Urinary Proteome Research Articles

Characterizing the urinary proteome of prematurity-associated lung disease in school-aged children

IntroductionAlthough different phenotypes of lung disease after preterm birth have recently been described, the underlying mechanisms associated with each phenotype are poorly understood. We, therefore, compared the urinary proteome for different spirometry phenotypes in preterm-born children with preterm- and term-born controls.MethodsPreterm and term-born children aged 7–12 years, from the Respiratory Health Outcomes in Neonates (RHiNO) cohort, underwent spirometry and urine collection. Urine was analysed by Nano-LC Mass-Spectrometry with Tandem-Mass Tag labelling. The preterm-born children were classified into phenotypes of prematurity-associated preserved ratio impaired spirometry (pPRISm, FEV1 < lower limit of normal (LLN), FEV1/FVC ≥ LLN), prematurity-associated obstructive lung disease (POLD, FEV1 < LLN, FEV1/FVC < LLN) and preterm controls (FEV1 ≥ LLN,). Biological relationships between significantly altered protein abundances were analysed using Ingenuity Pathways Analysis software, and receiver operator characteristic curves were calculated.ResultsUrine was analysed from 160 preterm-born children and 44 term controls. 27 and 21 were classified into the pPRISm and POLD groups, respectively. A total of 785 proteins were detected. Compared to preterm-born controls, sixteen significantly altered proteins in the pPRISm group were linked to six biological processes related to upregulation of inflammation and T-cell biology. In contrast, four significantly altered proteins in the POLD group were linked with neutrophil accumulation. Four proteins (DNASE1, PGLYRP1, B2M, SERPINA3) in combination had an area under the curve of 0.73 for pPRISm and three combined proteins (S100A8, MMP9 and CTSC) had AUC of 0.76 for POLD.ConclusionsIn this exploratory study, we demonstrate differential associations of the urinary proteome with pPRISm and POLD.Trial registrationEudraCT: 2015-003712-20

A Pilot Urinary Proteome Study Reveals Widespread Influences of Circadian Rhythm Disruption by Sleep Deprivation.

It is widely accepted that circadian rhythm disruption caused short- or long-term adverse effects on health. Although many previous studies have focused on exploration of the molecular mechanisms, there is no rapid, convenient, and non-invasive method to reveal the influence on health after circadian rhythm disruption. Here, we performed a high-resolution mass spectrometry-based data-independent acquisition (DIA) quantitative urinary proteomic approach in order to explore whether urine could reveal stress changes to those brought about by circadian rhythm disruption after sleep deprivation. After sleep deprivation, the subjects showed a significant increase in both systolic and diastolic blood pressure compared with routine sleep. More than 2000 proteins were quantified and they contained specific proteins for various organs throughout the body. And a total of 177 significantly up-regulated proteins and 68 significantly down-regulated proteins were obtained after sleep deprivation. These differentially expressed proteins (DEPs) were associated with multiple organs and pathways, which reflected widespread influences of sleep deprivation. Besides, machine learning identified a panel of five DEPs (CD300A, SCAMP3, TXN2, EFEMP1, and MYH11) that can effectively discriminate circadian rhythm disruption. Taken together, our results validate the value of urinary proteome in predicting and diagnosing the changes by circadian rhythm disruption.

Identification of Potential Predictive and Diagnostic Urinary Biomarkers for Acute Rejection in Renal Transplant Recipients: A Proteomics Study

Acute rejection (AR) is one of the main predictors of long-term survival of allograft. The development of noninvasive diagnostic biomarkers of AR is an unmet need for the timely detection. This study aimed to identify novel detective biomarkers of AR by analyzing the urine proteome profile of transplant patients. Forty-two transplant patients including 30 biopsy-proven AR patients (including antibody and T-cell mediated rejection) and 12 transplant patients with stable renal function (control group) were enrolled. Label-free quantification (LFQ) proteomics technique was performed on urine samples. Multivariate statistical analysis was applied for biomarker identification. The ELISA method validated EGF (epidermal growth factor) from the top 10 candidate biomarkers in an independent cohort. Gene ontology and possible pathways were also analyzed. LFQ analysis revealed 453 identified proteins differentially expressed between groups that mainly participated in complement and coagulation pathways and proteolysis. Ten proteins with the highest AUCs (Area under the ROC Curve) were identified as candidate diagnostic biomarkers. Candidate biomarkers were mainly associated with extracellular matrix (ECM) degradation and epithelial-to-mesenchymal transition (EMT). Reduction of urinary EGF measured by ELISA in an independent group confirmed proteomics results. We introduced a unique set of diagnostic urinary biomarkers for AR. Interactions of biomarkers and validation of EGF among biomarker panels revealed that ECM remodeling and EMT might be the consequence of immunological processes in AR. If validated as a panel, the mentioned biomarkers might shed light on the pathogenesis of chronic injury after AR and point out the potential treatment strategies.

Biomarkers in acute kidney injury: On the cusp of a new era?

The field of nephrology has been slow in moving beyond the utilization of creatinine as an indicator for chronic kidney disease and acute kidney injury (AKI). Early diagnosis and establishment of etiology, in particular, are important for treatment of AKI. In the setting of hospital-acquired AKI, tubular injury is more common, but acute interstitial nephritis (AIN) has a more treatable etiology. However, it is likely that AIN is under- or misdiagnosed due to current strategies that largely rely on clinical gestalt. In this issue of the JCI, Moledina and colleagues made an elegant case for the chemokine called C-X-C motif ligand 9 (CXCL9) as a biomarker of AIN. The authors used urine proteomics and tissue transcriptomics in patients with and without AIN to identify CXCL9 as a promising, noninvasive, diagnostic biomarker of AIN. These results have clinical implications that should catalyze future research and clinical trials in this space.

Identification and validation of urinary CXCL9 as a biomarker for diagnosis of acute interstitial nephritis.

BackgroundAcute tubulointerstitial nephritis (AIN) is one of the few causes of acute kidney injury with diagnosis-specific treatment options. However, due to the need to obtain a kidney biopsy for histological confirmation, AIN diagnosis can be delayed, missed, or incorrectly assumed. Here, we identify and validate urinary CXCL9, an IFN-γ-induced chemokine involved in lymphocyte chemotaxis, as a diagnostic biomarker for AIN.MethodsIn a prospectively enrolled cohort with pathologist-adjudicated histological diagnoses, termed the discovery cohort, we tested the association of 180 immune proteins measured by an aptamer-based assay with AIN and validated the top protein, CXCL9, using sandwich immunoassay. We externally validated these findings in 2 cohorts with biopsy-confirmed diagnoses, termed the validation cohorts, and examined mRNA expression differences in kidney tissue from patients with AIN and individuals in the control group.ResultsIn aptamer-based assay, urinary CXCL9 was 7.6-fold higher in patients with AIN than in individuals in the control group (P = 1.23 × 10-5). Urinary CXCL9 measured by sandwich immunoassay was associated with AIN in the discovery cohort (n = 204; 15% AIN) independently of currently available clinical tests for AIN (adjusted odds ratio for highest versus lowest quartile: 6.0 [1.8-20]). Similar findings were noted in external validation cohorts, where CXCL9 had an AUC of 0.94 (0.86-1.00) for AIN diagnosis. CXCL9 mRNA expression was 3.9-fold higher in kidney tissue from patients with AIN (n = 19) compared with individuals in the control group (n = 52; P = 5.8 × 10-6).ConclusionWe identified CXCL9 as a diagnostic biomarker for AIN using aptamer-based urine proteomics, confirmed this association using sandwich immunoassays in discovery and external validation cohorts, and observed higher expression of this protein in kidney biopsies from patients with AIN.FundingThis study was supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) awards K23DK117065 (DGM), K08DK113281 (KM), R01DK128087 (DGM), R01DK126815 (DGM and LGC), R01DK126477 (KNC), UH3DK114866 (CRP, DGM, and FPW), R01DK130839 (MES), and P30DK079310 (the Yale O'Brien Center). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

In-depth urinary and exosome proteome profiling analysis identifies novel biomarkers for diabetic kidney disease.

Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM). Monitoring the early diagnostic period and disease progression plays a crucial role in treating DKD. In this study, to comprehensively elucidate the molecular characteristics of urinary proteins and urinary exosome proteins in type 2 DKD, we performed large-scale urinary proteomics (n=144) and urinary exosome proteomics (n=44) analyses on T2DM patients with albuminuria in varying degrees. The dynamics analysis of the urinary and exosome proteomes in our study provides a valuable resource for discovering potential urinary biomarkers in patients with DKD. A series of potential biomarkers, such as SERPINA1 and transferrin (TF), were detected and validated to be used for DKD diagnosis or disease monitoring. The results of our study comprehensively elucidated the changes in the urinary proteome and revealed several potential biomarkers reflecting the progression of DKD, which provide a reference for DKD biomarker screening.

AZGP1 as a potential biomarker of IgA vasculitis with nephritis in a children‑based urinary proteomics study by diaPASEF.

The present study aimed to screen for potential biomarkers in the urine of immunoglobulin A vasculitis with nephritis (IgAVN) using parallel accumulation‑serial fragmentation combined with data‑independent acquisition (diaPASEF) proteomic approach. Urine proteomes of eight children with IgAVN and eight healthy children were identified by diaPASEF and all differential proteins were analyzed by Gene Ontology and Kyoto Encyclopedia of Gene and Genome. Then, the specific biomarkers of urine samples from 10 children with IgAVN, 10 children with IgAV and 10 healthy children were verified by ELISA. The present study screened 254 differential proteins from the experiment, including 190 upregulated proteins and 64 downregulated proteins. The results of the ELISA showed that the concentration of urinary zinc‑alpha‑2‑glycoprotein (AZGP1) in children with IgAVN was significantly higher compared with that in children with IgAV and healthy children. The present study provided the potential clinical application of AZGP1 as a helpful biomarker and a potential indicator for early diagnosis of the occurrence of IgAVN.

Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients.

Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation. Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry. Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline. This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Proteomic research on new urinary biomarkers of renal disease in canine leishmaniosis: Survival and monitoring response to treatment

The objective of our study was to search for survival biomarkers (SB) and treatment response monitoring biomarkers (TRMB) in the urinary proteome of dogs with renal disease secondary to canine leishmaniosis (CanL), using UHPLC–MS/MS. The proteomic data are available via ProteomeXchange with identifier PXD042578. Initially, a group of 12 dogs was evaluated and divided into survivors (SG; n = 6) and nonsurvivors (NSG; n = 6). A total of 972 proteins were obtained from the evaluated samples. Then, bioinformatic analysis reduced them to 6 proteins like potential SB increased in the NSG, specifically, Haemoglobin subunit Alpha 1, Complement Factor I, Complement C5, Fibrinogen beta chain (fragment), Peptidase S1 domain-containing protein, and Fibrinogen gamma chain. Afterwards, SG was used to search for TRMB, studying their urine at 0, 30, and 90 days, and 9 proteins that decreased after treatment were obtained: Apolipoprotein E, Cathepsin B, Cystatin B, Cystatin-C-like, Lysozyme, Monocyte differentiation CD14, Pancreatitis-associated precursor protein, Profilin, and Protein FAM3C. Finally, enrichment analysis provided information about the biological mechanisms in which these proteins are involved. In conclusion, this study provides 15 new candidate urinary biomarkers and an improved understanding of the pathogenesis of kidney disease in CanL.

Dynamics of urine proteomics biomarker and disease progression in patients with IgA nephropathy.

Immunoglobulin A nephropathy (IgAN) frequently leads to kidney failure. The urinary proteomics-based classifier IgAN237 may predict disease progression at the time of kidney biopsy. We studied whether IgAN237 also predicts progression later in the course of IgAN. Urine from patients with biopsy-proven IgAN was analyzed using capillary electrophoresis-mass spectrometry at baseline (IgAN237-1, n=103) and at follow-up (IgAN237-2, n=89). Patients were categorized as "non-progressors" (IgAN237 ≤0.38) and "progressors" (IgAN237 >0.38). Estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio slopes were calculated. Median age at biopsy was 44years, interval between biopsy and IgAN237-1 was 65months and interval between IgAN237-1 and IgAN237-2 was 258days (interquartile range 71-531). IgAN237-1 and IgAN237-2 values did not differ significantly and were correlated (rho=0.44, P<.001). Twenty-eight percent and 26% of patients were progressors based on IgAN237-1 and IgAN237-2, respectively. IgAN237 inversely correlated with chronic eGFR slopes (rho = -0.278, P=.02 for score-1; rho = -0.409, P=.002 for score-2) and with ±180days eGFR slopes (rho = -0.31, P=.009 and rho = -0.439, P=.001, respectively). The ±180days eGFR slopes were worse for progressors than for non-progressors (median -5.98 versus -1.22mL/min/1.73 m2 per year for IgAN237-1, P<.001; -3.02 vs 1.08mL/min/1.73 m2 per year for IgAN237-2, P=.0047). In multiple regression analysis baseline progressor/non-progressor according to IgAN237 was an independent predictor of eGFR180days-slope (P=.001). The urinary IgAN237 classifier represents a risk stratification tool in IgAN also later in the course of the dynamic disease. It may guide patient management in an individualized manner.

Blood- and Urine-Based Liquid Biopsy for Early-Stage Cancer Investigation: Taken Clear Renal Cell Carcinoma as a Model

Liquid biopsy is a noninvasive technique that can provide valuable information for disease characterization by using biofluids as a source of biomarkers. Proteins found in biofluids can offer a wealth of information for understanding pathological processes. In this study, we used early-stage clear cell renal cell carcinoma (ccRCC) as a model to explore the proteomic relationships among tissue, plasma, and urine. We analyzed samples of tumor tissue, plasma, and urine from a cohort of 27 ccRCC patients with T1-2 stage and 27 matched healthy controls, using liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis. We integrated the differential proteins found in the three types of samples to explore ccRCC-associated molecular changes. Our results showed that both plasma and urine proteomes could reflect functional changes in tumor tissue. In plasma, cytoskeletal proteins and metabolic enzymes were differentially expressed, while in urine, adhesion molecules and defense proteins showed differential levels. The differential proteins found in plasma and urine both reflect the binding and catalytic activity of tumor tissue. Additionally, proteins only changed in biofluids could reflect body immune response changes, with plasma proteins involved in actin cytoskeleton and oxidative stress, and urine proteins involved in granulocyte adhesion and leukocyte extravasation signaling. Plasma and urine proteins could effectively distinguish RCC from control, with good performances (plasma/urine: 92.6%/92.6% specificity, 96.3%/92.6% sensitivity, and an area under the curve of 0.981/0.97). In conclusion, biofluids could not only reflect functional changes in tumor tissue but also reflect changes in the body's immune response. These findings will benefit the understanding of body biomarkers in tumors and the discovery of potential disease biomarkers.

#2749 QUANTIFICATION OF URINE PROTEINS FOR DISCOVERY OF BIOMARKERS FOR KIDNEY INJURIES BY DIA-PROTEOMICS

Abstract Background and Aims Recent advances in proteomics have enabled to quantify proteins comprehensively. We aimed to establish a platform of urine quantitative proteomics for selection of the biomarkers and assessment of health conditions. Since plasma proteins of varying amounts of were present in urine especially from proteinuric patients, we subtracted the plasma protein conteents from total urine proteomes after the quantitative proteomics for discovery of biomarkers for kidney injuries. On the other hand, urine proteins excreted from plasma may be indicators of pathophysiological conditions in the body, suggesting that the urine proteome profiles figure out health conditions and early discovery of any diseases in the future. Method More than 120,000 urine samples (∼10 ml each) have been collected from ∼15,000 patients with various diseases and healthy volunteers (HV) and stored in -20C freezers. In this study, urine samples were selected from HV, diabetic patients (DM) with or without microalbuminuria and other CKD patients. Proteins were precipitated by a methanol/chloroform precipitation method from 1 ml urine. The protein fractions were digested with trypsin and the tryptic peptides were purified by C18 column chromatography. The tryptic peptides of 200 ng each were analyzed by LC-MS (tims-TOFpro, Bruker) by quantitative Data-independent acquisition (DIA) proteomics and proteins were quantified by the DIA-nn tool. Major top 30 plasma proteins were removed from the quantitative proteomics data and kidney- and other urinary tract organ-derived proteins were normalized to compare their proportions in urine. Results Approximately 2,500 proteins were quantified by the DIA analysis. In urine from HV, proportions of plasma proteins were small, however, in urine from DM patients with microalbuminuria or CKD patients, the plasma protein contents were increased, resulting in relative decreases of urine proportions of other proteins derived from the kidney or other urinary tract organs. By subtraction of the plasma proteins from total urine proteins, protein proportions were comparable to select candidates of biomarkers for kidney injuries. Comparison of total urine protein profiles between HV and DM elucidated their differences, which might indicate changes of pathophysiological conditions. Conclusion By recent quantitative proteomics urine proteins were quantified comprehensively, facilitating discovery of biomarkers for kidney injuries and also evaluation of health conditions in the body.

#2561 URINE PROTEOMICS FOR PREDICTION OF DISEASE PROGRESSION IN PATIENTS WITH IGA NEPHROPATHY

Abstract Background and Aims IgA nephropathy (IgAN) may lead to kidney failure. The urinary proteomics biomarker-based classifier (IgAN237) may predict progression at the time of kidney biopsy [1]. We now studied whether IgAN237 predicts disease progression not only at the time of biopsy but also later in the course of IgAN. Method Urine samples from 103 patients with biopsy-proven IgAN were analyzed using capillary electrophoresis-mass spectrometry at baseline (IgAN237 score-1) and 89 also at follow-up (IgAN237 score-2). Patients were grouped into ‘non-progressors’ (IgAN237 score ≤0.38 Units) and ‘progressors’ (IgAN237 score &amp;gt;0.38 Units). Historical and follow-up data included, e.g., estimated glomerular filtration rate (eGFR)-slopes, urinary albumin/creatinine ratio (UACR)-slopes and medication. Multiple logistic regression analysis, using a stepwise model, was done with progressors and non-progressors as dependent factors including explanatory variables age, sex, eGFR- and UACR-slopes. Results Median age at biopsy was 44 years (range 11-92); 63% were male. Median interval between biopsy and IgAN237 score-1 was 65 months (0-606), and between IgAN237 score-1 and score-2 was 258 days (71-531). IgAN237 score-1 and score-2 values did not differ significantly and were correlated (rho = 0.44, p&amp;lt;0.001). Twenty-eight and 26% of patients were classified as progressors based on IgAN237 score-1 and score-2, respectively. The IgAN237 score inversely correlated with the chronic eGFR slopes (rho = -0.278, p = 0.02 for score-1; rho = -0.409, p = 0.002 for score-2) and with ±180days eGFR slopes (rho = -0.31, p = 0.009 and rho = -0.439, p = 0.001, respectively) (Figure 1). The ±180days eGFR-slopes were more reduced for progressors than non-progressors (median -5.98 versus -1.22 mL/min/1.73m2 per year for score-1, p&amp;lt;0.001; -3.02 vs 1.08 mL/min/1.73m2 per year for score-2, p = 0.047) (Figure 2). The only significant variable maintained in a multiple logistic regression analysis for IgAN237 score-1 (progressor vs non-progressor) was ±180days eGFR slope (p&amp;lt;0.001) and for IgAN237 score-2 the UACR (p = 0.002) and age at baseline (p = 0.016). Conclusion The urinary IgAN237 classifier represents a risk stratification tool in IgAN not only at the time of biopsy but also later in the course of the disease. It may guide physicians in management and follow-up strategies in an individualized manner.

#5482 CIRCULATING BIOMARKERS OF COLLAGEN REMODELING AS MARKERS FOR DEVELOPMENT OF MICROALBUMINURIA IN TYPE 2 DIABETES: THE PRIORITY TRIAL

Abstract Background and Aims Kidney disease progression is characterized by extensive deposition of extracellular matrix components such as collagen. Therefore, we assessed the effect of spironolactone on collagen remodeling biomarkers and investigated the association between the markers and development of microalbuminuria in serum of persons with type 2 diabetes and normoalbuminuria. Method We measured collagen type III (PRO-C3), VI (PRO-C6; endotrophin), VII (PRO-C7), and XXVIII (PRO-C28) formation, and a fragment of degraded crosslinked collagen type III (CTX-III) in serum of persons with high-risk (stratified by a urinary proteomics classifier, CKD273) for development of microalbuminuria, randomized to either spironolactone 25 mg or placebo. Crude and adjusted Cox models were applied to investigate the association between baseline biomarker levels and development of the primary outcome, persistent microalbuminuria, and development of microalbuminuria in at least one morning void sample, used as a secondary outcome. Results In the PRIORITY trial, spironolactone treatment did not prevent progression to microalbuminuria compared to placebo. All biomarkers were measured in 154 participants at baseline and 117 at end-of-study (week 208). Treatment with spironolactone did not affect serum levels of any of the investigated collagen remodeling biomarkers compared to placebo, and there were no differences in delta-biomarker levels between baseline and end-of-study (p ranging 0.277-0.875). In crude analyses, serum endotrophin was associated with both the primary (n = 44) and secondary outcome (n = 74) (HR per two-fold higher level: 1.58, 95% CI: 1.03-2.44, p = 0.037 and 1.62, 1.12-2.35, p = 0.010), whereas serum PRO-C3 was only associated with the secondary outcome (1.89, 1.19-2.99, p = 0.007). After adjustment for sex, baseline age, systolic blood pressure, estimated glomerular filtration rate, urinary albumin-creatinine ratio, and HbA1c, serum endotrophin and PRO-C3 remained significantly associated with the secondary outcome (1.52, 1.03-2.24, p = 0.036 and 1.73, 1.08-2.78, p = 0.022). Levels of PRO-C7, PRO-C28, and CTX-III were not associated with the specified outcomes. Neither baseline levels of serum endotrophin nor PRO-C3 correlated with baseline CKD273 levels (R: 0.00-0.06, p&amp;gt;0.47). Conclusion Treatment with spironolactone did not change serum levels of collagen remodeling biomarkers. Serum endotrophin, reflecting collagen type VI formation and the pro-fibrotic molecule endotrophin, and PRO-C3, a marker of collagen type III formation, were associated with development of microalbuminuria in at least one urine sample, suggesting that these biomarkers are relevant risk markers for kidney disease development in type 2 diabetes.

#4547 IDENTIFICATION OF GLYCOSYLATED IGF2 IN HUMAN URINARY PEPTIDOME AND ITS ASSOCIATION WITH CKD

Abstract Background and Aims Chronic kidney disease (CKD) is prevalent in 10% of world's adult population, with estimated glomerular filtration rate (eGFR) and albuminuria employed in its diagnosis. Role of protein glycosylation in causal mechanisms of CKD progression is largely unknown. Aim of this study was to identify urinary O-linked glycopeptides in association to CKD for better characterization of CKD molecular manifestations. Method Urine samples from eight CKD and two healthy subjects were analyzed by Capillary Electrophoresis-Tandem Mass Spectroscopy (CE-MS/MS) and glycopeptide analysis using Proteome Discoverer 1.4 was performed. Distribution of identified glycopeptides and correlation with Age, eGFR and Albuminuria were evaluated in 3810 datasets from the Human Urinary Proteome database using Spearman's rank correlation test and multiple linear regression analysis by statistical software R. The quantification of Insulin-like growth factor-II (IGF2) protein (7.5 kDa) was performed by enzyme-linked immunosorbent assay (ELISA) in two matched (for age, sex, no diabetes and no cardiovascular history) groups of 12 plasma samples each (eGFR &amp;lt; 30 ml/min/1.73m2 and eGFR &amp;gt; 90 ml/min/1.73m2). An open-source tool “Proteasix” was used to predict proteases involved in cleavage of the identified glycopeptides. The transcriptomics database and analysis tool “Nephroseq v4” were utilized to investigate the expression of predicted proteases in existing human transcriptomics CKD datasets. Results In total, 17 O-linked glycopeptides from 7 different proteins were identified, derived primarily from Insulin-like growth factor-II (IGF2) (n = 6). Glycosylation occurred at the surface exposed IGF2 Threonine 96 position. Further Investigation of the human proteome database containing 3810 datasets, showed that only three unglycosylated urinary peptides of IGF2 were identified, at significantly lower abundance and frequency (0-2.5% in the database) in comparison to the glycosylated forms (frequency of 23-90%). Interestingly, all identified IGF2 peptides belonged to E-domain of IGF2, i.e., 92 – 180 aa; which cleaves after undergoing O-linked glycosylation during the post-translational process yielding the “mature IGF2” (25-91 aa) protein. Results of multiple linear regression analysisa indicated that three IGF2 glycopeptides, DVStPPTVLPDNFPRYPVGKF [β(1024) = 0.08, p = 1.42*10−4], DVStPPTVLPDNFPRYPVG [β(560) = 0.01, p = 5.22*10−5] and DVStPPTVLPDNFPRYP [β(290) = 0.02, p = 1.07*10−4], exhibited positive correlation with Age and the glycopeptide tPPTVLPDNFPRYP showed strong negative association with eGFR [β(177) = -0.01, p = 4.21*10−9]. Along the same lines, the latter urinary IGF2 glycopeptide tPPTVLPDNFPRYP was found at increased [p &amp;lt; 2.2*10−16] abundance in CKD (n = 686) in comparison to healthy control (n = 229) datasets. In addition, IGF2 increased [p = 0.042] plasma levels were observed in CKD patients (eGFR &amp;lt; 30 ml/min/1.73m2 group) as supported by ELISA measurements. Protease predictions, considering also available transcriptomics data, suggest activation of cathepsin S with CKD potentially involved in the cleavage of the CKD-associated glycopeptide of IGF2. Conclusion Collectively, this study indicates that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place; which may be reflective of respective changes in the mature IGF2 protein abundance and function; as well as associated protease activity. Further, correlation analyses of the levels of the CKD-associated urinary IGF2 glycopeptide with the plasma IGF2 levels with CKD progression is planned. a Results of multiple linear regression where regression Co-efficient is β; degree of freedom is stated in (brackets); and p-value &amp;lt;0.05 was considered statistically significant

#3120 PREDICTION OF COVID-19 SEVERITY IN HOSPITALIZED PATIENTS USING URINARY PROTEOMICS AND MACHINE LEARNING

Abstract Background and Aims COVID-19 has been a significant public health concern for the last three years; however, not much is known about the mechanisms that lead to severe kidney outcomes in patients hospitalized with COVID-19. In this multicenter study, we combine isobaric TMT-tagged urinary proteomics and machine learning to predict severe kidney outcomes in hospitalized COVID-19 patients. Method Urine samples from hospitalized COVID-19 patients in two medical centers (Mount Sinai Hospital and University of Michigan) were used in this study in adherence with proper consenting protocols. Urine samples were prepared for LC-MS/MS analysis as previously reported [1]. The obtained spectra were analyzed using Proteome discoverer software and matched against Uniprot human database. For constructing the ML algorithm, the samples were randomly divided into discovery and validation set at a 2:1 ratio. Severe outcomes were defined as ICU admission, mechanical ventilation, acute kidney injury (AKI), death, or length of stay more than 21 days. Limma test was used on the discovery set to identify differentially expressed proteins and then features were selected using Boruta feature selection method. 10-fold cross validation on a random forest model was then applied to obtain receiver operating characteristic (ROC) curves. Results Urine samples from 120 PCR-positive COVID-19 patients from two different medical centers were collected within one week of hospitalization. More than 3,000 unique urinary proteins were identified using TMT-tagged mass spectrometry. For constructing a predictive algorithm, patients were stratified into severe and mild outcomes. Using Limma test on the discovery set, we identified differentially expressed proteins (DEPs) in severe outcome cohort vs the mild outcome cohort (Figure 1A). A set of 12 top features were identified using Boruta feature selection method and used for random forest model construction within the discovery set with 10-fold cross validation (Figure 1B). The generated ROC curves show that the algorithm demonstrated good predictive power for both discovery and validation set with 87% and 79% accuracy, respectively and close to 90% specificity (Figure 1C, D). On average, major adverse kidney events were observed in patients within 5-13 days after hospitalization. Enrichment analysis of DEP in COVID-19 patients compared to healthy patients showed significant upregulation of immune related processes and downregulation of proteolytic and metabolic processes. Enrichment analysis of DEPs in severe COVID-19 patients compared to mild COVID-19 patients showed significant upregulation of exocytosis and some immune related processes and downregulation of cell adhesion and extracellular matrix organization related processes (Figure 2A, B). Upregulated proteins were associated with kidney proximal tubular cells in addition to pulmonary alveolar cells (Figure 2C). Downregulated proteins were associated strongly with kidney cells such as podocytes and mesangial cells in addition to endothelial cells (Figure 2D). Conclusion Here, we developed an algorithm for prediction of severity in COVID-19 patients within 5-13 days after hospitalization. We further delineate potential mechanisms that drive severe outcomes in COVID-19 patients. Learnings from this study can be used for developing therapeutic options for long COVID, in addition to better preparedness in the event of other respiratory illnesses in the future.

PREDICTION OF CORONARY ARTERY DISEASE USING URINARY PROTEOMICS

Objective: Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. Design and method: Urinary proteome was analyzed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with a previously developed CAD-associated classifier, ACSP75. The Framingham risk score was available in 737 participants. Bioinformatics analysis was performed based on the CAD-associated peptides. Results: The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an AUC of 0.82 (95% CI: 0.78-0.87) for the CAD prediction in 8 years, superior to ACSP75 (AUC: 0.53, 95% CI: 0.47-0.60). On top of ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with CAD (HR: 1.54, 95% CI: 1.26-1.89, P&lt;0.0001) higher risk of CAD. The new classifier further improved the risk reclassification of CAD on top of the Framingham risk score (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001). Conclusions: A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

URINARY PROTEOMIC SIGNATURE OF THE RETINAL MICROVASCULATURE

Objective: The retinal vasculature provides a window into the body's microcirculation. Variations in retinal vessel diameters have been associated with adverse cardiovascular outcomes, such as stroke and coronary heart disease. As the pathogenesis of retinal microvascular diameter changes remain poorly understood, this study aimed to provide novel insights by characterizing the urinary proteomic signature of the retinal microvasculature. Design and method: The central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriole-to venule-ratio (AVR) were measured from digitized retinal photographs of 562 randomly recruited Flemish participants (mean age, 48.9 years; 50.9% women). The urinary proteome was analyzed using capillary electrophoresis coupled with mass spectrometry. Multivariable linear regression analysis identified significant urinary peptides with adjustment for age, sex, body mass index, smoking, hypertension, plasma glucose, total and HDL cholesterol, eGFR, and history of cardiovascular disease. Reactome pathway enrichment analysis was performed to interpret the proteomic signature. Results: Table 1 shows participant characteristics. Of the 359 peptides analyzed, 19 were significantly associated with CRAE after adjusting for potential confounders and multiple testing (Table 2). In the proteomic profile, 14 peptides (74%) were fragments of collagen I, III, and XI proteins, while the remaining peptides were fragments of the fibrinogen alpha chain (11%), xylosyltransferase 1 (5%), S100 calcium-binding protein A9 (5%), and hemoglobin subunit alpha (5%) proteins. Pathway analysis showed that these proteins were predominantly involved in endothelial cell proliferation and migration, collagen formation and degradation, and cell-extracellular matrix interactions (Figure 1). No peptides were significantly associated with CRVE and AVR following adjustment. Conclusions: This study revealed a novel urinary proteomic signature as a potential biomarker of retinal arteriolar narrowing. The signature suggests fibrosis and neovascularization may be involved in retinal microvascular diameter changes. Further investigation may help elucidate the pathophysiologic mechanisms underlying the relationship between microvascular and macrovascular disease.

Analysis of urine differential proteins in patients with allergic rhinitis

BackgroundAllergic rhinitis (AR) is one of the most common clinical allergic diseases. Early diagnosis and medical intervention will benefit patients with allergic rhinitis. In this study, we focused on changes in urine proteomics in AR patients to investigate their potential clinical utility in AR diagnosis and evaluation. Material and methodsTMT-labeled mass spectrometry-based proteomics was carried out to identify differentially expressed proteins (DEPs) in urine between allergic rhinitis patients and normal control groups. The molecular biological role of DEPs was investigated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein-protein interaction (PPI) network analysis. ResultsEnrichment analysis showed that the differentially expressed proteins were mainly related to cell-cell adhesion, complement and coagulation cascades, peptidase activity regulation, MAP kinase activity, etc. Compared with the NC group, HLA-DRB1, WFDC12, and DEFA4, among the top ten up-regulated proteins in the urine of the AR group, were related to the biological process of the humoral immune response. Among the top 10 down-regulated proteins, GUSB, SQSTM1, and KIT are related to protein domain-specific binding in terms of molecular function. ConclusionsWe found differential protein changes between AR patients and normal subjects may be related to the pathophysiological changes of AR, which provides the possibility for further exploration of urinary proteomics biomarkers in the future.

Excavation of diagnostic biomarkers and construction of prognostic model for clear cell renal cell carcinoma based on urine proteomics.

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients' prognosis. Urine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data. 133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05). Our study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.