Abstract

   Proteinuria is a risk factor for chronic kidney disease (CKD)
   progression and associated complications. However, there is
   insufficient information on individual protein components in urine and
   the severity of CKD. We aimed to investigate urinary proteomics and its
   association with proteinuria and kidney function in early-stage CKD and
   in healthy individuals. A 24 h urine sample of 42 individuals (21-CKD
   and 21-healthy individuals) was used for mass spectrometry-based
   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 tools.
   Overall, 298 unique proteins were identified in the cohort; of them,
   250 proteins 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). The level of 24 h proteinuria positively
   correlated with emPAI (r = 0.390, p = 0.011). The emPAI of some urinary
   proteomics had close positive (ALBU, ZA2G, IGKC) and negative (OSTP,
   CD59, UROM, KNG1, RNAS1, CD44, AMBP) correlations (r < 0.419, p <
   0.001) with 24 h proteinuria levels. Additionally, a few proteins
   (VTDB, AACT, A1AG2, VTNC, 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 their response
   to therapy and their relationship to long-term outcomes.

   Keywords: urinary proteomics, proteinuria, chronic kidney disease,
   biomarkers

1. Introduction

   Chronic kidney disease (CKD) is a global public health concern that
   affects approximately 850 million people of the world population
   [[52]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 [[53]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 [[54]3]. Unfortunately, currently available treatments of CKD are
   not successful at improving 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 [[55]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) [[56]4,[57]5].
   Among these, glomerular proteinuria and each tubular proteinuria
   independently and/or together are pathognomonic of renal damage
   [[58]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)
   [[59]5,[60]7,[61]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 [[62]7,[63]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 [64]NCT04311684, the date of registration was 17
   March 2020) [[65]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.

   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
   [[66]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 creatinine, 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 colorimetric
   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 proteomic evaluation [[67]12]. Acetone precipitation was used
   as a method for protein isolation as described by Sun et al. [[68]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 concentration 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 used for estimating the FDR.
   Mass error windows of 100 ppm and 0.05 Da were allowed for MS and
   MS/MS, respectively.

   Exponentially modified protein abundance index (emPAI) was selected as
   a label-free quantification technique based on spectral counting of
   tryptic peptides as described elsewhere [[69]14]. The emPAI values were
   calculated for the identified proteins by the Mascot 2.6.1 software
   (Matrix Science, London, UK).

2.4. Reactome and GO Analysis

   Highly abundant proteins were selected that were expressed in more than
   50% of study participants to further analyze for functional enrichment
   and pathway analyses, which resulted in 29 proteins in the CKD group
   and 37 proteins in the control group. The list and percentage of the
   selected proteins identified in more than 50% of participants are
   presented in [70]Figure 1. Gene Ontology (GO) enrichment analysis was
   conducted to gain functional insights for selected proteins, including
   biological process, protein class, and cellular components via Protein
   Analysis Through Evolutionary Relationships (PANTHER,
   [71]http://pantherdb.org accessed on 18 March 2022) [[72]15,[73]16].
   Pathway enrichment analysis was conducted using The Reactome
   Knowledgebase ([74]https://reactome.org accessed on 18 March 2022) that
   produces a probability value (p-value) by correcting it for false
   discovery rates using the Benjamini–Hochberg method [[75]17].

Figure 1.

   [76]Figure 1
   [77]Open in a new tab

   A comparison of protein distribution was identified in more than 50% of
   any group participants. CKD, chronic kidney disease; HC, healthy
   controls.

2.5. Statistical Analyses

   Statistical analyses were conducted using Stata MP2 16.1 version.
   Numerical variables were expressed as mean ± standard deviation (SD)
   for normal distribution and median and interquartile range (IQR) for
   abnormal distribution. Categorical variables were presented as numbers
   (percentage), and for categorical variables, a chi-square test was
   performed. 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 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
   [78]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 m^2 109.1 (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 × 10^9/L 5.4 (5.0–6.2) 7.3 (6.8–8.9) 0.0003
   RBC 10 × 10^12/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 × 10^9/L 252 (228–298) 284 (228–329) 0.410
   ESR, mm/h 9 (5–15) 27 (12–37) 0.002
   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
   [79]Open in a new tab

   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 ([80]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). [81]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). [82]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.

Table 2.

   Correlations between the level of 24 h Protein excretion and the emPAI
   of Urinary Proteome.
   Urinary Proteome Protein Name Spearman’s Rho p-Value N of Obs Sum of
   emPAI emPAI, Median (IQR) Molecular 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
   IGHA2 Immunoglobulin heavy constant alpha 2 0.4833 0.0494 17 11.13 0.6
   (0.5–0.8) 36,591
   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
   [83]Open in a new tab

   The correlations of serum creatinine levels and eGFR with emPAI of some
   proteins shown in [84]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).

Table 3.

   Correlations of Serum Creatinine level and eGFR with emPAI of some
   proteins.
   Information about Proteins Serum Creatinine eGFR
   Urinary Proteome Protein Name Molecular Weight (Da) N of Obs Spearman’s
   Rho p-Value Spearman’s Rho p-Value
   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
   [85]Open in a new tab

3.4. GO Enrichment Analysis of Urinary Proteome

   [86]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 ([87]Figure 2A).

Figure 2.

   [88]Figure 2

   [89]Figure 2

   [90]Figure 2
   [91]Open in a new tab

   GO enrichment analysis of the 29 selected proteins in the CKD group (in
   red) and 37 proteins in the control group (in blue). (A) protein class.
   (B) cellular component. (C) biological process.

   Further, the cellular component displays how most urine proteins are
   excreted from the extracellular region, cell periphery, membrane, and
   cell surface components of both groups. Notably, proteins that are
   excreted from the extracellular region are markedly higher in the urine
   samples of the CKD group compared to the urine samples of the healthy
   group ([92]Figure 2B).

   Enrichment of proteins that are involved in cellular processes,
   metabolic processes, biological regulation, the response to stimulus,
   and immune system processes is significantly higher in the urine
   samples of the CKD group, while enrichment of proteins in signaling and
   cell adhesion processes are slightly higher in the urine samples of the
   healthy group ([93]Figure 2C).

3.5. Pathway Enrichment Analysis of Proteins

   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 relevant proteins ([94]Supplement
   Figure S1). For the control group, The Reactome Knowledgebase generated
   signal cascades for 30 out of 37 submitted proteins, where 163
   different pathways were hit by at least one of the relevant proteins
   ([95]Supplement Figure S2). Supplement Tables S1 and S2 provide results
   for pathway analysis of the 28 most relevant proteins of the CKD group
   and 31 relevant proteins of the control groups with an indication of
   significance level (p < 0.05) and false discovery rate (FDR < 5%).

   Further, to identify the most significant pathways in both groups, a
   filter was implicated using the significance level of p < 0.003.
   [96]Figure 3 displays the comparison of the most significant pathways
   of 28 relevant proteins of the CKD group and 31 relevant proteins of
   the control groups. Pathway analysis shows that hemostasis, and the
   immune system and vesicle-mediated transport-related subpathways are
   significantly upregulated in participants with CKD compared to the
   healthy group. However, extracellular matrix organization-related
   pathways are downregulated in participants with CKD compared to the
   healthy group.

Figure 3.

   [97]Figure 3
   [98]Open in a new tab

   A comparison of highly significant pathways of the most relevant
   proteins in the CKD and healthy control groups (p < 0.003). The color
   codes denote the different pathway groups. CKD, chronic kidney disease;
   HC, healthy controls; IGF, insulin-like growth factor; IGFBPs, uptake
   by insulin-like growth factor binding proteins.

4. Discussion

   This pilot study, assessing the urinary proteome in proteinuric
   patients with early GFR category CKD and healthy controls, found
   positive and negative relationships between emPAI levels of some
   urinary proteins with the amount of excreted 24 h proteinuria as well
   as with kidney function. Moreover, the differential presence of urinary
   proteins in CKD patients and healthy participants identified
   overrepresented biological processes in CKD patients, such as
   hemostasis, and immune system and vesicle mediated transport-related
   subpathways as well as, equally relevant, downregulated extracellular
   matrix organization-related pathways in participants with CKD compared
   to the healthy group. This information may be used to identify
   activated molecular pathways in CKD and assess the response to therapy.

   Proteinuria usually results from a disrupted glomerular filter barrier,
   although it may also result from failure of the proximal tubular cells
   to reabsorb proteins filtered by healthy glomerular filter barriers or
   by excessive amounts of plasma proteins that are usually filtered by
   glomeruli (e.g., monoclonal light chains) [[99]18]. Excess glomerular
   filtration of proteins causes tubular cell stress as tubular cells
   increase protein reabsorption, resulting in an inflammatory and
   profibrotic response and the loss of tubular cells. In routine clinical
   practice, only a few specific urinary proteins are measured for
   diagnostic purposes, including urinary albumin and kappa/lambda light
   chains. However, this approach potentially misses important information
   that may be provided by assessing a more ample protein panel. In this
   regard, the urinary peptidome may predict CKD progression and the
   plasma proteome at the time of active COVID-19 was recently found to
   associate with persistent symptoms out to 12 months
   [[100]19,[101]20,[102]21].

   In our study, we found statistically significant linear correlation
   between 24 h urinary protein level and the emPAI of albumin,
   zinc-alpha-2-glycoprotein, and immunoglobulin kappa constant. As
   expected, albumin (molecular weight 69 kDA) had the highest emPAI and
   highest relation to proteinuria. This finding is in correspondence with
   other literature data [[103]22,[104]23,[105]24]. On the other hand, the
   emPAI of a few proteins with relatively higher molecular weight (>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 [[106]25], in contrast to
   albumin, may be down released from injured tubules in patients with
   higher proteinuria [[107]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 function. 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 [[108]27]. Urinary VTDB
   had previously been reported to predict kidney injury in patients with
   acute decompensated heart failure [[109]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
   [[110]29,[111]30,[112]31]. AACT, a serine protease inhibitor, also
   termed SerpinA3, is mainly synthesized in the liver and protects
   against different inflammatory diseases [[113]32]. Urinary AACT levels
   were positively correlated with preclinical renal fibrosis following
   AKI [[114]33]. Similarly, VTNC, an extracellular matrix protein
   secreted in response to tissue injury, was also expressed in
   preclinical renal fibrosis [[115]34,[116]35,[117]36]. CD44 protein is a
   transmembrane protein, expressed by injured proximal tubular and
   glomerular cells [[118]37,[119]38,[120]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 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 [[121]5]. This is a hallmark of glomerular
   proteinuria in CKD, particularly in glomerulonephritis [[122]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 [[123]41,[124]42].
   Previously, platelet–leukocyte aggregates were reported as a potential
   biomarker for kidney diseases and outcomes [[125]42]. Upregulation of
   an immune system related subpathway, specifically neutrophil
   degradation, is likely due to acute inflammation during early stages of
   glomerulonephritis [[126]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 [[127]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 [[128]45,[129]46,[130]47,[131]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 [[132]49]. A similar phenomenon had been previously
   observed using a related technique, urine capillary
   electrophoresis–mass spectrometry (CE-MS) peptidomics
   [[133]19,[134]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
   [[135]19,[136]20].

   There are several limitations in our study. First, only patients with
   early-stage CKD (median eGFR 111 mL/min/1.73 m^2) 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
   [137]NCT04311684). The date of registration was 17 March 2020.

Acknowledgments