for the study of brain injury markers, and it can provide a sensitive reflection of brain pathophysiology at an early stage. In this study, the proteomics technique of data-independent acquisition (DIA) was used to profile the urinary proteome in a global cerebral I/R rat model, and then the altered proteins were validated by using the parallel reaction monitoring (PRM) strategy. A summary of the overall experimental approach is presented in Figure 1 . This study aims to explore urinary protein biomarkers of global cerebral I/R injury and provide clues to further understand its molecular biological mechanisms. Results And Discussion 1. Histopathological Damage in the Hippocampus To evaluate ischemia and histological damage, H&E staining was conducted on brain sections of the hippocampal CA1 region, which is most well known as being selectively vulnerable following ischemia. H&E staining indicated that no neuron morphology abnormalities were observed in the sham group (Fig. 2A). At reperfusion for 12 h, the number of neurons was reduced, the structure was complete, and no typical apoptotic cells were observed (Fig. 2B). With the reperfusion time prolonged to 48 h, the number of intact neurons decreased significantly, shrunken cell bodies and nuclear pyknosis (Fig. 2C). 2. Urine Proteome Changes To preliminarily investigate how the urine proteome changes with I/R progression, twenty-one urine samples from the sham and I/R group (12 h and 48 h) were analyzed via a label-free DIA workflow. To generate spectral library A, fractions separated with a spin column were analyzed by DDA-MS and then processed using Proteome Discoverer (version 2.3) and Spectronaut Pulsar X. The library included eight DDA analyses of fractions resulting in 1152 protein groups and 6260 peptides with at least one unique peptide and a Q value<0.01. Raw DIA-MS data files acquired with 30 refined isolation windows from the twenty-one individual urine samples were loaded into Spectronaut Pulsar X. Overall, a total of 866 (699 on average) protein groups were identified from twenty-one biological replicates. All identification and quantitation details are listed in supporting Table S3. One hundred and sixty-four proteins significantly differed in the urine samples compared to the control samples (1.5-fold change, p<0.05). There were 59 and 123 altered urinary proteins at 12 and 48 h, respectively, after I/R (Table S4-5). Eighteen proteins changed significantly at both 12 and 48 h (Table 1). Among these proteins, 7 showed an overall upregulated or downregulated trend: T-kininogen 2, Prostaglandin-H2 D-isomerase, 14-3-3 protein theta, Ig gamma-2B chain C region, Cathepsin Z, Parvalbumin alpha and Hepcidin (highlighted in red in Table 1). This may suggest that these proteins have the potential to be used for the early detection of cerebral I/R injury. Table 1. The urinary proteins that were consistently altered at 12 and 48 h after I/R injury. UniProt ID Protein name 12 h 48 h FC p-value FC p-value P62260 14-3-3 protein epsilon 2.9 4.5E-02 2.3 2.1E-02 D3ZTV3 Leucine-rich repeat transmembrane protein FLRT2 2.8 3.1E-02 2.3 2.7E-03 Q6P9T8 Tubulin beta-4B chain 2.8 3.3E-02 2.3 6.8E-03 Q5ZQU0 Sushi, nidogen and EGF-like domain-containing protein 1 2.3 3.8E-02 2.3 1.9E-02 P08932 T-kininogen 2 1.6 3.6E-02 2.3 3.2E-03 P22057 Prostaglandin-H2 D-isomerase -1.5 4.4E-02 -2.1 8.5E-03 Q9EQX6 Platelet-derived growth factor C -1.6 3.8E-02 -1.6 1.5E-02 O89117 Beta-defensin 1 -1.6 3.0E-02 -1.5 3.4E-02 Q9WVH8 Fibulin-5 -1.7 8.1E-03 -1.7 2.2E-02 P68255 14-3-3 protein theta -1.7 2.0E-02 -2.4 4.3E-03 P14046 Alpha-1-inhibitor 3 -2.1 1.6E-02 -1.9 2.4E-02 P20761 Ig gamma-2B chain C region -2.2 2.9E-02 -2.4 2.1E-02 P05544 Serine protease inhibitor A3L -2.3 5.2E-03 -1.6 4.6E-02 Q9R1T3 Cathepsin Z -2.0 9.0E-03 -2.2 1.3E-02 O70534 Protein delta homolog 1 -2.3 2.6E-04 -1.9 3.3E-03 P02625 Parvalbumin alpha -3.2 2.0E-02 -4.6 1.3E-02 Q99MH3 Hepcidin -3.4 1.1E-03 -4.7 1.6E-02 P17559 Uteroglobin -4.7 4.6E-02 -3.1 3.9E-02 “-” means a downward trend. 3. Function Annotation of Differentially Abundant Proteins The functional annotation of differentially abundant proteins at 12 and 48 h consisted of sorting them into the “biological process”, “cellular component” and “molecular function” categories using DAVID (Figure 3). One hundred and sixty-four differentially abundant proteins were annotated. In the biological process category, cell-matrix adhesion, positive regulation of ERK1 and ERK2 cascade, cellular response to interleukin-6, negative regulation of endothelial cell apoptotic process, and acute-phase response were overrepresented at 12 h after 12 h I/R. At 48 h after I/R, negative regulation of endopeptidase activity, aging, negative regulation of blood coagulation, angiogenesis, and innate immune response were overrepresented (Fig. 3A). In the molecular function category, receptor binding, serine-type endopeptidase inhibitor activity, cell adhesion molecule binding, and protein binding were overrepresented at both time points. Oxygen transporter activity was overrepresented at 12 h after I/R (Fig. 3B). In the cellular component category, most of these differentially abundant proteins were associated with extracellular exosomes, extracellular space, blood microparticles, and the external side of the plasma membrane (Fig. 3C). 4. Protein-Protein Interactions of Differentially Abundant Proteins To better understand the pathogenic mechanisms in global cerebral I/R, a protein-protein interaction (PPI) network for 164 changed proteins was constructed using STRING (Fig. 4). The STRING PPI network analysis showed that the average node degree was 3.73, the average local clustering coefficient was 0.466, and the PPI enrichment p-value was less than 1.0E-16. The above results revealed that these proteins had more interactions among themselves than would be expected for a random set of proteins of similar size. Such an enrichment pattern indicates that the proteins are at least partially biologically connected as a group in I/R. 5. PRM Validation and Analysis of Differentially Abundant Proteins In the validation phase, 255 peptides corresponding to seventy-one proteins were finally scheduled for PRM-MS analysis in another 21 urine samples. Overall, thirty-two proteins were significantly altered at multiple time points (1.5-fold change, p<0.05) (Table S6). After the p-value was adjusted by the Benjamini & Hochberg method, filtering yielded fifteen proteins with an adjusted p-value<0.05, including 8 increased and 7 decreased proteins (Table 2). The expression trends of the corresponding proteins were consistent with the results from the DIA discovery quantification. Table 2. Differentially abundant urinary proteins validated by PRM quantification. UniProt ID Human homolog Protein name Trend Related to brain I/R injury P02680 P02679 Fibrinogen gamma chain ↑ Plasma [12, 13], brain [14] P35444 P49747 Cartilage oligomeric matrix protein ↓ Q09030 Q03403 Trefoil factor 2 ↓ Plasma [15] P10247 P04233 H-2 class II histocompatibility antigen gamma chain ↓ P02650 P02649 Apolipoprotein E ↑ Brain [16], plasma [17] Q5M871 O60667 Fas apoptotic inhibitory molecule 3 ↑ Q08463 Q9UP38 Frizzled-1 ↑ Brain [18] P43303 P27930 Interleukin-1 receptor type 2 ↑ P29598 P00749 Urokinase-type plasminogen activator ↑ P10252 P09326 CD48 antigen ↑ P08932 P01042 T-kininogen 1 ↑ Blood [19] Q9R1T3 Q9UBR2 Cathepsin Z ↓ Plasma [15] P22057 P41222 Prostaglandin-H2 D-isomerase ↓ P02625 P20472 Parvalbumin alpha ↓ Hippocampus [20] Q99MH3 P81172 Hepcidin ↓ Plasma [21, 22], brain [23] Four proteins were changed significantly only at 12 h after I/R, when no obvious histopathological changes had yet appeared; these proteins included Fibrinogen gamma chain (FGG), Cartilage oligomeric matrix protein, Trefoil factor 2, and H-2 class II histocompatibility antigen gamma chain. These differentially abundant proteins may provide important clues for the early diagnosis of cerebral I/R injury. Two of these differentially abundant proteins were associated with cerebral I/R injury. Plasma fibrinogen was independently associated with overall ischemic stroke and all subtypes, both in the acute stage (p < 0.001) and at the three-month follow-up (p < 0.05) [12]. In a case-control study, FGG was associated with a reduced risk of ischemic stroke [13]. FGG was also highly expressed in the ischemic penumbra of focal cerebral ischemia rats [14]. Trefoil factor 2 was upregulated in the plasma of atrial fibrillation patients with ischemic stroke [15]. Six proteins were upregulated only at 48 h after I/R injury, when there was certain histopathological damage in the hippocampus; these proteins included Apolipoprotein E, Fas apoptotic inhibitory molecule 3, Frizzled-1, Interleukin-1 receptor type 2, Urokinase-type plasminogen activator and CD48 antigen. These differentially abundant proteins may indicate the extent of cerebral I/R injury. Two of these differentially abundant proteins were associated with cerebral I/R injury. Apolipoprotein E (APOE) is the primary apolipoprotein synthesized in the brain in response to ischemia-reperfusion injury with known neuroprotective effects exerted through antioxidant, anti-inflammatory, anti-excitotoxic, and neurotrophic mechanisms [16]. The APOE genotype showed a positive dose-response association with ischemic stroke in people of European ancestry [17]. Frz1 expression was significantly decreased in the brain tissue of middle cerebral artery occlusion rats [18]. Five proteins showed an overall upregulated or downregulated trend at 12 and 48 h after I/R injury, including T-kininogen 1, Cathepsin Z, Prostaglandin-H2 D-isomerase, Parvalbumin alpha and Hepcidin (Fig. 5). These differential proteins may reflect the progression of cerebral I/R injury. Three of these differential proteins were associated with cerebral I/R injury. The mRNA expression of T-kininogen 1 was upregulated in the blood of idiopathic thrombophilia [19]. Cathepsin Z was upregulated in the plasma of atrial fibrillation patients with ischemic stroke [15]. Parvalbumin alpha protein and mRNA were reduced in the hippocampal tissue of C57BL6 mice with unilateral right carotid ligation. Plasma/serum hepcidin levels were significantly higher in acute ischemic stroke patients than in the control group [21, 22]. The hepcidin mRNA levels and hepcidin/prohepcidin protein levels are upregulated in the ischemic brain [23]. Conclusion In this study, fifteen differentially abundant urinary proteins were identified and validated in 4-VO rats. Eight of the differential proteins were reported to be associated with cerebral I/R injury. These findings may provide important clues for the monitoring of cerebral I/R injury and further understanding of its molecular biology mechanisms. Methods 1. Animals Forty-two male Wistar rats (200–250 g) were purchased from Charles River China (Beijing, China). All animals were maintained on a standard laboratory diet with a controlled indoor temperature (21±2℃), humidity (65–70%) and 12/12 h light–dark cycle conditions. The animal experiments were reviewed and approved by the Qingdao University Hospital Medical Ethics Committee (No.027-2019). All methods were carried out in accordance with relevant guidelines and regulations of the National Health Commission and the Ministry of Science and Technology and conformed to the guidelines for animal research. The Wistar rats were randomly divided into two groups: a control group (n = 21) and an I/R group (n = 21). Ischemia was induced by Pulsinelli’s four-vessel occlusion method [24]. Briefly, the bilateral common carotid arteries were surgically exposed and clamped shut with microclips for 10 min. The experiment was conducted in two phases; for details, see Figure 1. For the discovery phase, differentially abundant urinary proteins were identified by label-free DIA quantification in twenty-one independent samples from the control group (7 samples) and the I/R group at 12 and 48 h (7 samples per time point). For the validation phase, the 21 remaining urine samples (7 from the control group and 7 per time point from the I/R group at 12 and 48 h) were evaluated by targeted quantification with PRM. 2. Histological Analysis For histopathology, three rats in the I/R group and three rats in the control group were randomly sacrificed at 12 h and 48 h after I/R. The hippocampus was harvested and then quickly fixed in 10% neutral buffered formalin. The formalin-fixed tissues were embedded in paraffin, sectioned (4 mm) and stained with hematoxylin and eosin (H&E) to reveal histopathological lesions. 3. Urine Collection and Sample Preparation Urine samples were collected from the control and I/R groups at 12 and 48 h after I/R. Rats were individually placed in metabolic cages for six hours. During urine collection, food was withheld from the rats to prevent the urine from being contaminated. After collection, the urine samples were immediately centrifuged at 2 000 g for 30 min at 4°C and then stored at −80°C. Urinary protein extraction: Urine samples were centrifuged at 12 000 g for 30 min at 4°C. Six volumes of prechilled acetone were added after the pellets were removed, and the samples were precipitated at 4°C overnight. 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 each sample was measured by a Bradford protein assay. Tryptic digestion: The proteins were digested with trypsin (Promega, USA) using filter-aided sample preparation methods [25]. Briefly, 100 µg of the protein sample was loaded onto a 10-kDa filter unit (Pall, USA). The protein solution was reduced with 4.5 mM DTT for 1 h at 37°C and then alkylated with 10 mM indoleacetic acid for 30 min at room temperature in the dark. The proteins were digested with trypsin (enzyme-to-protein ratio of 1:50) for 14 h at 37°C. The peptides were desalted on Oasis HLB cartridges (Waters, USA) and lyophilized for trap column fractionation and LC-MS/MS analysis. 4. Spin Column Separation To generate a spectral library for DIA analysis, pooled peptide samples from all samples were fractionated using a high-pH reversed-phase peptide fractionation kit (Thermo Pierce, USA) according to the manufacturer’s instructions. Briefly, 60 µg of a pooled peptide sample was loaded onto the spin column. A step gradient of increasing acetonitrile concentrations was applied to the column to elute the bound peptides. Ten different fractions were collected by centrifugation, including the flow-through fraction, the wash fraction and eight step gradient sample fractions (5, 7.5, 10, 12.5, 15, 17.5, 20 and 50% acetonitrile). The fractionated samples were dried completely and resuspended in 20 μL of 0.1% formic acid. Three microliters of each of the fractions was loaded for LC–data-dependent acquisition (DDA)–MS/MS analysis. 5. LC-MS/MS Setup for DDA and DIA An Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific, Germany) was coupled with an EASY-nLC 1000 HPLC system (Thermo Scientific, Germany). For DDA-MS and data-independent acquisition (DIA)–MS modes, the same LC settings were used for retention time stability. The digested peptides were dissolved in 0.1% formic acid and loaded onto a trap column (75 µm × 2 cm, 3 µm, C18, 100 A°). The eluent was transferred to a reversed-phase analytical column (50 µm × 250 mm, 2 µm, C18, 100 A°). The eluted gradient was 5–30% buffer B (0.1% formic acid in 80% acetonitrile; flow rate of 0.8 μL/min) for 90 min. To enable fully automated and sensitive signal processing, the calibration kit (iRT kit from Biognosys, Switzerland) reagent was spiked at a concentration of 1:20 v/v in all samples. The iRT kit reagent was spiked into the urinary peptides for spectral library generation. Additionally, before the real DIA runs, the iRT kit reagent was also spiked into all urinary samples. For the generation of the spectral library, the ten fractions from the spin column were analyzed in DDA-MS mode. The parameters were set as follows: the full scan was acquired from 350 to 1 550 m/z at 60 000, the cycle time was set to 3 s (top speed mode), the automatic gain control (AGC) was set to 1E6, and the maximum injection time was set to 50 ms. MS/MS scans were acquired in the Orbitrap at a resolution of 15,000 with an isolation window of 2 Da and collision energy of 32% (higher-energy collisional dissociation, HCD); the AGC target was set to 5E4, and the maximum injection time was 30 ms. For the DIA-MS method, forty individual samples were analyzed in DIA mode. For MS acquisition, the variable isolation window DIA method with 26 windows was developed (Table S1). The full scan was set at a resolution of 60,000 over an m/z range of 350 to 1,200, followed by DIA scans with a resolution of 30,000, HCD collision energy of 32%, AGC target of 1E6 and maximal injection time of 50 ms. 6. LC-MS/MS Setup for PRM In the discovery phase, seventy-one differentially abundant urinary proteins were identified by the label-free DIA proteomic method. All of these proteins were evaluated by the PRM-MS method in the remaining twenty-one urine samples. LC-PRM-MS/MS data were acquired in an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific, Germany) coupled with an EASY-nLC 1200 HPLC system (Thermo Scientific, Germany). For the generation of the PRM spectral library, pooled peptide samples were analyzed in DDA-MS mode 6 times. The peptides were loaded on a reversed-phase trap column (75 µm × 2 cm, 3 µm, C18, 100 Å, Thermo Scientific, Germany), and the eluent was then transferred to a reversed-phase analytical column (50 µm × 250 mm, 2 µm, C18, 100 Å, Thermo Scientific, Germany). The elution gradient consisted of 5–35% buffer B (0.1% formic acid in 80% acetonitrile; flow rate 0.8 μL/min) for 90 min. The MS parameters were set as follows: the full scan was acquired from 350 to 1 550 m/z at 60 000, the cycle time was set to 3 s (top speed mode), the AGC was set to 1E6, and the maximum injection time was set to 50 ms. MS/MS scans were acquired using the Orbitrap at a resolution of 30 000 with an isolation window of 1.6 Da and collision energy at 30% (HCD), the AGC target was set to 5E4, and the maximum injection time was 60 ms. For the PRM-MS method, thirty-two individual samples were analyzed in PRM mode. Ultimately, 255 peptides were scheduled, and the retention time (RT) segment was set to 8 min for each targeted peptide (Table S2). The normalized collision energy was fixed ats 30%, and the quadrupole isolation window was fixed at 1.6 Da. The other parameters were the same as described in the last paragraph. 7. Label-Free DIA Quantification Analysis To generate the spectral library, the raw data files acquired for the ten fractions in DDA mode were processed using Proteome Discoverer (version 2.3; Thermo Scientific, Germany) with SEQUEST HT against the SwissProt Rattus database (released in May 2019, containing 8086 sequences) appended with the iRT peptide sequences. The search parameters consisted of a parent ion mass tolerance of 10 ppm; fragment ion mass tolerance of 0.02 Da; fixed modification of carbamidomethylated cysteine (+58.00 Da); and variable modifications of oxidized methionine (+15.995 Da) and deamidated glutamine and asparagine (+0.984 Da). For other settings, the default parameters were used. A false discovery rate (FDR) cutoff of 0.01 was applied at the protein level. The results were then imported to Spectronaut™ Pulsar (Biognosys, Switzerland) software to generate the spectral library [26]. The raw DIA-MS files were imported into Spectronaut Pulsar with the default settings. In brief, a dynamic window for the XIC extraction window and a nonlinear iRT calibration strategy were used. Mass calibration was set to local mass calibration. Cross-run normalization was enabled to correct for systematic variance in LC-MS performance, and a local normalization strategy was used [27]. Protein inference, which gave rise to the protein groups, was performed on the principle of parsimony using the ID picker algorithm as implemented in Spectronaut Pulsar [28]. All 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 the respective fragment ions for MS2. Student’s t-test was applied with a significance criterion of 1.5 were used as the criteria for the identification of differentially expressed proteins. 8. PRM-MS Quantification Analysis Skyline (version 3.6.1 10279) [8] was used to build the spectrum library and filter peptides for PRM analysis. For each targeted protein, 2-6 associated peptides were selected using the following rules: (i) identification in the untargeted analysis with a q value <1%, (ii) complete digestion by trypsin, (iii) containing 8–18 amino acid residues, (iv) exclusion of the first 25 amino acids at the N-terminus of proteins, and (v) fixed carbamidomethylation of cysteine. Prior to individual sample analysis, pooled peptide samples were subjected to PRM experiments to refine the target list. Finally, forty-four proteins with 255 peptides (Table S2) were scheduled. The RT segment was set to 8 min for each targeted peptide with its expected RT in the center based on the pooled sample analysis. All of the PRM-MS data were processed with Skyline. By comparing the same peptide across runs, the RT location and integration boundaries were adjusted manually to exclude interfering regions. Each protein’s intensity was quantitated using the summation of intensities from its corresponding transitions. The transition settings were as follows: precursor charges +2, +3; ion charge +1; ion type b, y, p; product ions from ion 3 to last ion -1; automatically select all matching transitions; ion match tolerance 0.02 m/z; select the 6 most intense product ions. The details of the transition are listed in supporting Table S2. Prior to the statistical analysis, the quantified protein intensities were normalized according to the summed intensity. The differentially abundant proteins were selected using one-way ANOVA, and p-values were adjusted by the Benjamini & Hochberg method. Significance was defined by a p-value of < 0.05 and a fold change of 1.5. 9. Bioinformatics Analysis Bioinformatics analysis was carried out to better study the biological function of the dysregulated proteins. The Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8 (https://david.ncifcrf.gov/) was used to perform the functional annotation of the differentially abundant urinary proteins identified at 12 and 48 h. In this study, significant GO enrichment was defined as p<0.05. Protein-protein interaction networks were constructed using the STRING database (http://www.string-db.org), which is a database of known and predicted protein interactions, including direct (physical) and indirect (functional) associations. Declarations Author Contributions X.S., W.Q. and M.W. conceived and designed the experiments. X.S. and Q.L. performed the experiments. X.S. and W.Q. analyzed the data and wrote the manuscript. All authors approved the final manuscript. Competing Interests The authors declare that they have no competing interests. Acknowledgments This research was supported by the National Natural Science Foundation of China (82000881). Statement on ARRIVE guidelines : We declared that this study was carried out in compliance with the ARRIVE guidelines. References Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, et al: Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation 2017, 135: e146-e603. Sanganalmath SK, Gopal P, Parker JR, Downs RK, Parker JC, Jr., Dawn B: Global cerebral ischemia due to circulatory arrest: insights into cellular pathophysiology and diagnostic modalities. Mol Cell Biochem 2017, 426: 111-127. Bajan K: Can we predict neurological outcome following out-of-hospital cardiac arrest (OHCA) and therapeutic hypothermia? Indian J Crit Care Med 2015, 19: 301-303. Shao F, Li CS, Liang LR, Li D, Ma SK: Outcome of out-of-hospital cardiac arrests in Beijing, China. Resuscitation 2014, 85: 1411-1417. Nguyen PL, Alreshaid L, Poblete RA, Konye G, Marehbian J, Sung G: Targeted Temperature Management and Multimodality Monitoring of Comatose Patients After Cardiac Arrest. Front Neurol 2018, 9: 768. Rech TH, Vieira SR, Nagel F, Brauner JS, Scalco R: Serum neuron-specific enolase as early predictor of outcome after in-hospital cardiac arrest: a cohort study. Crit Care 2006, 10: R133. Al-Mufti F, Amuluru K, Smith B, Damodara N, El-Ghanem M, Singh IP, Dangayach N, Gandhi CD: Emerging Markers of Early Brain Injury and Delayed Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage. World Neurosurg 2017, 107: 148-159. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ: Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26: 966-968. An M, Gao Y: Urinary Biomarkers of Brain Diseases. Genomics Proteomics Bioinformatics 2015, 13: 345-354. Zhang L, Li Y, Meng W, Ni Y, Gao Y: Dynamic urinary proteomic analysis in a Walker 256 intracerebral tumor model. Cancer Med 2019, 8: 3553-3565. Ni Y, Zhang F, An M, Yin W, Gao Y: Early candidate biomarkers found from urine of glioblastoma multiforme rat before changes in MRI. Sci China Life Sci 2018, 61: 982-987. Jood K, Danielson J, Ladenvall C, Blomstrand C, Jern C: Fibrinogen gene variation and ischemic stroke. J Thromb Haemost 2008, 6: 897-904. Cheung EY, Uitte de Willige S, Vos HL, Leebeek FW, Dippel DW, Bertina RM, de Maat MP: Fibrinogen gamma' in ischemic stroke: a case-control study. Stroke 2008, 39: 1033-1035. Zou Y, Gong P, Zhao W, Zhang J, Wu X, Xin C, Xiong Z, Li Z, Wu X, Wan Q, et al: Quantitative iTRAQ-based proteomic analysis of piperine protected cerebral ischemia/reperfusion injury in rat brain. Neurochem Int 2019, 124: 51-61. Hijazi Z, Wallentin L, Lindback J, Alexander JH, Connolly SJ, Eikelboom JW, Ezekowitz MD, Granger CB, Lopes RD, Pol T, et al: Screening of Multiple Biomarkers Associated With Ischemic Stroke in Atrial Fibrillation. J Am Heart Assoc 2020, 9: e018984. Tukhovskaya EA, Yukin AY, Khokhlova ON, Murashev AN, Vitek MP: COG1410, a novel apolipoprotein-E mimetic, improves functional and morphological recovery in a rat model of focal brain ischemia. J Neurosci Res 2009, 87: 677-682. Khan TA, Shah T, Prieto D, Zhang W, Price J, Fowkes GR, Cooper J, Talmud PJ, Humphries SE, Sundstrom J, et al: Apolipoprotein E genotype, cardiovascular biomarkers and risk of stroke: systematic review and meta-analysis of 14,015 stroke cases and pooled analysis of primary biomarker data from up to 60,883 individuals. Int J Epidemiol 2013, 42: 475-492. Matei N, Camara J, McBride D, Camara R, Xu N, Tang J, Zhang JH: Intranasal wnt3a Attenuates Neuronal Apoptosis through Frz1/PIWIL1a/FOXM1 Pathway in MCAO Rats. J Neurosci 2018, 38: 6787-6801. Sabater-Lleal M, Martinez-Perez A, Buil A, Folkersen L, Souto JC, Bruzelius M, Borrell M, Odeberg J, Silveira A, Eriksson P, et al: A genome-wide association study identifies KNG1 as a genetic determinant of plasma factor XI Level and activated partial thromboplastin time. Arterioscler Thromb Vasc Biol 2012, 32: 2008-2016. Chavez-Valdez R, Emerson P, Goffigan-Holmes J, Kirkwood A, Martin LJ, Northington FJ: Delayed injury of hippocampal interneurons after neonatal hypoxia-ischemia and therapeutic hypothermia in a murine model. Hippocampus 2018, 28: 617-630. Slomka A, Switonska M, Zekanowska E: Hepcidin Levels Are Increased in Patients with Acute Ischemic Stroke: Preliminary Report. J Stroke Cerebrovasc Dis 2015, 24: 1570-1576. Petrova J, Manolov V, Vasilev V, Tzatchev K, Marinov B: Ischemic stroke, inflammation, iron overload - Connection to a hepcidin. Int J Stroke 2016, 11: NP16-17. Ding H, Yan CZ, Shi H, Zhao YS, Chang SY, Yu P, Wu WS, Zhao CY, Chang YZ, Duan XL: Hepcidin is involved in iron regulation in the ischemic brain. PLoS One 2011, 6: e25324. Pulsinelli WA, Buchan AM: The four-vessel occlusion rat model: method for complete occlusion of vertebral arteries and control of collateral circulation. Stroke 1988, 19: 913-914. Wisniewski JR, Zougman A, Nagaraj N, Mann M: Universal sample preparation method for proteome analysis. Nat Methods 2009, 6: 359-362. Bruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher C, et al: Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics 2015, 14: 1400-1410. Callister SJ, Barry RC, Adkins JN, Johnson ET, Qian WJ, Webb-Robertson BJ, Smith RD, Lipton MS: Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res 2006, 5: 277-286. Zhang B, Chambers MC, Tabb DL: Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. J Proteome Res 2007, 6: 3549-3557. Additional Declarations No competing interests reported. Supplementary Files Supportingtables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1006901","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":60891305,"identity":"bb646a6b-d720-4b0a-a8d2-7a767ecc0e16","order_by":0,"name":"Xiaopeng Sun","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaopeng","middleName":"","lastName":"Sun","suffix":""},{"id":60891307,"identity":"2d8e06d6-7b1a-4d49-93d4-adb9c2304c34","order_by":1,"name":"Qiujie Li","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiujie","middleName":"","lastName":"Li","suffix":""},{"id":60891308,"identity":"91d7b5dd-33c5-4383-bf72-0ba163e9aba1","order_by":2,"name":"Mingshan Wang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingshan","middleName":"","lastName":"Wang","suffix":""},{"id":60891309,"identity":"5380dca5-619a-4429-a720-36023dbca551","order_by":3,"name":"Weiwei Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFACxgYGBgMGHgb2xsaHH0jTwnO42ViCNNsk0tsEeIhRaHC8uU3iQ4G1jLnkwzYGCQY7Od0GQlrOHGyTnGGQzmM5O7HtQQFDsrHZAQJazG4ktt3mMTjMY3A7sd1AguFA4jaCWu4/bLv9B6Tl5sE2CR6itNxgbLvNANICZBCnxf5MYvvPHqBfDM4kAgPZgAi/SLYff2zw44+1vcHx4w8ffqiwkyOoBQqYobQBccqRtYyCUTAKRsEowAIAJBlEBUf09LYAAAAASUVORK5CYII=","orcid":"","institution":"Qingdao University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2021-10-22 08:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1006901/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1006901/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15313821,"identity":"9e80282c-247e-49f2-9e41-2de43fe27864","added_by":"auto","created_at":"2021-11-08 15:09:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":227664,"visible":true,"origin":"","legend":"Workflow of this study.","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/dac744a45fbf16f9d47e47df.png"},{"id":15313823,"identity":"68c8fba5-dde1-496b-a494-eb1b211a3f77","added_by":"auto","created_at":"2021-11-08 15:09:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":724436,"visible":true,"origin":"","legend":"H\u0026E staining assessment of neuronal morphology in the hippocampal CA1 area. A: The sham group showed an orderly arrangement of neurons with complete cell structure; B: The I/R 12 h group showed a reduced number of neurons, but their structure was complete, and there were no typical apoptotic cells; C: The I/R 48 h group showed a disorderly arrangement of cells, and the cytoplasm and nucleoli were stained deeply, indicating nuclear pyknosis. Scale bars=100 µm.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/5df376a651720e7898a7992f.png"},{"id":15313908,"identity":"e5d47e91-2469-4f7d-a59b-66155a4ce1d4","added_by":"auto","created_at":"2021-11-08 15:12:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":198186,"visible":true,"origin":"","legend":"Functional analysis of differentially abundant proteins at 12 h and 48 h after I/R. A) Biological process category; B) Molecular function category; C) Cellular component category.","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/b324c125a3047b0cb0403d2f.png"},{"id":15313353,"identity":"561f6901-2a0a-4c17-bec6-1d6960de1c19","added_by":"auto","created_at":"2021-11-08 15:06:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554914,"visible":true,"origin":"","legend":"STRING PPI network analysis of the 164 differentially abundant proteins in I/R rats. The average node degree was 3.73, the average local clustering coefficient was 0.466, and the PPI enrichment p-value was less than 1.0E-16.","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/08e17091b8e8ec1a7e9df62b.png"},{"id":15313351,"identity":"e5d679a5-8e92-4dcd-8cc8-2f36d944776d","added_by":"auto","created_at":"2021-11-08 15:06:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124058,"visible":true,"origin":"","legend":"Abundance of candidate urine biomarkers in I/R rats by PRM quantification. The x‐axis represents different groups, and the y‐axis represents the area of intensity based on PRM quantification. * p\u003c0.05.","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/cedd31af4939ab76d27f9d19.png"},{"id":16826055,"identity":"c45aa9ef-b04b-4451-a921-adf3a90f8d86","added_by":"auto","created_at":"2021-12-29 10:59:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2945378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/2a9bebe1-cd2e-4d1e-9112-5ec47f9bf454.pdf"},{"id":15313348,"identity":"525528c4-ab58-42bb-ad50-a825cc4bee9c","added_by":"auto","created_at":"2021-11-08 15:06:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":245508,"visible":true,"origin":"","legend":"","description":"","filename":"Supportingtables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1006901/v1/a066ad529171ccdbe8bdd82c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eUrinary Proteome Changes in Global Cerebral Ischemia-Reperfusion Injury Rat Model Using Proteomics\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal cerebral ischemia-reperfusion (I/R) injury is the leading cause of death in severe hypotension caused by cardiac arrest, drowning, and excessive blood loss [1]. Because the brain is very sensitive to hypoxia, the brain tissue sustains irreversible damage 4 to 6 min after the loss of circulation, especially in highly metabolically active regions such as the hippocampus, thalamus, cerebral cortex, and striatum [2]. Global cerebral I/R injury is closely associated with neurological dysfunction, from mild cognitive impairment to a minimally conscious state or even a persistent vegetative state [3]. According to reports, up to 80% of survivors are in a vegetative state, and patients who recover well often have serious psychological complications such as depression, anxiety, and posttraumatic stress disorder, which seriously affect their quality of life [4]. To date, the diagnosis and evaluation of global cerebral I/R injury have mainly relied on clinical symptoms, neuroimaging, electrophysiology, and biochemical examination of blood or cerebrospinal fluid. However, hemodynamic instability, sedation and hypothermia are common in these patients, limiting the application of these tests [5]. The main clinical treatment methods include supportive therapy, symptomatic treatment, mild hypothermia and hyperbaric oxygen therapy. There is still a lack of specific, effective neuroprotective strategies and drugs. Therefore, early and accurate assessment of the degree of brain injury and prognosis is critical for diagnosis and treatment.\u003c/p\u003e \u003cp\u003eCurrently, blood and cerebrospinal fluid are the main sources of samples for the study of brain injury biomarkers. Although many candidate protein biomarkers have been reported in blood and cerebrospinal fluid, they have not been further applied [6, 7]. There are several drawbacks to focusing on these fluids: 1) An invasive sampling process is necessary to obtain blood or cerebrospinal fluid, and the latter is especially difficult to obtain. 2) Blood and cerebrospinal fluid are important components of the internal environment; therefore, they are strictly controlled by homeostatic mechanisms to maintain the relative stability of their components. When a certain change is introduced into the internal environment, the body will reduce this change as much as possible and eliminate it through various mechanisms to keep its composition relatively stable. Therefore, changes in biomarkers in blood and cerebrospinal fluid are not sufficiently sensitive. 3) Due to technical limitations, the sample size and the extent of protein identification in previous studies were relatively small, and the conclusions have not been extensively validated. Thus, simple, noninvasive, and sensitive biomarkers are needed.\u003c/p\u003e \u003cp\u003eAccording to the largest human urine proteome database including 6085 sequence of protein information, some of these proteins have been reported to be enriched in 32 tissues and organs, such as the brain, gastrointestinal tract, kidneys and so on [8]. Among these proteins, 1956 that were enriched in brain tissue were identified in urine; this number ranked first among the 32 tissues and organs evaluated. Several studies have shown that urine can reflect the pathophysiology of some neurological diseases, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and neuroendocrine tumors [9]. In addition, in a glioblastoma rat model and a Walker 256 lateral ventricle inoculation rat model, the urine proteome changed significantly before clinical symptoms and brain histopathological changes appeared [10, 11]. Overall, urine is a good source of specimens for the study of brain injury markers, and it can provide a sensitive reflection of brain pathophysiology at an early stage.\u003c/p\u003e \u003cp\u003eIn this study, the proteomics technique of data-independent acquisition (DIA) was used to profile the urinary proteome in a global cerebral I/R rat model, and then the altered proteins were validated by using the parallel reaction monitoring (PRM) strategy. A summary of the overall experimental approach is presented in Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study aims to explore urinary protein biomarkers of global cerebral I/R injury and provide clues to further understand its molecular biological mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results And Discussion","content":"\n\u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\u003cp\u003e\u003cstrong\u003e1. Histopathological Damage in\u0026nbsp;the Hippocampus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate ischemia and histological damage, H\u0026amp;E staining was conducted on brain sections of the hippocampal CA1 region, which is most well known as being selectively vulnerable following ischemia. H\u0026amp;E staining indicated that no neuron morphology abnormalities were observed in the sham group (Fig. 2A). At reperfusion for 12 h, the number of neurons was reduced, the structure was complete, and no typical apoptotic cells were observed (Fig. 2B). With the reperfusion time prolonged to 48 h, the number of intact neurons decreased significantly, shrunken cell bodies and nuclear pyknosis (Fig. 2C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Urine Proteome Changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo\u0026nbsp;preliminarily\u0026nbsp;investigate how the urine proteome changes with I/R progression, twenty-one urine samples from\u0026nbsp;the sham\u0026nbsp;and I/R group (12 h\u0026nbsp;and\u0026nbsp;48 h) were analyzed via\u0026nbsp;a\u0026nbsp;label-free DIA workflow.\u003c/p\u003e\n\u003cp\u003eTo generate spectral library A, fractions separated with a spin column were analyzed by DDA-MS and then processed using Proteome Discoverer (version 2.3) and Spectronaut Pulsar X.\u0026nbsp;The library\u0026nbsp;included eight DDA analyses of fractions resulting\u0026nbsp;in\u0026nbsp;1152 protein groups and 6260 peptides with at least one unique\u0026nbsp;peptide\u0026nbsp;and a Q value\u0026lt;0.01. Raw DIA-MS data files acquired with 30 refined isolation windows from the twenty-one individual urine samples were loaded into Spectronaut Pulsar X. Overall, a total of 866 (699\u0026nbsp;on\u0026nbsp;average) protein groups were identified from twenty-one biological replicates. All identification and quantitation details are listed in supporting Table S3.\u003c/p\u003e\n\u003cp\u003eOne hundred and sixty-four proteins significantly differed in the urine samples compared to the control\u0026nbsp;samples\u0026nbsp;(1.5-fold change, p\u0026lt;0.05).\u0026nbsp;There were 59 and 123 altered urinary proteins at 12 and 48 h, respectively, after I/R (Table S4-5). Eighteen proteins changed significantly at both 12 and\u0026nbsp;48 h\u0026nbsp;(Table 1).\u0026nbsp;Among these proteins, 7 showed an overall upregulated or downregulated trend: T-kininogen 2, Prostaglandin-H2 D-isomerase, 14-3-3 protein theta, Ig gamma-2B chain C region, Cathepsin Z, Parvalbumin alpha and Hepcidin (highlighted in red in Table 1). This may suggest\u0026nbsp;that these proteins have the potential to be used for the early detection of cerebral I/R injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. The urinary proteins that were\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econsistently\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;altered at 12 and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e48 h\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;after I/R injury.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"12.690355329949238%\"\u003e\n \u003cp\u003eUniProt ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"51.43824027072758%\"\u003e\n \u003cp\u003eProtein name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.597292724196276%\"\u003e\n \u003cp\u003e12 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"18.274111675126903%\"\u003e\n \u003cp\u003e48 h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"31.132075471698112%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.0188679245283%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP62260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003e14-3-3 protein epsilon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e4.5E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e2.1E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eD3ZTV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eLeucine-rich repeat transmembrane protein FLRT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e3.1E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e2.7E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eQ6P9T8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eTubulin beta-4B chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.16751269035533%\"\u003e\n \u003cp\u003e3.3E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e6.8E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eQ5ZQU0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eSushi, nidogen and EGF-like domain-containing protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e3.8E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e1.9E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP08932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eT-kininogen 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e3.6E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.429780033840948%\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e3.2E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP22057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eProstaglandin-H2 D-isomerase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e4.4E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e8.5E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eQ9EQX6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003ePlatelet-derived growth factor C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.16751269035533%\"\u003e\n \u003cp\u003e3.8E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e1.5E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eO89117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eBeta-defensin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e3.0E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e3.4E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eQ9WVH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eFibulin-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.16751269035533%\"\u003e\n \u003cp\u003e8.1E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e2.2E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP68255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003e14-3-3 protein theta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e2.0E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e4.3E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP14046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eAlpha-1-inhibitor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e1.6E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e2.4E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP20761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eIg gamma-2B chain C region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e2.9E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e2.1E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP05544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eSerine protease inhibitor A3L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e5.2E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e4.6E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eQ9R1T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eCathepsin Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.16751269035533%\"\u003e\n \u003cp\u003e9.0E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e1.3E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eO70534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eProtein delta homolog 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e2.6E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e3.3E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP02625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eParvalbumin alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e2.0E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e1.3E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eQ99MH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eHepcidin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.16751269035533%\"\u003e\n \u003cp\u003e1.1E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e1.6E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP17559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.43824027072758%\"\u003e\n \u003cp\u003eUteroglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.16751269035533%\"\u003e\n \u003cp\u003e4.6E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.429780033840948%\"\u003e\n \u003cp\u003e-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.844331641285956%\"\u003e\n \u003cp\u003e3.9E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026ldquo;-\u0026rdquo; means a downward trend.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e \u003cstrong\u003eFunction Annotation of Differentially Abundant Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe functional annotation of differentially abundant proteins at 12 and 48 h consisted of sorting them into the \u0026ldquo;biological process\u0026rdquo;, \u0026ldquo;cellular component\u0026rdquo; and \u0026ldquo;molecular function\u0026rdquo; categories using DAVID (Figure 3). One hundred and sixty-four differentially abundant proteins were annotated. In the biological process category, cell-matrix adhesion,\u0026nbsp;positive regulation of ERK1 and ERK2 cascade, cellular response to interleukin-6, negative regulation of endothelial cell apoptotic process, and acute-phase response were\u0026nbsp;overrepresented at 12 h after 12 h I/R. At 48 h after I/R, negative regulation of endopeptidase activity, aging, negative regulation of blood coagulation, angiogenesis, and innate immune response were\u0026nbsp;overrepresented (Fig. 3A).\u003c/p\u003e\n\u003cp\u003eIn the molecular function category, receptor binding, serine-type endopeptidase inhibitor activity, cell adhesion molecule binding, and protein binding were overrepresented at both time points. Oxygen transporter activity was overrepresented at 12 h after I/R (Fig. 3B). In the cellular component category, most of these differentially abundant proteins were associated with extracellular exosomes, extracellular space, blood microparticles, and the external side of the plasma membrane (Fig. 3C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Protein-Protein Interactions of Differentially Abundant Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the pathogenic mechanisms in global cerebral I/R, a protein-protein interaction (PPI) network for 164 changed proteins was constructed using STRING (Fig. 4). The STRING PPI network analysis showed that the average node degree was 3.73, the average local clustering coefficient was 0.466, and the PPI enrichment p-value was less than 1.0E-16. The above results revealed that these proteins had more interactions among themselves than would be expected for a random set of proteins of similar size. Such an enrichment pattern indicates that the proteins are at least partially biologically connected as a group in I/R.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003e5. PRM Validation and Analysis of Differentially Abundant Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the validation phase, 255 peptides corresponding to seventy-one proteins were finally scheduled for PRM-MS analysis in another 21 urine samples. Overall, thirty-two proteins were\u0026nbsp;significantly\u0026nbsp;altered\u0026nbsp;at multiple time points (1.5-fold change, p\u0026lt;0.05) (Table S6). After\u0026nbsp;the\u0026nbsp;p-value\u0026nbsp;was\u0026nbsp;adjusted by\u0026nbsp;the\u0026nbsp;Benjamini \u0026amp; Hochberg method, filtering yielded fifteen proteins with\u0026nbsp;an adjusted\u0026nbsp;p-value\u0026lt;0.05, including 8 increased and 7 decreased proteins (Table 2). The expression trends of the corresponding proteins were consistent with\u0026nbsp;the results from the DIA discovery\u0026nbsp;quantification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Differentially abundant urinary proteins validated by PRM quantification.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eUniProt ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eHuman homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.97902097902098%\"\u003e\n \u003cp\u003eProtein name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.391608391608392%\"\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003eRelated to brain I/R injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP02680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP02679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eFibrinogen gamma chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003ePlasma\u0026nbsp;[12, 13], brain\u0026nbsp;[14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP35444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP49747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eCartilage oligomeric matrix protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eQ09030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eQ03403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eTrefoil factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003ePlasma\u0026nbsp;[15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP10247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP04233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eH-2 class II histocompatibility antigen gamma chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP02650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP02649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eApolipoprotein E\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003eBrain\u0026nbsp;[16], plasma\u0026nbsp;[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eQ5M871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eO60667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eFas apoptotic inhibitory molecule 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eQ08463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eQ9UP38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eFrizzled-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003eBrain\u0026nbsp;[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP43303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP27930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eInterleukin-1 receptor type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP29598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP00749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eUrokinase-type plasminogen activator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP10252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP09326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eCD48 antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP08932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP01042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eT-kininogen 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003eBlood\u0026nbsp;[19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eQ9R1T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eQ9UBR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eCathepsin Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003ePlasma\u0026nbsp;[15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP22057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP41222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eProstaglandin-H2 D-isomerase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eP02625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP20472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eParvalbumin alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003eHippocampus\u0026nbsp;[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.888111888111888%\"\u003e\n \u003cp\u003eQ99MH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.713286713286713%\"\u003e\n \u003cp\u003eP81172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"45.97902097902098%\"\u003e\n \u003cp\u003eHepcidin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.391608391608392%\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.027972027972027%\"\u003e\n \u003cp\u003ePlasma\u0026nbsp;[21, 22], brain\u0026nbsp;[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFour\u0026nbsp;proteins\u0026nbsp;were changed significantly\u0026nbsp;only at 12 h\u0026nbsp;after I/R, when no obvious histopathological changes had yet appeared; these proteins included Fibrinogen gamma chain (FGG), Cartilage oligomeric matrix protein, Trefoil factor 2, and H-2 class II histocompatibility antigen gamma chain. These differentially abundant proteins may provide important clues for the early diagnosis of cerebral I/R injury. Two of these differentially abundant proteins were associated with cerebral I/R injury. Plasma fibrinogen was independently associated with overall ischemic stroke and all subtypes, both in the acute stage (p \u0026lt; 0.001) and at\u0026nbsp;the\u0026nbsp;three-month follow-up (p \u0026lt; 0.05)\u0026nbsp;[12]. In a case-control study, FGG was associated with a reduced risk of ischemic stroke\u0026nbsp;[13]. FGG was also highly expressed in the ischemic penumbra of focal cerebral ischemia rats\u0026nbsp;[14]. Trefoil factor 2 was upregulated in the\u0026nbsp;plasma\u0026nbsp;of atrial fibrillation patients with ischemic stroke\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003eSix proteins were upregulated only at\u0026nbsp;48 h\u0026nbsp;after I/R injury, when\u0026nbsp;there was\u0026nbsp;certain histopathological damage in\u0026nbsp;the\u0026nbsp;hippocampus; these proteins included Apolipoprotein E, Fas apoptotic inhibitory molecule 3, Frizzled-1, Interleukin-1 receptor type 2, Urokinase-type plasminogen activator and CD48 antigen. These differentially abundant proteins may indicate the extent of cerebral I/R injury. Two of these differentially abundant proteins were associated with cerebral I/R injury. Apolipoprotein E (APOE) is the primary apolipoprotein synthesized in the brain in response to ischemia-reperfusion injury with known neuroprotective effects exerted through antioxidant, anti-inflammatory, anti-excitotoxic, and neurotrophic mechanisms\u0026nbsp;[16].\u0026nbsp;The\u0026nbsp;APOE genotype showed a positive dose-response association with ischemic stroke in people of European ancestry\u0026nbsp;[17]. Frz1 expression\u0026nbsp;was significantly decreased\u0026nbsp;in the brain tissue of middle cerebral artery occlusion rats\u0026nbsp;[18].\u003c/p\u003e\n\u003cp\u003eFive proteins showed an overall upregulated or downregulated trend at 12 and 48 h after I/R injury, including T-kininogen 1, Cathepsin Z, Prostaglandin-H2 D-isomerase, Parvalbumin alpha and Hepcidin (Fig. 5). These differential proteins may reflect the progression of cerebral I/R injury. Three of these differential proteins were associated with cerebral I/R injury. The mRNA expression of T-kininogen 1 was upregulated in the blood of idiopathic thrombophilia [19]. Cathepsin Z was upregulated in the plasma of atrial fibrillation patients with ischemic stroke [15]. Parvalbumin alpha protein and mRNA were reduced in the hippocampal tissue of C57BL6 mice with unilateral right carotid ligation. Plasma/serum hepcidin levels were significantly higher in acute ischemic stroke patients than in the control group [21, 22]. The hepcidin mRNA levels and hepcidin/prohepcidin protein levels are upregulated in the ischemic brain [23].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, fifteen differentially abundant urinary proteins were identified and validated in 4-VO rats. Eight of the differential proteins were reported to be associated with cerebral I/R injury. These findings may provide important clues for the monitoring of cerebral I/R injury and further understanding of its molecular biology mechanisms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1. Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForty-two male Wistar rats (200\u0026ndash;250 g) were purchased from Charles River China (Beijing, China). All animals were maintained on a standard laboratory diet with a controlled indoor temperature (21\u0026plusmn;2℃), humidity (65\u0026ndash;70%) and 12/12 h light\u0026ndash;dark cycle conditions. The animal experiments were reviewed and approved by the Qingdao University Hospital Medical Ethics Committee (No.027-2019). All methods were carried out in accordance with relevant guidelines and regulations of the National Health Commission and the Ministry of Science and Technology and conformed to the guidelines for animal research.\u003c/p\u003e\n\u003cp\u003eThe Wistar rats were randomly divided into two groups: a control group (n = 21) and an I/R group (n = 21).\u0026nbsp;Ischemia\u0026nbsp;was induced by Pulsinelli\u0026rsquo;s four-vessel occlusion method\u0026nbsp;[24]. Briefly, the bilateral common carotid arteries were surgically\u0026nbsp;exposed\u0026nbsp;and clamped shut with\u0026nbsp;microclips\u0026nbsp;for 10 min. The experiment was conducted in two phases;\u0026nbsp;for details, see Figure 1. For the discovery phase, differentially abundant urinary proteins were identified by label-free DIA quantification in twenty-one independent samples from the control\u0026nbsp;group (7 samples) and the I/R group at 12 and\u0026nbsp;48 h\u0026nbsp;(7 samples per time point). For the validation phase, the 21 remaining urine samples (7 from the control group and 7 per time point from the I/R group at\u0026nbsp;12\u0026nbsp;and\u0026nbsp;48 h) were evaluated by targeted quantification with PRM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Histological Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor histopathology, three rats in the I/R group and three rats in the control group were randomly sacrificed at\u0026nbsp;12 h\u0026nbsp;and\u0026nbsp;48\u0026nbsp;h\u0026nbsp;after\u0026nbsp;I/R. The hippocampus was harvested and then quickly fixed in 10% neutral buffered formalin. The formalin-fixed tissues were embedded in paraffin, sectioned (4 mm) and stained with hematoxylin and eosin (H\u0026amp;E) to reveal histopathological lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Urine Collection and Sample Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUrine samples were collected from the control and I/R groups at 12 and\u0026nbsp;48 h\u0026nbsp;after\u0026nbsp;I/R. Rats were individually placed in metabolic cages for six hours. During urine collection, food was withheld from the rats to prevent the urine from being contaminated. After collection, the urine samples were immediately centrifuged at 2 000\u003cem\u003eg\u003c/em\u003e for 30 min at 4\u0026deg;C and then stored at\u0026nbsp;\u0026minus;80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUrinary protein extraction:\u003c/em\u003e Urine samples were centrifuged at 12\u0026nbsp;000\u003cem\u003eg\u003c/em\u003e for 30 min at 4\u0026deg;C. Six volumes of prechilled acetone were added after the pellets were removed, and the samples were precipitated at 4\u0026deg;C overnight. 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 each sample was measured by a Bradford protein assay.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTryptic digestion:\u003c/em\u003e The\u0026nbsp;proteins were digested with trypsin (Promega, USA) using filter-aided sample preparation methods\u0026nbsp;[25]. Briefly, 100 \u0026micro;g of the protein sample was loaded onto\u0026nbsp;a\u0026nbsp;10-kDa filter unit (Pall, USA). The protein solution was reduced with 4.5 mM DTT for 1 h at 37\u0026deg;C and then alkylated with 10 mM indoleacetic acid for 30 min at room temperature in the dark. The proteins were digested with trypsin (enzyme-to-protein ratio of 1:50) for 14 h at 37\u0026deg;C. The peptides were desalted on Oasis HLB cartridges (Waters, USA) and lyophilized for trap column fractionation and LC-MS/MS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Spin Column Separation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo\u0026nbsp;generate\u0026nbsp;a spectral library for DIA analysis, pooled\u0026nbsp;peptide samples\u0026nbsp;from all samples were fractionated using a high-pH reversed-phase peptide fractionation kit (Thermo Pierce, USA) according to the manufacturer\u0026rsquo;s instructions. Briefly, 60\u0026nbsp;\u0026micro;g of\u0026nbsp;a pooled\u0026nbsp;peptide\u0026nbsp;sample was loaded onto the spin column. A step gradient of increasing acetonitrile concentrations was applied to the column to elute the bound peptides. Ten different fractions were collected by centrifugation, including the flow-through fraction, the wash fraction and eight step gradient sample fractions (5, 7.5, 10, 12.5, 15, 17.5, 20 and 50%\u0026nbsp;acetonitrile). The fractionated samples were dried completely and resuspended in 20 \u0026mu;L of 0.1% formic acid. Three microliters of each of the fractions was loaded for LC\u0026ndash;data-dependent acquisition (DDA)\u0026ndash;MS/MS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. LC-MS/MS Setup for DDA and DIA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific, Germany) was coupled with an EASY-nLC 1000 HPLC system (Thermo Scientific, Germany).\u0026nbsp;For DDA-MS and data-independent acquisition (DIA)\u0026ndash;MS modes, the same LC settings were used for retention time stability. The digested peptides were dissolved in 0.1% formic acid and loaded\u0026nbsp;onto\u0026nbsp;a trap column (75 \u0026micro;m \u0026times; 2 cm, 3 \u0026micro;m, C18, 100 A\u0026deg;). The eluent was transferred to a reversed-phase analytical column (50 \u0026micro;m \u0026times; 250 mm, 2 \u0026micro;m, C18, 100 A\u0026deg;). The eluted gradient was 5\u0026ndash;30% buffer B (0.1% formic acid in 80% acetonitrile; flow rate of 0.8 \u0026mu;L/min) for 90 min. To enable fully automated and sensitive signal processing, the calibration kit (iRT kit from Biognosys, Switzerland) reagent was spiked at a concentration of 1:20 v/v in all samples. The iRT kit reagent was spiked into the urinary peptides for spectral library generation.\u0026nbsp;Additionally, before the real DIA runs, the iRT kit reagent was also spiked into all urinary samples.\u003c/p\u003e\n\u003cp\u003eFor the generation of the spectral library, the ten fractions from\u0026nbsp;the\u0026nbsp;spin column were analyzed in DDA-MS mode. The parameters were set as follows: the full scan was acquired from 350 to 1 550 m/z at 60 000, the cycle time was set to 3 s (top speed mode),\u0026nbsp;the automatic gain control (AGC) was set to 1E6,\u0026nbsp;and the maximum injection time was set to 50 ms. MS/MS scans were acquired in the Orbitrap at a resolution of 15,000 with an isolation window of 2 Da and collision energy of 32% (higher-energy collisional dissociation, HCD); the AGC target was set to 5E4, and the maximum injection time was 30 ms.\u003c/p\u003e\n\u003cp\u003eFor the DIA-MS method, forty individual samples were analyzed in DIA mode. For MS acquisition, the variable isolation window DIA method with 26 windows was developed (Table S1). The full scan was set at a resolution of 60,000 over an m/z range of 350 to 1,200, followed by DIA scans with a resolution of 30,000, HCD collision energy of 32%, AGC target of 1E6 and maximal injection time of 50 ms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. LC-MS/MS Setup for PRM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the discovery phase, seventy-one\u0026nbsp;differentially\u0026nbsp;abundant urinary proteins were identified by\u0026nbsp;the\u0026nbsp;label-free DIA proteomic method. All\u0026nbsp;of\u0026nbsp;these proteins were evaluated by\u0026nbsp;the\u0026nbsp;PRM-MS\u0026nbsp;method in the\u0026nbsp;remaining\u0026nbsp;twenty-one urine samples. LC-PRM-MS/MS data were acquired in\u0026nbsp;an\u0026nbsp;Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific, Germany) coupled with an EASY-nLC 1200 HPLC system (Thermo Scientific, Germany).\u003c/p\u003e\n\u003cp\u003eFor the generation of the PRM spectral library, pooled peptide samples were analyzed in DDA-MS mode 6 times. The peptides were loaded on a reversed-phase trap column (75 \u0026micro;m \u0026times; 2 cm, 3 \u0026micro;m, C18, 100 \u0026Aring;, Thermo Scientific, Germany), and the eluent was then transferred to a reversed-phase analytical column (50 \u0026micro;m \u0026times; 250 mm, 2 \u0026micro;m, C18, 100 \u0026Aring;, Thermo Scientific, Germany). The elution gradient consisted of 5\u0026ndash;35% buffer B (0.1% formic acid in 80% acetonitrile; flow rate 0.8 \u0026mu;L/min) for 90 min. The MS parameters were set as follows: the full scan was acquired from 350 to 1 550 m/z at 60 000, the cycle time was set to 3 s (top speed mode), the AGC was set to 1E6, and the maximum injection time was set to 50 ms. MS/MS scans were acquired using the Orbitrap at a resolution of 30 000 with an isolation window of 1.6 Da and collision energy at 30% (HCD), the AGC target was set to 5E4, and the maximum injection time was 60 ms.\u003c/p\u003e\n\u003cp\u003eFor the PRM-MS method, thirty-two individual samples were analyzed in PRM mode. Ultimately, 255 peptides were scheduled,\u0026nbsp;and the retention time (RT) segment was set to 8 min for each targeted peptide (Table S2). The normalized collision energy was fixed ats 30%,\u0026nbsp;and the quadrupole isolation window\u0026nbsp;was fixed\u0026nbsp;at 1.6 Da. The other parameters were the same as described in the last paragraph.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Label-Free DIA Quantification Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate the spectral library, the raw data files acquired for the ten fractions in DDA mode were processed using Proteome Discoverer (version 2.3; Thermo Scientific, Germany) with SEQUEST HT against the SwissProt \u003cem\u003eRattus\u003c/em\u003e database (released in May 2019, containing 8086 sequences) appended with the iRT\u0026nbsp;peptide\u0026nbsp;sequences. The\u0026nbsp;search\u0026nbsp;parameters consisted of a parent ion mass tolerance of 10 ppm; fragment ion mass tolerance of 0.02 Da; fixed modification of carbamidomethylated cysteine (+58.00 Da); and variable modifications of oxidized methionine (+15.995 Da) and deamidated glutamine and asparagine (+0.984 Da). For other settings, the default parameters were used. A false discovery rate (FDR) cutoff of 0.01 was applied at the protein level. The results were then imported to Spectronaut\u0026trade; Pulsar (Biognosys, Switzerland) software to generate the spectral library\u0026nbsp;[26].\u003c/p\u003e\n\u003cp\u003eThe raw DIA-MS files were imported into Spectronaut Pulsar with the default settings. In brief, a dynamic window for the XIC extraction window and a nonlinear iRT calibration strategy were used. Mass calibration was set to local mass calibration. Cross-run normalization was enabled to correct for systematic variance in LC-MS performance, and a local normalization strategy was used\u0026nbsp;[27]. Protein inference, which gave rise to the protein groups, was performed on the principle of parsimony using the ID picker algorithm as implemented in Spectronaut Pulsar\u0026nbsp;[28]. All 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 the respective fragment ions for MS2. Student\u0026rsquo;s t-test was applied with a significance criterion of \u0026lt;0.05. A minimum of two peptides matched to a protein and a fold change \u0026gt;1.5 were used as the criteria for the identification of differentially expressed proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. PRM-MS Quantification Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSkyline (version 3.6.1 10279)\u0026nbsp;[8]\u0026nbsp;was used to build the spectrum library and filter peptides for PRM analysis. For each targeted protein, 2-6 associated peptides were selected using the following rules: (i) identification in the untargeted analysis with a q value \u0026lt;1%, (ii)\u0026nbsp;complete digestion\u0026nbsp;by trypsin, (iii) containing 8\u0026ndash;18 amino acid residues, (iv) exclusion of the first 25 amino acids at the N-terminus of proteins, and (v) fixed carbamidomethylation of cysteine. Prior to individual sample analysis, pooled peptide samples were subjected to PRM experiments to refine the target list. Finally, forty-four proteins with 255 peptides (Table S2) were scheduled. The RT segment was set to 8 min for each targeted peptide with its expected RT in the center based on the pooled sample analysis.\u003c/p\u003e\n\u003cp\u003eAll of the PRM-MS data were processed with Skyline. By comparing the same peptide across runs, the RT location and integration boundaries were adjusted manually to exclude interfering regions. Each protein\u0026rsquo;s intensity was quantitated using the summation of intensities from its corresponding transitions. The transition settings were as follows: precursor charges +2, +3; ion charge +1; ion type b, y, p; product ions from ion 3 to last ion -1; automatically select all matching transitions; ion match tolerance 0.02 m/z; select the 6 most intense product ions. The details of the transition are listed in supporting Table S2.\u0026nbsp;Prior to the statistical analysis, the quantified protein intensities were normalized according to the summed intensity. The\u0026nbsp;differentially abundant\u0026nbsp;proteins were selected using\u0026nbsp;one-way ANOVA, and p-values were adjusted by\u0026nbsp;the\u0026nbsp;Benjamini \u0026amp; Hochberg method.\u0026nbsp;Significance was defined by a p-value of \u0026lt; 0.05 and a fold change\u0026nbsp;of\u0026nbsp;1.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Bioinformatics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioinformatics analysis was carried out to better study the biological function of the dysregulated proteins. The Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8 (https://david.ncifcrf.gov/) was used to perform the functional annotation of the differentially abundant urinary proteins identified at 12 and 48 h. In this study, significant GO enrichment was defined as p\u0026lt;0.05. Protein-protein interaction networks were constructed using the STRING database (http://www.string-db.org), which is a database of known and predicted protein interactions, including direct (physical) and indirect (functional) associations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.S., W.Q. and M.W. conceived and designed the experiments. X.S. and Q.L. performed the experiments. X.S. and W.Q. analyzed the data and wrote the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (82000881).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on ARRIVE guidelines\u003c/strong\u003e: We declared that this study was carried out in compliance with the ARRIVE guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBenjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, et al: \u003cstrong\u003eHeart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association.\u003c/strong\u003e \u003cem\u003eCirculation \u003c/em\u003e2017, \u003cstrong\u003e135:\u003c/strong\u003ee146-e603.\u003c/li\u003e\n\u003cli\u003eSanganalmath SK, Gopal P, Parker JR, Downs RK, Parker JC, Jr., Dawn B: \u003cstrong\u003eGlobal cerebral ischemia due to circulatory arrest: insights into cellular pathophysiology and diagnostic modalities.\u003c/strong\u003e \u003cem\u003eMol Cell Biochem \u003c/em\u003e2017, \u003cstrong\u003e426:\u003c/strong\u003e111-127.\u003c/li\u003e\n\u003cli\u003eBajan K: \u003cstrong\u003eCan we predict neurological outcome following out-of-hospital cardiac arrest (OHCA) and therapeutic hypothermia?\u003c/strong\u003e \u003cem\u003eIndian J Crit Care Med \u003c/em\u003e2015, \u003cstrong\u003e19:\u003c/strong\u003e301-303.\u003c/li\u003e\n\u003cli\u003eShao F, Li CS, Liang LR, Li D, Ma SK: \u003cstrong\u003eOutcome of out-of-hospital cardiac arrests in Beijing, China.\u003c/strong\u003e \u003cem\u003eResuscitation \u003c/em\u003e2014, \u003cstrong\u003e85:\u003c/strong\u003e1411-1417.\u003c/li\u003e\n\u003cli\u003eNguyen PL, Alreshaid L, Poblete RA, Konye G, Marehbian J, Sung G: \u003cstrong\u003eTargeted Temperature Management and Multimodality Monitoring of Comatose Patients After Cardiac Arrest.\u003c/strong\u003e \u003cem\u003eFront Neurol \u003c/em\u003e2018, \u003cstrong\u003e9:\u003c/strong\u003e768.\u003c/li\u003e\n\u003cli\u003eRech TH, Vieira SR, Nagel F, Brauner JS, Scalco R: \u003cstrong\u003eSerum neuron-specific enolase as early predictor of outcome after in-hospital cardiac arrest: a cohort study.\u003c/strong\u003e \u003cem\u003eCrit Care \u003c/em\u003e2006, \u003cstrong\u003e10:\u003c/strong\u003eR133.\u003c/li\u003e\n\u003cli\u003eAl-Mufti F, Amuluru K, Smith B, Damodara N, El-Ghanem M, Singh IP, Dangayach N, Gandhi CD: \u003cstrong\u003eEmerging Markers of Early Brain Injury and Delayed Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage.\u003c/strong\u003e \u003cem\u003eWorld Neurosurg \u003c/em\u003e2017, \u003cstrong\u003e107:\u003c/strong\u003e148-159.\u003c/li\u003e\n\u003cli\u003eMacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ: \u003cstrong\u003eSkyline: an open source document editor for creating and analyzing targeted proteomics experiments.\u003c/strong\u003e \u003cem\u003eBioinformatics \u003c/em\u003e2010, \u003cstrong\u003e26:\u003c/strong\u003e966-968.\u003c/li\u003e\n\u003cli\u003eAn M, Gao Y: \u003cstrong\u003eUrinary Biomarkers of Brain Diseases.\u003c/strong\u003e \u003cem\u003eGenomics Proteomics Bioinformatics \u003c/em\u003e2015, \u003cstrong\u003e13:\u003c/strong\u003e345-354.\u003c/li\u003e\n\u003cli\u003eZhang L, Li Y, Meng W, Ni Y, Gao Y: \u003cstrong\u003eDynamic urinary proteomic analysis in a Walker 256 intracerebral tumor model.\u003c/strong\u003e \u003cem\u003eCancer Med \u003c/em\u003e2019, \u003cstrong\u003e8:\u003c/strong\u003e3553-3565.\u003c/li\u003e\n\u003cli\u003eNi Y, Zhang F, An M, Yin W, Gao Y: \u003cstrong\u003eEarly candidate biomarkers found from urine of glioblastoma multiforme rat before changes in MRI.\u003c/strong\u003e \u003cem\u003eSci China Life Sci \u003c/em\u003e2018, \u003cstrong\u003e61:\u003c/strong\u003e982-987.\u003c/li\u003e\n\u003cli\u003eJood K, Danielson J, Ladenvall C, Blomstrand C, Jern C: \u003cstrong\u003eFibrinogen gene variation and ischemic stroke.\u003c/strong\u003e \u003cem\u003eJ Thromb Haemost \u003c/em\u003e2008, \u003cstrong\u003e6:\u003c/strong\u003e897-904.\u003c/li\u003e\n\u003cli\u003eCheung EY, Uitte de Willige S, Vos HL, Leebeek FW, Dippel DW, Bertina RM, de Maat MP: \u003cstrong\u003eFibrinogen gamma\u0026apos; in ischemic stroke: a case-control study.\u003c/strong\u003e \u003cem\u003eStroke \u003c/em\u003e2008, \u003cstrong\u003e39:\u003c/strong\u003e1033-1035.\u003c/li\u003e\n\u003cli\u003eZou Y, Gong P, Zhao W, Zhang J, Wu X, Xin C, Xiong Z, Li Z, Wu X, Wan Q, et al: \u003cstrong\u003eQuantitative iTRAQ-based proteomic analysis of piperine protected cerebral ischemia/reperfusion injury in rat brain.\u003c/strong\u003e \u003cem\u003eNeurochem Int \u003c/em\u003e2019, \u003cstrong\u003e124:\u003c/strong\u003e51-61.\u003c/li\u003e\n\u003cli\u003eHijazi Z, Wallentin L, Lindback J, Alexander JH, Connolly SJ, Eikelboom JW, Ezekowitz MD, Granger CB, Lopes RD, Pol T, et al: \u003cstrong\u003eScreening of Multiple Biomarkers Associated With Ischemic Stroke in Atrial Fibrillation.\u003c/strong\u003e \u003cem\u003eJ Am Heart Assoc \u003c/em\u003e2020, \u003cstrong\u003e9:\u003c/strong\u003ee018984.\u003c/li\u003e\n\u003cli\u003eTukhovskaya EA, Yukin AY, Khokhlova ON, Murashev AN, Vitek MP: \u003cstrong\u003eCOG1410, a novel apolipoprotein-E mimetic, improves functional and morphological recovery in a rat model of focal brain ischemia.\u003c/strong\u003e \u003cem\u003eJ Neurosci Res \u003c/em\u003e2009, \u003cstrong\u003e87:\u003c/strong\u003e677-682.\u003c/li\u003e\n\u003cli\u003eKhan TA, Shah T, Prieto D, Zhang W, Price J, Fowkes GR, Cooper J, Talmud PJ, Humphries SE, Sundstrom J, et al: \u003cstrong\u003eApolipoprotein E genotype, cardiovascular biomarkers and risk of stroke: systematic review and meta-analysis of 14,015 stroke cases and pooled analysis of primary biomarker data from up to 60,883 individuals.\u003c/strong\u003e \u003cem\u003eInt J Epidemiol \u003c/em\u003e2013, \u003cstrong\u003e42:\u003c/strong\u003e475-492.\u003c/li\u003e\n\u003cli\u003eMatei N, Camara J, McBride D, Camara R, Xu N, Tang J, Zhang JH: \u003cstrong\u003eIntranasal wnt3a Attenuates Neuronal Apoptosis through Frz1/PIWIL1a/FOXM1 Pathway in MCAO Rats.\u003c/strong\u003e \u003cem\u003eJ Neurosci \u003c/em\u003e2018, \u003cstrong\u003e38:\u003c/strong\u003e6787-6801.\u003c/li\u003e\n\u003cli\u003eSabater-Lleal M, Martinez-Perez A, Buil A, Folkersen L, Souto JC, Bruzelius M, Borrell M, Odeberg J, Silveira A, Eriksson P, et al: \u003cstrong\u003eA genome-wide association study identifies KNG1 as a genetic determinant of plasma factor XI Level and activated partial thromboplastin time.\u003c/strong\u003e \u003cem\u003eArterioscler Thromb Vasc Biol \u003c/em\u003e2012, \u003cstrong\u003e32:\u003c/strong\u003e2008-2016.\u003c/li\u003e\n\u003cli\u003eChavez-Valdez R, Emerson P, Goffigan-Holmes J, Kirkwood A, Martin LJ, Northington FJ: \u003cstrong\u003eDelayed injury of hippocampal interneurons after neonatal hypoxia-ischemia and therapeutic hypothermia in a murine model.\u003c/strong\u003e \u003cem\u003eHippocampus \u003c/em\u003e2018, \u003cstrong\u003e28:\u003c/strong\u003e617-630.\u003c/li\u003e\n\u003cli\u003eSlomka A, Switonska M, Zekanowska E: \u003cstrong\u003eHepcidin Levels Are Increased in Patients with Acute Ischemic Stroke: Preliminary Report.\u003c/strong\u003e \u003cem\u003eJ Stroke Cerebrovasc Dis \u003c/em\u003e2015, \u003cstrong\u003e24:\u003c/strong\u003e1570-1576.\u003c/li\u003e\n\u003cli\u003ePetrova J, Manolov V, Vasilev V, Tzatchev K, Marinov B: \u003cstrong\u003eIschemic stroke, inflammation, iron overload - Connection to a hepcidin.\u003c/strong\u003e \u003cem\u003eInt J Stroke \u003c/em\u003e2016, \u003cstrong\u003e11:\u003c/strong\u003eNP16-17.\u003c/li\u003e\n\u003cli\u003eDing H, Yan CZ, Shi H, Zhao YS, Chang SY, Yu P, Wu WS, Zhao CY, Chang YZ, Duan XL: \u003cstrong\u003eHepcidin is involved in iron regulation in the ischemic brain.\u003c/strong\u003e \u003cem\u003ePLoS One \u003c/em\u003e2011, \u003cstrong\u003e6:\u003c/strong\u003ee25324.\u003c/li\u003e\n\u003cli\u003ePulsinelli WA, Buchan AM: \u003cstrong\u003eThe four-vessel occlusion rat model: method for complete occlusion of vertebral arteries and control of collateral circulation.\u003c/strong\u003e \u003cem\u003eStroke \u003c/em\u003e1988, \u003cstrong\u003e19:\u003c/strong\u003e913-914.\u003c/li\u003e\n\u003cli\u003eWisniewski JR, Zougman A, Nagaraj N, Mann M: \u003cstrong\u003eUniversal sample preparation method for proteome analysis.\u003c/strong\u003e \u003cem\u003eNat Methods \u003c/em\u003e2009, \u003cstrong\u003e6:\u003c/strong\u003e359-362.\u003c/li\u003e\n\u003cli\u003eBruderer R, Bernhardt OM, Gandhi T, Miladinovic SM, Cheng LY, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher C, et al: \u003cstrong\u003eExtending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.\u003c/strong\u003e \u003cem\u003eMol Cell Proteomics \u003c/em\u003e2015, \u003cstrong\u003e14:\u003c/strong\u003e1400-1410.\u003c/li\u003e\n\u003cli\u003eCallister SJ, Barry RC, Adkins JN, Johnson ET, Qian WJ, Webb-Robertson BJ, Smith RD, Lipton MS: \u003cstrong\u003eNormalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics.\u003c/strong\u003e \u003cem\u003eJ Proteome Res \u003c/em\u003e2006, \u003cstrong\u003e5:\u003c/strong\u003e277-286.\u003c/li\u003e\n\u003cli\u003eZhang B, Chambers MC, Tabb DL: \u003cstrong\u003eProteomic parsimony through bipartite graph analysis improves accuracy and transparency.\u003c/strong\u003e \u003cem\u003eJ Proteome Res \u003c/em\u003e2007, \u003cstrong\u003e6:\u003c/strong\u003e3549-3557.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"global cerebral ischemia-reperfusion, 4-VO rats, urine, proteomics","lastPublishedDoi":"10.21203/rs.3.rs-1006901/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1006901/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCerebral ischemia-reperfusion (I/R) injury is the leading cause of death in severe hypotension caused by cardiac arrest, drowning, and excessive blood loss. Urine can sensitively reflect pathophysiological changes in the brain even at an early stage.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, a rat model of global cerebral I/R injury was established via Pulsinelli\u0026rsquo;s four-vessel occlusion (4-VO) method. The proteomics techniques of data-independent acquisition (DIA) and parallel reaction monitoring (PRM) were applied to profile the urinary proteome. The differentially expressed proteins were subjected to Gene Ontology (GO) and protein-protein interaction (PPI) analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOne hundred and sixty-four proteins significantly differed in the 4-VO rat urine samples compared to the control samples (1.5-fold change, p\u0026lt;0.05). GO analysis showed that the acute-phase response, the ERK1 and ERK2 cascade, endopeptidase activity, blood coagulation, and angiogenesis were overrepresented. After PRM validation, fifteen differentially expressed proteins were identified, and their expression was consistent with the DIA quantification. The abundance of FGG, COMP, TFF2, and HG2A was significantly changed only at 12 h after I/R injury. APOE, FAIM3, FZD1, IL1R2, UROK and CD48 were upregulated only at 48 h after I/R injury. KNG1, CATZ, PTGDS, PRVA and HEPC showed an overall trend of upregulation or downregulation at 12 and 48 h after I/R injury, reflecting the progression of cerebral I/R injury.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this study, fifteen differentially expressed urinary proteins were identified and validated in a 4-VO rat model. Eight of these proteins were reported to be associated with cerebral I/R injury. These findings provide important clues to inform the monitoring of cerebral I/R injury and further the current understanding of its molecular biological mechanisms.\u003c/p\u003e","manuscriptTitle":"Urinary Proteome Changes in Global Cerebral Ischemia-Reperfusion Injury Rat Model Using Proteomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-08 15:06:41","doi":"10.21203/rs.3.rs-1006901/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"93f91d5c-36ba-4af0-8c9d-c565f239c0eb","owner":[],"postedDate":"November 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8361141,"name":"Neurobiology of Disease"},{"id":8361142,"name":"General Biochemistry"}],"tags":[],"updatedAt":"2021-12-29T10:59:06+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-08 15:06:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1006901","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1006901","identity":"rs-1006901","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}