diagnostics Article Urinary Protein Profiling for Potential Biomarkers of Chronic Kidney Disease: A Pilot Study Abduzhappar Gaipov 1,2, *,† , Zhalaliddin Makhammajanov 3,† , Zhanna Dauyey 1 , Zhannur Markhametova 1 , Kamilla Mussina 1 , Assem Nogaibayeva 4 , Larissa Kozina 5 , Dana Auganova 6 , Pavel Tarlykov 6 , Rostislav Bukasov 7 , Zhandos Utegulov 8 , Duman Turebekov 9 , Maria Jose Soler 10,11 , Alberto Ortiz 12 and Mehmet Kanbay 13 1 Department of Medicine, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan 2 Clinical Academic Department of Internal Medicine, CF “University Medical Center”, Astana 010000, Kazakhstan 3 Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan 4 Department of Education, LLP BBNura, Astana 010000, Kazakhstan 5 Department of Laboratory Diagnostics, National Scientific Medical Center, Astana 010000, Kazakhstan 6 Department of Proteomics and Mass Spectrometry, National Center for Biotechnology, Astana 010000, Kazakhstan 7 Department of Chemistry, SSH, Nazarbayev University, Astana 010000, Kazakhstan 8 Department of Physics, SSH, Nazarbayev University, Astana 010000, Kazakhstan 9 Department of Internal Medicine, Astana Medical University, Astana 010000, Kazakhstan 10 Department of Nephrology, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain 11 Nephrology and Kidney Transplant Research Group, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain Citation: Gaipov, A.; 12 Department of Medicine, Universidad Autonoma de Madrid and IIS-Fundacion Jimenez Diaz, Makhammajanov, Z.; Dauyey, Z.; 28040 Madrid, Spain 13 Division of Nephrology, Department of Medicine, Koc University, 34450 Istanbul, Turkey Markhametova, Z.; Mussina, K.; Nogaibayeva, A.; Kozina, L.; * Correspondence: abduzhappar.gaipov@nu.edu.kz Auganova, D.; Tarlykov, P.; Bukasov, † These authors contributed equally to this work. R.; et al. Urinary Protein Profiling for Potential Biomarkers of Chronic Abstract: Proteinuria is a risk factor for chronic kidney disease (CKD) progression and associated Kidney Disease: A Pilot Study. complications. However, there is insufficient information on individual protein components in Diagnostics 2022, 12, 2583. urine and the severity of CKD. We aimed to investigate urinary proteomics and its association https://doi.org/10.3390/ with proteinuria and kidney function in early-stage CKD and in healthy individuals. A 24 h urine diagnostics12112583 sample of 42 individuals (21-CKD and 21-healthy individuals) was used for mass spectrometry-based Academic Editor: Akito Maeshima proteomics analysis. An exponentially modified protein abundance index (emPAI) was calculated for each protein. Data were analyzed by Mascot software using the SwissProt database and bioinformatics Received: 19 September 2022 tools. Overall, 298 unique proteins were identified in the cohort; of them, 250 proteins belong to Accepted: 20 October 2022 the control group with median (IQR) emPAI 39.1 (19–53) and 142 proteins belong to the CKD group Published: 25 October 2022 with median (IQR) emPAI 67.8 (49–117). The level of 24 h proteinuria positively correlated with Publisher’s Note: MDPI stays neutral emPAI (r = 0.390, p = 0.011). The emPAI of some urinary proteomics had close positive (ALBU, ZA2G, with regard to jurisdictional claims in IGKC) and negative (OSTP, CD59, UROM, KNG1, RNAS1, CD44, AMBP) correlations (r < 0.419, published maps and institutional affil- p < 0.001) with 24 h proteinuria levels. Additionally, a few proteins (VTDB, AACT, A1AG2, VTNC, iations. and CD44) significantly correlated with kidney function. In this proteomics study, several urinary proteins correlated with proteinuria and kidney function. Pathway analysis identified subpathways potentially related to early proteinuric CKD, allowing the design of prospective studies that explore Copyright: © 2022 by the authors. their response to therapy and their relationship to long-term outcomes. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: urinary proteomics; proteinuria; chronic kidney disease; biomarkers distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Diagnostics 2022, 12, 2583. https://doi.org/10.3390/diagnostics12112583 https://www.mdpi.com/journal/diagnostics Diagnostics 2022, 12, 2583 2 of 16 1. Introduction Chronic kidney disease (CKD) is a global public health concern that affects approxi- mately 850 million people of the world population [1]. In 2017, CKD ranked 12th among the leading causes of death with more than 1.2 million deaths, and it is estimated that the all-cause mortality of CKD together with CKD-attributable cardiovascular diseases (CVD) reached over 2.6 million deaths in the same year [2]. These numbers are likely to progress, and by 2040, CKD may reach the 5th rank on the list of leading causes of global death [3]. Unfortunately, currently available treatments of CKD are not successful at im- proving disease outcomes due to the diagnosis of CKD mostly in the late stages. Therefore, early detection of CKD and understanding the mechanisms of its progression are probably the only effective strategy for preventing CKD-associated social economic burdens and mortalities. Proteinuria is recognized as the most important marker and is a risk factor for CKD progression and associated complications among both diabetic and non-diabetic patients [4]. The presence of excess proteins in urine as proteinuria may reflect at least one of these following mechanisms, but is not limited to: increased blood levels of low-molecular- mass (LMM) proteins, such as immunoglobulin light chains (pre-renal proteinuria); a loss of selective filtration of high-molecular-mass (HMM) proteins (glomerular proteinuria); inadequate reabsorption of filtered LMM proteins (tubular proteinuria); loss of tubular cell-derived proteins (tubular proteinuria); loss of lower urinary tract-derived proteins (post-renal proteinuria) [4,5]. Among these, glomerular proteinuria and each tubular proteinuria independently and/or together are pathognomonic of renal damage [6]. Reabsorption of excess amounts of filtered proteins by proximal tubular epithelial cells (PTECs) has been reported to render toxic effects on PTECs leading to tubulointerstitial inflammation, tubular atrophy, interstitial fibrosis, and eventually, end-stage renal disease (ESRD) [5,7,8]. Thus, acceleration of CKD progression is highly expected in patients with higher proteinuria levels due to the tubular toxicity of filtered proteins. However, despite high levels of proteinuria, paradoxically, some patients may have a slow decline of renal function compared to those patients with lower levels of urinary protein excretion. Thus, there is a knowledge gap; why the severity of CKD progression could differ between patients with similar levels of proteinuria or some patients with higher proteinuria do not progress as rapid as expected? Differences in the physical-chemical properties of filtered proteins, the molecular weight (low, middle, and high molecular weight proteins), the biological activity or cell origin, or the stimuli for protein secretion of urinary proteins may underlie these differences [7,9]. Therefore, early detection of “high-risk candidate” proteins, which are associated with proteinuria and renal function, may serve as reliable diagnostic markers of CKD progression. Considering this knowledge gap, in this preliminary study, we aimed to compare urinary protein profiles and their relationship with levels of proteinuria and kidney function in CKD patients and healthy individuals. We also aimed to compare the biologic and pathophysiologic processes based on protein profiles of CKD patients and healthy individuals. 2. Materials and Methods 2.1. Patients and Study Settings This cross-sectional pilot study included early-stage CKD patients with different levels of proteinuria, and 21 age and gender matched healthy subjects. The study was approved by Nazarbayev University Institutional Review Ethics Committee on 25 February 2020 (NU-IREC 208/06122019), and all participants were subject to sign an informed consent. This study is part of the trial that has been registered in ClinicalTrials.gov (Trial registration ID NCT04311684, the date of registration was 17 March 2020) [10]. Patient examinations were conducted according to good medical and laboratory practice and in keeping with the recommendations set forth by the Declaration of Helsinki Guidelines for Biomedical Research Involving Human Subjects. Diagnostics 2022, 12, 2583 3 of 16 Participants were recruited in this study at “National Scientific Medical Center” (NSMC, Astana, Kazakhstan) between March 2020 and December 2021 and were COVID-19-free at the time of enrollment. All participants were informed about the study protocol and gave informed consent prior to enrolling in the study. Inclusion criteria for the patient group were as follows: CKD stage 1–3 by KDIGO classification [11] caused by glomerular diseases, with proteinuria > 300 mg/24 h. Exclusion criteria were: age < 18 and >65 years, pregnant females, patients with diabetes mellitus, cancer, infectious diseases, and other life-treating comorbidities or conditions. The diagnosis of suspected glomerular disease was based on the presence of proteinuria in the absence of systemic diseases but was not confirmed by kidney biopsy. 2.2. Laboratory Tests and Data Collection The laboratory investigators were blinded to data showing patients’ clinical outcomes until the end of the study. Blood and urine samples were collected independently, without considering the outcome of the patients. Blood metabolic profiles including serum crea- tinine, serum urea, glucose, uric acid, total protein, AST, ALT, lipid profiles, and protein fractions were obtained for all participants. A 24 h urinalysis including urine protein, sodium, potassium, uric acid, creatinine as well as complete blood count parameters was also defined during the enrollment. All routine laboratory tests were performed by colori- metric method on a Cobas Integra 400 plus analyzer (Oststeinbek, Germany). An estimated glomerular filtration rate (eGFR) was calculated via the CKD-EPI equation. Demographic data, medical history, and comorbid diseases were recorded after interviews with the participants and collected from medical carts. Additional 24 h urine samples were immediately stored at −80 ◦ C for further pro- teomic evaluation [12]. Acetone precipitation was used as a method for protein isolation as described by Sun et al. [13]. Final protein samples were resuspended in 25 mM NH4HCO3 and stored at −80 ◦ C until use. Protein concentrations were measured with a NanoDrop 1000 (Thermo, Waltham, MA, USA) following the manufacturer’s instructions. A concen- tration of fifty µg of proteins was transferred to an Eppendorf tube for in-solution protein digestion. 2.3. Mass Spectrometry Analysis The proteins were reduced, alkylated, and digested by trypsin (20 ng/uL) at 37 ◦ C overnight, and the obtained peptide mixtures were desalted, and concentrated using ZipTip with C18 resin (Millipore, Cork, Ireland). The eluted peptides were then processed with a centrifugal evaporator (Eppendorf, Hamburg, Germany) and re-suspended in 10 µL of 0.1% trifluoroacetic acid. Samples were analyzed using nanoflow reversed-phase C18 liquid chromatography-tandem mass spectrometry (LC-MS/MS). For chromatography, a Dionex HPLC pump with a trapping column (Acclaim PepMap100 C18 pre-column) was used. An Acclaim PepMap100 C18 RSLC column (Thermo, MA, USA) was used to separate the peptides in a 75 min multistep acetonitrile gradient at a flow rate of 0.3 mL/min. An unmodified captive spray ion source (capillary 1500 V, dry gas 3.0 L/min, dry temperature 150 ◦ C) was used with the Impact II ESI-QUAD-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany). Full-scan MS spectra were acquired at a spectral rate of 2.0 Hz, followed by the acquisition of one MS/MS spectrum. The MS/MS data were analyzed in Data Analysis 3.4 software (Bruker Daltonics, Bremen, Germany) and saved in Mascot generic format (*.mgf) then searched with the Mascot 2.6.1 software (Matrix Science, London, UK) against the Swiss-Prot protein database (release February 2021) taxonomically restricted to Homo sapiens (human) containing 20,396 sequences. The search parameters used were oxidation of methionine as variable modification and carbamidomethylation of cysteine residues as fixed modification. A maximum of two missed cleavages of tryptic peptides was allowed. The peptide and protein data were extracted using a significance level of p < 0.05. The false discovery rate (FDR) was set to 1% and the decoy database search was parameters used were oxidation of methionine as variable modification and carbami- domethylation of cysteine residues as fixed modification. A maximum of two missed Diagnostics 2022, 12, 2583 cleavages of tryptic peptides was allowed. The peptide and protein data were extracted 4 of 16 using a significance level of p < 0.05. The false discovery rate (FDR) was set to 1% and the decoy database search was used for estimating the FDR. Mass error windows of 100 ppm and used0.05 Da were allowed for estimating for Mass the FDR. MS and MS/MS, error windowsrespectively. of 100 ppm and 0.05 Da were allowed Exponentially modified for MS and MS/MS, respectively. protein abundance index (emPAI) was selected as a label- free quantification Exponentiallytechnique modified based proteinonabundance spectral counting of tryptic index (emPAI) peptides was as as selected described a label- elsewhere [14]. The emPAI values were calculated for the identified proteins by the Mas- free quantification technique based on spectral counting of tryptic peptides as described cot 2.6.1 software elsewhere (Matrix [14]. The emPAIScience, London, values were UK). for the identified proteins by the Mascot calculated 2.6.1 software (Matrix Science, London, UK). 2.4. Reactome and GO Analysis 2.4. Reactome and GO Analysis Highly abundant proteins were selected that were expressed in more than 50% of studyHighly participants abundantto further proteinsanalyze for functional were selected enrichment that were expressedandin pathway more than analyses, 50% of which resulted in 29toproteins study participants further in the CKD analyze forgroup and 37 functional proteins inand enrichment the control pathway group. The analyses, list andresulted which percentage in 29ofproteins the selected proteins in the CKD groupidentified in proteins and 37 more thanin 50% of participants the control group. Theare presented in Figureof1.the list and percentage Gene Ontology selected (GO) proteins enrichment identified analysis in more than was 50% conducted to gain of participants are presented in Figure 1. Gene Ontology (GO) enrichment analysis was conducted functional insights for selected proteins, including biological process, protein class, and to gain functional cellular insights forvia components selected Proteinproteins, Analysis including Throughbiological process, Evolutionary protein class, Relationships and (PAN- cellular http://pantherdb.org THER, components via Protein Analysis accessed onThrough 18 March Evolutionary Relationships 2022) [15,16]. Pathway (PANTHER, enrichment http://pantherdb.org analysis was conducted accessed on 18Reactome using The March 2022) [15,16]. Pathway Knowledgebase enrichment analysis (https://reactome.org ac- was cessedconducted on 18 Marchusing The that 2022) Reactome Knowledgebase produces a probability (https://reactome.org accessed value (p-value) by correcting on it for 18 March 2022) that produces a probability value (p-value) false discovery rates using the Benjamini–Hochberg method [17]. by correcting it for false discovery rates using the Benjamini–Hochberg method [17]. Figure 1. A comparison of protein distribution was identified in more than 50% of any group Figure 1. A comparison participants. of protein CKD, chronic distribution kidney disease; HC, was identified healthy in more than 50% of any group par- controls. ticipants. CKD, chronic kidney disease; HC, healthy controls. 2.5. Statistical Analyses 2.5. Statistical StatisticalAnalyses analyses were conducted using Stata MP2 16.1 version. Numerical variables were Statistical as mean ± expressed analyses standard were deviation conducted using(SD) for MP2 Stata normal distribution 16.1 and median version. Numerical and varia- interquartile bles range (IQR) were expressed for abnormal as mean ± standarddistribution. Categorical deviation (SD) variables for normal were presented distribution and me- as numbers dian (percentage), and interquartile and(IQR) range for categorical for abnormalvariables, a chi-square distribution. test was Categorical performed. variables were Two-sided t-tests for parametric and Wilcoxon rank-sum tests for nonparametric data were performed. Spearman’s correlation test was used for identifying correlations between the protein profiles and eGFR, a correlation between serum creatinine level with protein profiles, and correlations between the level of 24 h protein excretion and the emPAI of urinary proteins. All data were compared between healthy controls (HC) and CKD patients. A Diagnostics 2022, 12, 2583 5 of 16 p-value less than 0.05 (typically ≤ 0.05) is considered statistically significant. The estimated power for the sample size of 42 participants was 0.9935. 3. Results 3.1. General Characteristics of the Study Population The baseline characteristics of the study population are presented in Table 1. There are 21 healthy controls and 21 early-stage CKD patients comparable in terms of age, gender, and eGFR. Median eGFR was 109.1 (IQR 103.3–120.1) and 111 (72.6–138.1) mL/min in healthy control and CKD groups, respectively. In 90% of cases, the primary cause of CKD was glomerular diseases. The laboratory findings in the CKD group present the abnormal ranges related to glomerular disease (hypoproteinemia, hyperlipidemia, proteinuria), compared to healthy controls. Table 1. General and clinical biochemical characteristics of patients. Parameters Healthy Controls, n = 21 CKD Group, n = 21 p-Value Demographics and clinical data Gender, female, % 15 (71%) 12 (57%) 0.334 Age, year 35.6 ± 8.3 38.5 ± 13.7 0.419 eGFR, mL/min/1.73 m2 109.1 (103.3–120.1) 111 (72.6–138.1) 0.835 Primary diagnosis, n (%) Glomerular disease N/A 19 (90%) Post-transplant CKD N/A 1 (4,76%) CKD of unknown etiology N/A 1 (4.76%) Biochemistry Serum creatinine, µmol/L 65.1 (59.6–71.4) 69.6 (57.4–127.9) 0.285 Serum urea, mmol/L 4.0 (3.5–4.6) 5.2 (4.0–8.6) 0.014 Serum glucose, mmol/L 5.1 (4.9–5.5) 5.0 (4.4–5.3) 0.973 Serum uric acid, mcmol/L 277.5 (234.7–321.0) 359.6 (283.0–436.4) 0.018 Serum sodium, mmol/L 139.5 (138–141) 140 (139–141) 0.863 Serum potassium, mmol/L 4.4 (4.1–4.8) 4.2 (4.0–4.6) 0.232 Ca ionized, mmol/L 1.3 (1.3–1.3) 1.3 (1.3–1.3) 0.071 Total bilirubin, mcmol/L 7.1 (5.0–11.5) 5.0 (3.1–7.7) 0.042 ALT, mckat/L 0.2 (0.1–0.3) 0.3 (0.2–0.5) 0.111 ACT, mckat/L 0.3 (0.2–0.3) 0.3 (0.2–0.4) 0.396 Total cholesterol, mmol/L 4.4 (4.0–4.7) 6.5 (5.5–7.9) <0.0001 HDL, mmol/L 1.5 (1.2–1.9) 1.4 (1.1–1.7) 0.514 LDL, mmol/L 2.7 (2.2–3.2) 4.1 (3.3–5.6) 0.0001 Triglycerides, mmol/L 0.9 (0.6–1.3) 2.3 (1.4–3.0) <0.0001 Serum total protein, g/L 68.9 (66.5–72.0) 50.8 (44.8–64.6) <0.0001 Protein fractions, % Albumin 68.4 (66.1–69.7) 58.0 (43.9–63.1) <0.0001 Globulin alpa-1 1.9 (1.8–2.1) 2.9 (2.3–3.4) <0.0001 Globulin alpa-2 8.5 (8.0–8.9) 14.3 (10.9–19.9) <0.0001 Globulin beta-1 6.0 (5.6–6.4) 8.5 (7.1–9.7) <0.0001 Globulin beta-2 3.2 (3.1–3.7) 5.4 (3.9–7.3) 0.0001 Gamma globulin 12.3 (10.9–14.4) 10.3 (8.5–13.8) 0.039 CBC WBC 10 × 109 /L 5.4 (5.0–6.2) 7.3 (6.8–8.9) 0.0003 RBC 10 × 1012 /L 4.7 (4.3–5.2) 4.4 (4.0–4.7) 0.043 HGB g/L 129 (121–155) 125 (115–138) 0.220 PLT 10 × 109 /L 252 (228–298) 284 (228–329) 0.410 ESR, mm/h 9 (5–15) 27 (12–37) 0.002 Diagnostics 2022, 12, 2583 6 of 16 Table 1. Cont. Parameters Healthy Controls, n = 21 CKD Group, n = 21 p-Value Urinalysis, 24 h Proteinuria, g/24 h 0.1 (0.1–0.2) 2.0 (1.4–6.9) <0.0001 Sodium, mmol/24 h 119 (71.0–164.9) 206.4 (145.0–317.1) 0.009 Potassium, mmol/24 h 40.8 (33.8–49.5) 42.2 (33.5–51.1) 0.697 Uric acid, mmol/24 h 2.4 (2.0–3.3) 2.6 (1.6–3.4) 0.990 Creatinine, mmol/24 h 9.6 (8.5–10.5) 9.7 (8.0–13.5) 0.464 Proteomic data Total # of defined proteins, n 250 142 emPAI for total proteins 39.1 (19–53) 67.8 (49–117) 0.003 # of selected proteins, n 45 32 emPAI for selected proteins 43.4 (24–71) 45.1 (18–80) 0.0001 Abbreviations: CKD: Chronic kidney disease; eGFR: estimated glomerular filtration rate; emPAI: exponentially modified protein abundance index; ALT: Alanine Aminotransferase; ACT: Activated Clotting Time; HDL: high- density lipoprotein; LDL: low-density lipoprotein; CBC: complete blood count; WBC: white blood cells; RBC: red blood cells; HGB: hemoglobin; PLT: platelet; ESR: Erythrocyte Sedimentation Rate. 3.2. Urinary Protein Profiles of Study Population Initially, 298 individual proteins were identified in the overall cohort (Table 1); of them, 250 belong to the control group with median (IQR) emPAI 39.1 (19–53) and 142 proteins belong to the CKD group with median (IQR) emPAI 67.8 (49–117). Figure 1 depicts that the observed selected proteome composition of urine samples markedly differs between groups. Most proteins with higher emPAI that were observed in urine samples of CKD patients were mostly found in a small number of urine samples of healthy individuals. Contrarily, most proteins that were observed in urine samples of healthy individuals were either not found or found in a small number of urine samples of patients. However, the amount of emPAI was significantly higher in the CKD group, corresponding to the amount of 24 h proteinuria. 3.3. Associations between Renal Function and Protein Composition The level of 24 h proteinuria positively correlated with both the emPAI of total and selected proteins (r = 0.390, p = 0.011 and r = 0.532, p = 0.003, respectively). Table 2 shows the correlations between the level of 24 h protein excretion and the emPAI of the urinary proteome. The level of 24 h protein excretion positively correlated with ALBU, ZA2G, IGKC, FETUA, VTDB, A1BG, TRFE, and IGHA2, while the emPAI of OSTP, CD59, UROM, KNG1, RNAS1, CD44, AMBP, A2GL, and PTGDS proteins negatively correlated with the level of 24 h proteinuria. The correlations of serum creatinine levels and eGFR with emPAI of some proteins shown in Table 3. The serum creatinine level positively correlated with VTDB (r = 0.617, p = 0.0008) and negatively correlated with AACT (r = −0.627, p = 0.007), A1AG2 (r = −0.464, p = 0.03), VTNC (r = −0.60, p = 0.04), whereas eGFR is positively correlated with CD44 (r = 0.470, p = 0.02) and negatively correlated with VTDB (r = −0394, p = 0.05). Overall, the most consistent correlation with markers of kidney function was provided by VTDB, as it correlated with both serum creatinine (positively) and eGFR (negatively). Diagnostics 2022, 12, 2583 7 of 16 Table 2. Correlations between the level of 24 h Protein excretion and the emPAI of Urinary Proteome. Urinary Spearman’s emPAI, Molecular Protein Name p-Value N of Obs Sum of emPAI Proteome Rho Median (IQR) Weight (Da) ALBU Albumin 0.6287 <0.0001 42 1388.9 21.5 (5.0–44.1) 69,367 ZA2G Zinc-alpha-2-glycoprotein 0.6254 0.0001 35 37.32 1 (0.4–1.85) 34,259 IGKC Immunoglobulin kappa constant 0.4199 0.0087 38 245.0 3.3 (1.1–6.2) 11,765 FETUA Alpha-2-HS-glycoprotein 0.5347 0.0328 16 4.06 0.2 (0.1–0.4) 39,341 VTDB Vitamin D-binding protein 0.4742 0.0125 27 13.67 0.3 (0.2–0.8) 52,918 A1BG Alpha-1B-glycoprotein 0.4296 0.0321 25 25.85 1.1 (0.7–1.3) 54,254 TRFE Serotransferrin 0.3815 0.0342 31 83.71 2.5 (0.7–4.0) 77,064 Immunoglobulin heavy constant IGHA2 0.4833 0.0494 17 11.13 0.6 (0.5–0.8) 36,591 alpha 2 OSTP Osteopontin −0.8017 <0.0001 27 9.47 0.3 (0.2–0.4) 35,423 CD59 CD59 glycoprotein −0.7633 <0.0001 28 85.42 2.4 (1.3–4.1) 14,177 UROM Uromodulin −0.7589 <0.0001 36 56.16 1.1 (0.2–2.7) 69,761 KNG1 Kininogen-1 −0.7269 <0.0001 33 21.01 0.6 (0.1–1.0) 71,957 RNAS1 Ribonuclease pancreatic −0.6924 <0.0001 39 38.62 0.6 (0.4–1.7) 17,644 CD44 CD44 antigen −0.6803 0.0001 26 2.56 0.1 (0.04–0.1) 81,538 AMBP Protein AMBP −0.5899 <0.0001 41 160.57 1.9 (0.8–4.4) 38,999 A2GL Leucine-rich alpha-2-glycoprotein −0.5478 0.0186 18 4.60 0.2 (0.2–0.3) 38,178 PTGDS Prostaglandin-H2 D-isomerase −0.3365 0.0389 38 44.05 1.2 (0.5–1.3) 21,029 Table 3. Correlations of Serum Creatinine level and eGFR with emPAI of some proteins. Information about Proteins Serum Creatinine eGFR Urinary Molecular Spearman’s Spearman’s Protein Name N of Obs p-Value p-Value Proteome Weight (Da) Rho Rho VTDB Vitamin D-binding protein 52,918 26 0.6172 0.0008 −0.3944 0.0461 AACT Alpha-1-antichymotrypsin 47,651 17 −0.6271 0.0071 0.3611 0.1544 A1AG2 Alpha-1-acid glycoprotein 2 23,603 21 −0.4637 0.0342 0.2023 0.3791 VTNC Vitronectin 54,306 12 −0.5999 0.0392 0.4888 0.1068 CD44 CD44 antigen 81,538 25 −0.1365 0.5154 0.4696 0.0179 3.4. GO Enrichment Analysis of Urinary Proteome Figure 2 illustrates the GO enrichment analysis of selected proteins in the CKD and control groups. The protein class of GO analysis shows significantly higher numbers of proteins in the defense protein, protein-binding activity modulator, transfer protein, protein modifying enzyme, and metabolite interconversion enzyme classes in urine samples of the CKD group compared to urine samples of the healthy group. However, protein numbers in the transmembrane signal receptor, RNA metabolism protein, intercellular signal molecule, membrane traffic, cell adhesion, and extracellular matrix protein classes are higher in the urine samples of the controls compared to the urine samples of the CKD group (Figure 2A). gnostics 2022, 12, 2583 9 of 16 Diagnostics 2022, 12, 2583 8 of 16 A B Figure 2. Cont. Diagnostics 2022, 12, 2583 10 of 16 Diagnostics 2022, 12, 2583 9 of 16 C Figure 2.Figure 2. GO enrichment GO enrichment analysisanalysis ofselected of the 29 the 29 selected proteinsproteins in the in the group CKD CKD group (in and (in red) red) 37 andpro- 37 proteins teins in the control in the group control (in (in group blue). (A)(A) blue). protein class. protein (B) (B) class. cellular component. cellular component.(C)(C) biological process. biological process. Further,Further, the cellular the cellular component component displaysdisplays how most how mostproteins urine urine proteins are excreted are excreted from from the extracellular the extracellular region, region, cell periphery, cell periphery, membrane, membrane, and celland cell surface surface components components of both of both groups.groups. Notably,Notably, proteinsproteins that arethat are excreted excreted from the from the extracellular extracellular region region are are markedly markedly higher in the urine samples of the CKD group compared to higher in the urine samples of the CKD group compared to the urine samples of the urine samples of the the healthy group (Figure healthy group (Figure 2B).2B). Enrichment Enrichment of proteins of proteins that arethat are involved involved in cellular in cellular processes, processes, metabolic metabolic processes, bi- processes, ological regulation, the response to stimulus, and immune system processes biological regulation, the response to stimulus, and immune system processes is signifi- is significantly higher in cantly higher in the the urine urine samples samples of ofthe theCKD CKDgroup, group,while whileenrichment enrichment ofof proteins proteins in in signaling signaling and cell adhesion processes are slightly higher in the urine samples of the group and cell adhesion processes are slightly higher in the urine samples of the healthy healthy(Figure 2C). group (Figure 2C). 3.5. Pathway Enrichment Analysis of Proteins 3.5. Pathway Enrichment Analysis of Proteins The Reactome Knowledgebase generated signal cascades for 28 out of 29 submitted The Reactome Knowledgebase generated signal cascades for 28 out of 29 submitted proteins of the patient group, where 123 different pathways were hit by at least one of the proteins of the patient group, where 123 different pathways were hit by at least one of the relevant proteins (Supplement Figure S1). For the control group, The Reactome Knowledge- relevant proteins (Supplement Figure S1). For the control group, The Reactome base generated signal cascades for 30 out of 37 submitted proteins, where 163 different path- Knowledgebase generated signal cascades for 30 out of 37 submitted proteins, where 163 ways were hit by at least one of the relevant proteins (Supplement Figure S2). different pathways were hit by at least one of the relevant proteins (Supplement Figure Supplement Tables S1 and S2 provide results for pathway analysis of the 28 most rel- S2). Supplement Tables evant proteins of S1 theand CKD S2group provideandresults for pathway 31 relevant proteinsanalysis of the groups of the control 28 mostwith an relevantindication proteins ofof significance level (p < 0.05) and false discovery rate (FDR < 5%). with the CKD group and 31 relevant proteins of the control groups an indicationFurther, of significance levelthe to identify (p 69 kDA), such as uromodulin, CD44 antigen, and kininogen-1 as well as with relatively lower molecular weight (<39 kDA), such as AMBP, osteopontin, CD55 glycoprotein and ribonuclease pancreatin were negatively correlated with the level of 24 h proteinuria. Uromodulin, which is exclusively produced by tubular cells [25], in contrast to albumin, may be down released from injured tubules in patients with higher proteinuria [26]. At this stage, it is difficult to explain the correlations between 24 h proteinuria and other proteins due to some limitations of this study, which needs to evaluate these relationships in urine samples, exclusively in a large cohort of CKD patients with proteinuria. Our data showed a close relation between a few urine proteins and kidney func- tion. Urinary Vitamin D-binding protein (VTDB) was consistently increased when kidney function declined. VTDB is the primary carrier in the circulation of 25-hydroxyvitamin D (25(OH) D), the precursor of the active form of vitamin D [27]. Urinary VTDB had previously been reported to predict kidney injury in patients with acute decompensated heart failure [28]. Other proteins were associated with at least some measures of kidney function. A1AG2 (orosomucoid) was upregulated in idiopathic nephrotic syndrome and diabetic nephropathy and associated with disease progression [29–31]. AACT, a serine protease inhibitor, also termed SerpinA3, is mainly synthesized in the liver and protects against different inflammatory diseases [32]. Urinary AACT levels were positively corre- lated with preclinical renal fibrosis following AKI [33]. Similarly, VTNC, an extracellular matrix protein secreted in response to tissue injury, was also expressed in preclinical renal fibrosis [34–36]. CD44 protein is a transmembrane protein, expressed by injured proximal tubular and glomerular cells [37–39]. The correlation between the abundance of these proteins with kidney function argues for their role in kidney disease. We found an interesting difference in the biological process between CKD patients and healthy controls in gene ontology and Reactome enrichment analysis. Urinary proteins Diagnostics 2022, 12, 2583 12 of 16 that function in the extracellular region were significantly higher in the urine samples of patients compared to controls, indicating increased permeability of the glomerular barrier, which allows the leakage of excessive serum proteins into the urine [5]. This is a hallmark of glomerular proteinuria in CKD, particularly in glomerulonephritis [40]. Importantly, the urinary composition of urine samples of the two groups significantly differs. Thus, GO and Reactome analysis show different biological process and pathway enrichment levels. In pathway enrichment analysis, subpathways of hemostasis, such as platelet activation, signaling and aggregation, cell surface interactions at the vascular level, and formation of a fibrin clot may represent the activation of platelets at the site of glomerular damage and their contribution to renal inflammation via interaction with leukocytes [41,42]. Previously, platelet–leukocyte aggregates were reported as a potential biomarker for kidney diseases and outcomes [42]. Upregulation of an immune system related subpathway, specifically neutrophil degradation, is likely due to acute inflammation during early stages of glomerulonephritis [43]. Activation of complement cascades was also overrepresented. Complement activation is an important mediator of pathogenesis and the progression to ESRD of multiple kidney diseases, including many types of glomerulonephritis [44]. Heme scavenging is also upregulated in CKD patients. In the context of glomerular disease, heme derived from the presence of red blood cells in the tubular lumen is a well- known tubular cell stressor that may cause tubular cell injury and death [45–48]. One more important point to note is the absence of extracellular matrix organization-related pathways in CKD patients. In this regard, proteinuria promotes tubular cell injury and interstitial inflammation and fibrosis [49]. A similar phenomenon had been previously observed using a related technique, urine capillary electrophoresis–mass spectrometry (CE-MS) peptidomics [19,20]. CKD273 is a urinary peptidomics biomarker composed of 273 peptides that differentiates CKD from healthy subjects and predicts CKD progression. Interestingly, many of the urinary peptides in CKD273 are type 1 collagen peptides and they are decreased in the urine of CKD subjects, suggesting that decreased ECM degradation could contribute to the accumulation of ECM during kidney fibrosis [19,20]. There are several limitations in our study. First, only patients with early-stage CKD (median eGFR 111 mL/min/1.73 m2 ) were enrolled and the study was cross-sectional. An ongoing follow-up study will address whether the current findings are associated with long-term kidney outcomes. Second, the sample size is limited. A multicenter study with a larger sample size is needed for validation of the current findings. However, the design of such a large multicenter study should be necessarily based on a pilot study, such as the present one. Third, the absence of kidney biopsy precludes the study of urine-tissue correlations. 5. Conclusions This urinary proteomics study identified clear differences in the number and type of urinary proteins between proteinuric CKD patients with preserved kidney function and healthy individuals. Several urinary proteins were strongly correlated with 24 h proteinuria and/or kidney function. Interestingly, pathway analysis suggested significant upregulation of hemostasis, immune system, and vesicle mediated transport-related subpathways and decreased ECM subpathways in early stages of CKD. This information may be used to identify potential molecular mechanisms of kidney injury and to assess the impact of therapy on these mechanisms. Further prospective research is recommended with a larger study population to address both the impact of therapy on the urinary proteome and whether this proteomic pattern is an early biomarker of the response to therapy or of CKD progression. 6. Trial Registration The trial was approved by ClinicalTrials.gov (Trial registration ID NCT04311684). The date of registration was 17 March 2020. Diagnostics 2022, 12, 2583 13 of 16 Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/diagnostics12112583/s1, Figure S1: Pathway overview of the 29 most relevant proteins of the CKD group; Figure S2: Pathway overview of the 30 most relevant proteins of the healthy group; Table S1: Pathway analysis of the 28 most relevant proteins of the CKD group with the indication of significance level and false discovery rate; Table S2: Pathway analysis of the 30 most relevant proteins of the healthy group with the indication of significance level and false discovery rate. Author Contributions: Conceptualization, A.G. and Z.M. (Zhalaliddin Makhammajanov); method- ology, A.G., Z.M. (Zhalaliddin Makhammajanov) and P.T.; formal analysis, Z.M. (Zhalaliddin Makhammajanov), Z.D. and K.M.; investigation, A.G., Z.M. (Zhalaliddin Makhammajanov) and D.A.; resources, Z.M. (Zhannur Markhametova), A.N. and L.K.; data curation, Z.M. (Zhalaliddin Makhammajanov), Z.D. and K.M.; writing—original draft preparation, A.G. and Z.M. (Zhalaliddin Makhammajanov); writing—review and editing, Z.M. (Zhalaliddin Makhammajanov), Z.D., Z.M. (Zhannur Markhametova), M.K., M.J.S. and A.O.; visualization, Z.M. (Zhalaliddin Makhammajanov); supervision, A.G; project administration, A.G. and Z.M. (Zhannur Markhametova); funding acquisition, A.G., R.B., Z.U. and D.T. All authors have read and agreed to the published version of the manuscript. Funding: The authors acknowledge funding from the Nazarbayev University Collaborative Research Program (CRP) for 2020–2022 (Funder Project Reference: 091019CRP2105). Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Nazarbayev University Institutional Review Ethics Committee on 25 February 2020 (NU-IREC 208/06122019). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 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