Modeling a biofluid-derived extracellular vesicle surface signature to differentiate pediatric Idiopathic Nephrotic Syndrome clinical subgroups

Modeling a biofluid-derived extracellular vesicle surface signature to differentiate pediatric Idiopathic Nephrotic Syndrome clinical subgroups | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Modeling a biofluid-derived extracellular vesicle surface signature to differentiate pediatric Idiopathic Nephrotic Syndrome clinical subgroups Giulia Cricri, Andrea Gobbini, Stefania Bruno, Linda Bellucci, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4283782/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Idiopathic Nephrotic Syndrome (INS) is a common childhood glomerular disease requiring intense immunosuppressive drug treatments. Prediction of treatment response and the occurrence of relapses remains challenging. Biofluid-derived extracellular vesicles (EVs) may serve as novel liquid biopsies for INS classification and monitoring. Our cohort was composed of 106 INS children at different clinical time points (onset, relapse, and persistent proteinuria, remission, respectively), and 19 healthy controls. The expression of 37 surface EV surface markers was evaluated by flow cytometry in serum (n=83) and urine (n=74) from INS children (mean age=10.1, 58% males) at different time points. Urine EVs (n=7) and serum EVs (n=11) from age-matched healthy children (mean age=7.8, 94% males) were also analyzed. Tetraspanin expression in urine EVs was enhanced during active disease phase in respect to the remission group and positively correlates with proteinuria levels. Unsupervised clustering analysis identified an INS signature of 8 markers related to immunity and angiogenesis/adhesion processes. The CD41b, CD29, and CD105 showed the best diagnostic scores separating the INS active phase from the healthy condition. Interestingly, combining urinary and serum EV markers from the same patient improved the precision of clinical staging separation. Three urinary biomarkers (CD19, CD44, and CD8) were able to classify INS based on steroid sensitivity. Biofluid EVs offer a non-invasive tool for INS clinical subclassification and “personalized” interventions. Health sciences/Nephrology/Kidney diseases Health sciences/Biomarkers/Predictive markers Biological sciences/Biological techniques/Proteomic analysis Health sciences/Medical research/Pre clinical studies Health sciences/Medical research/Paediatric research Idiopathic Nephrotic Syndrome extracellular vesicles protein biomarkers steroid resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Idiopathic Nephrotic Syndrome (INS) is the most common form of glomerular disease in childhood, characterized by massive proteinuria, hypoalbuminemia, hyperlipidemia, and tissue edema 1 . The biological mechanisms leading to INS are poorly defined, but an immunological dysfunction has been advocated 2 . The current INS therapy is based on oral corticosteroids, which leads to complete remission in up to 80% of children, classified as steroid-sensitive (SSNS). However, SSNS children experience multiple relapses or even steroid dependence for which long-term immunosuppressive steroid-sparing agents are indicated 3 . Patients unresponsive to steroids (SRNS) show the most severe form and the need for kidney replacement therapy in 50% if remission is not achieved 4 . Both SSNS and SRNS can experience multiple adverse effects secondary to the immunosuppressive treatment 5,6 . There is therefore a major medical need to find early biomarkers to allow for a more precise selection and duration of the treatment and to characterize the INS subgroups for clinical studies. Extracellular vesicles (EVs) are nanosized particles naturally released by almost all cell types 7,8 and are easily found in many body fluids. EVs can carry selective surface markers inherited from their parent cells 9 , mirroring the functional state of the originating tissue. EVs contain lipids, proteins, and different forms of nucleic acids that are actively released from the cells from which they are derived 10 . Extensive investigation has been conducted on the wide array of molecules that can be enclosed within EVs, owing to their substantial relevance as biomarkers for various diseases 11 . Urinary EVs (uEVs) originate mainly from the kidney and urinary tract cells 12 and can be a source of important urinary biomarkers reflecting the molecular processes activated by kidney diseases 13 . uEVs have been used for an early diagnosis of chronic kidney disease (CKD) 14 , polycystic kidney disease 15 , active glomerulonephritis 16 , and tubulopathies 17 . Moreover, uEVs can be secreted by kidney-resident immune cells, acting as biomarkers of immune activation and tissue remodelling 18 . When the glomerular and tubular basement membranes are disrupted, uEVs may also derive from the bloodstream and represent other body compartments 19 . This study aimed to identify the expression signature of uEVs reflecting the different forms of INS in childhood and their possible response to the treatment before the start of therapy. Methods Patient Recruitment Strategy and clinical data Children with INS (first episode below 18 years old) were enrolled at the Pediatric Nephrology, Dialysis and Transplant Unit (Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico of Milano) in the period January 2022 to February 2023. A control group of age-matched children, with no kidney-related or immunological disease, was included in the study (CTRL). This study was conducted according to the principles expressed in the Declaration of Helsinki. Approve to the study was obtained by the IRCCS Ca’ Granda Institutional Review Board (ID 2633, INSiDe protocol). An informed consensus was obtained for all the participants enrolled in the study. Patients were treated with standard therapy with oral prednisone and classified according to the international guidelines as SSNS or SRNS 3,20 . Demographic data, current therapies, and responses to ongoing therapy were collected. Routine clinical and biochemical parameters were measured according to the clinical practice. Proteinuria was defined as urine protein/creatinine ratio (uPr/uCr) ≥ 0.2 mg/mg (mild proteinuria, 0.21–1.99 mg/mg and nephrotic range proteinuria, > 2 mg/mg). eGFR was calculated using the modified Schwartz formula 21 . Study Sample Collection Blood and urine were collected from children with INS and an age-matched control group (CTRL). Urine samples were processed within 6 hours (h) of collection according to established protocols 21,22 . Serum was obtained by activating the coagulation cascade from peripheral whole blood, followed by centrifugation at 3000 g for 20 min to remove corpuscular components. Aliquots of serum and urine were stored at -80°C until use. Urine and serum EVs characterization Unpurified extracellular vesicle (EV) from urine and serum samples were used. Dedicated urine samples underwent ultracentrifugation at 100.000 g for 2 h at 4°C for EV isolation (Beckman Coulter, OPTIMA XPN-90 Ultracentrifuge, Rotor Type 70-Ti, Brea, CA). EVs were then resuspended in PBS (Sigma-Aldrich) and freshly used or in 1% dimethyl sulfoxide (DMSO) (Sigma-Aldrich) and stored at − 80°C. Nanoparticle tracking analysis (NTA) was conducted on unpurified EVs using the Nanosight NS300 and analyzed as previously reported [24]. uCr was used as a normalization variable for particles number quantification in urines from both healthy subjects and patients with INS. Characterization involved transmission electron microscopy, super-Resolution Microscopy, and exoview analysis. Detailed procedures are in the Supplementary material (Supplementary technical method description). Cytofluorimetric analysis of EVs Unpurified uEVs (100 µL) and sEVs (1x 10 10 ) were analyzed using a MACSPlex human Exosome kit (Miltenyi Biotec) according to manufacturer’s instructions. For uEVs, surface marker median fluorescence intensity (MFI) was normalized against uCr to account for inter-patient EV variations based on the daily water intake. Flow cytometry was conducted on Cytoflex using CytExpert Software (Beckman Coulter, Brea, CA, USA), and data were analyzed with Flowjo software (Tree Star, Inc. Ashland, OR, USA). Raw data were reported in the Supplementary material (Supplementary cytofluorimetric data). Statistics Analysis was performed using RStudio (R v4.0.3) or Prism with GraphPad v9.0 (GraphPad Software, USA). Normalized cytofluorimetric signals were log-transformed for urine and serum markers. Transformed markers were used both for multivariate and univariate analysis. Principal components analysis (PCA) and clustering were done using FactoMineR (v2.4) and Complex Heatmap (v2.6.2), respectively. Two-sided Student’s or Mann-Whitney tests for pairwise comparisons and one-way ANOVA with Tukey’s post hoc tests or Kruskal-Wallis’s test with Dunn’s post hoc for multiple comparisons were selected based on data distribution. Correlation with biochemical variables was assessed using Pearson or Spearman coefficients. Marker frequency among patient groups was determined with a 50% threshold as previously reported 23 . The classification performance of both single markers and their combinations was evaluated using combiROC (v0.2.3) (sensitivity ≥ 40, specificity ≥ 70, AUC). Odds ratios (ORs) were calculated with univariate logistic regression (OR > 1 indicates increased likelihood of association with SRNS appearance, OR < 1 indicates decreased association). Significance was set at P < 0.05. Results Demographic and Clinical Variables Table 1 summarizes the clinical and biochemical characteristics of the INS study cohort (n = 105, 58% males) and healthy controls (CTRL) (n = 19, 94% males). The study cohort includes SSNS (n = 80) and SRNS (n = 25). Table 1 Clinical characteristics of the enrolled patients at the time of collection. Parameters Healthy children CTRL Active INS disease (n = 71) SSNS SRNS Inactive INS disease (n = 34) SSNS SRNS (n = 19) Onset (n = 17) Rel (n = 37) (n = 17) Rem (n = 26) Rem (n = 8) Demographic and clinical characteristics Sex, n (%) Male 18, (94.7) 43, (60.6) 11, (64.7) 24, (64.9) 8, (47.1) 18, (52.9) 13, (50) 5, (62.5) Female 1, (5.3) 28, (39.4) 6, (35.3) 13, (35.1) 9, (52.9) 16, (47.1) 13, (50) 3, (37.5) Age (yr), median (IQR) 6 ( 2 – 11 ) 9, ( 4 – 15 ) 4 ( 3 – 13 ) 8 ( 5 – 12 ) 15 (12-18.5) a, b, c 11, (8-14.25) 11 (6-13.25) 15 (8.25–17.75) Age at onset (yr), median (IQR) N/A 4, ( 3 – 12 ) 4 ( 3 – 13 ) 4 ( 2 – 5 ) 9 ( 4 – 15 ) c, d 5, ( 3 – 8 ) 4 (2.37–7.25) 7 (5.25–12.50) Drugs treatment at the time of collection, n (%) Immunosuppressants 0, (0) 25, (35.2) 0, (0) 14, (37.8) 11, (64.7) 30, (88.2) 24, (92.3) 6, (75) Others 0, (0) 4, (5.6) 0, (0) 0, (0) 4, (23.5) 0, (0) 0, (0) 0, (0) NT 19, (100) 42, (59.1) 17, (100) 23, (62.2) 2, (11.8) 4, (11.8) 2, (7.70) 2, ( 25 ) Biochemical parameters Protein-to-creatinine ratio, median (IQR) N/A 5.85, (1.86–8.7) f 8.5 (4.96–9.27) d, e 3.7 (1.60–8.52) d, e 2.68 (0.96–8.78) d 0.15, (0.12–0.18) 0.14 (0.11–0.16) 0.31 (0.19–0.63) Urine creatinine (g/L), median (IQR) 0.77 (0.42–1.23) 0.99, (0.59–1.6) 0.8 (0.47–1.56) 1.0 (0.63–1.64) 0.98 (0.57–1.88) 1.19, (0.8–1.54) 1.05 (0.57–1.48) 1.49 (1.02–1.80) Serum creatinine (mg/dL), median (IQR) 0.46 (0.29–0.56) 0.45, (0.31–0.68) 0.31 (0.26–0.50) e 0.40 (0.32–0.59) 0.84 (0.56–1.06) a, b, c 0.52, (0.46–0.62) 0.51 (0.43–0.57) 0.67 (0.47–0.95) eGFR (mL/min), median (IQR) 107 (86–132) 118, (91–145) 138 (101.5-163.5) 122 (106.8-149.8) 74 (58.50–106) b, c, d 115, (93–126) 117 (96.45–126.8) 99 (78-119.5) Immunoglobulins (mg/dL), median (IQR) IgG N/A 383, (148–618) f 150 (93–230) c, d, e 487 (365.5-719.5) 571 (186.5–679) 695, (500.3–919) 695 (465-849.3) 763 (644.8–1128) IgM N/A 127, (96.3–184) f 121 (100-201.5) d 127 (72–163) 162 (114.8–194) d 66, (38–110) 57 (37.5–112) 82 (58.25–118.8) IgA N/A 92, (72.3-138.8) 103 (57-168.5) 83 (59–129) 92 (69-192.5) 98, (46–151) 71 (31.5–110) 143 (95.50–234) Immunosuppressant drugs: prednisone, mycophenolate, tacrolimus, Others: ramipril, NT = not treated. IQR = interquartile range. N/A = not applicable. Age: SRNS vs. (a) CTRL (p < 0.01), (b) patients with SSNS onset (p < 0.001) and (c) SSNS Rel (p < 0.01). Age at the onset: SRNS vs. (c) SSNS Rel (p < 0.01) and (d) SSNS Rem (p < 0.05). Protein-to-creatinine ratio: SSNS Onset vs. (d) SSNS Rem (p < 0.001) and (e) with SRNS Rem (p < 0.001); SNSS Rel vs. (d) SSNS Rem (p < 0.001) and (e) SRNS Rem (p < 0.05); SRNS vs. (d) SSNS Rem (p < 0.001); Active INS vs. (f) Inactive INS (p < 0.001). Serum Creatinine: SSNS Onset vs. (e) SSNS Rem (p < 0.05); SRNS vs. (a) CTRL (p < 0.01), (b) SSNS onset (p < 0.001) and (c) SSNS Rel (p < 0.001). eGFR: SRNS vs. (b) SSNS onset (p < 0.001), (c) SSNS Rel (p < 0.001) and (d) SNSS Rem (p < 0.05). IgG: SNSS Onset vs. (c) SSNS Rel (p < 0.01), (d) vs. SSNS Rem (p < 0.001) and (e) SRNS Rem (p < 0.001); Active INS vs. (f) Inactive INS (p < 0.001). IgM: SSNS Onset and SRNS vs. (d) SSNS Rem (p < 0.05); Active INS vs. (f) Inactive INS (p < 0.001). The INS population was then subdivided into five groups: Group 1 SSNS patients at the onset of the disease (n = 17, SSNS Onset); -Group 2 SSNS at relapse (SSNS Rel, n = 37); -Group 3 SRNS patients with persistent proteinuria (above 0.5 mg/mg) (SRNS, n = 17); -Group 4 SSNS in remission (SSNS Rem, n = 26); -Group 5 SRNS patients who achieved complete response to second-line treatments (SRNS Rem, n = 8). A control group of age-matched children, with no kidney-related or immunological disease, was included in the study (CTRL). In selected experiments, children in groups 1, 2, and 3 were selected as patients with active INS (n = 71), while children in groups 4 and 5 were in the remission phase of the disease (inactive INS, n = 34). In our cohort, the age was homogenously distributed with a median of 8.5 years (IQR: 4–13) in SSNS and 6 years (IQR: 2–11) in CTRL, while SRNS patients had a higher median age of 15 years (IQR: 10–18). eGFR was reduced (74 mL/min) in SRNS compared to SSNS subgroups (138 and 122 mL/min for SSNS Onset and SSNS Rel, respectively). Serum IgG and IgM levels varied according to disease phase 24 , as expected. Qualitative and quantitative evaluation of urine EVs in INS. EVs isolated from the urine of INS children (uEVs) exhibited a heterogeneous size and preserved membranes, confirmed by TEM analysis (Fig. 1 A). ​Super-resolution microscopy analysis revealed tetraspanin distribution on single vesicles in both CTRL and INS patients (Fig. 1 B). Quantitative analysis showed significantly higher CD63 + and CD81 + uEVs in active INS (62,500 ± 23,300 and 65,700 ± 21,000 respectively, P < 0.05 and P < 0.01 ) compared to CTRL (22,100 ± 10,200, and 22,000 ± 9,600) and inactive INS (only for CD81, 28,800 ± 10,500, P < 0.05 ) (Fig. 1 C). Capture separation revealed different co-expression patterns among tetraspanins (Fig. 1 D), particularly significant for CD9/CD63 and CD81/CD63 in the active phase compared to CTRL ( P < 0.05 and < 0.01 , respectively). EVs co-expressing CD63/CD81 also showed a different distribution between the active and inactive INS stages ( P < 0.05 ). Triple-positive uEVs were less abundant but significantly increased in the active phase when captured with CD9 ( P < 0.001 vs CTRL and P < 0.01 vs inactive INS) and CD81 antibodies ( P < 0.05 vs both groups). Correlations between uEVs/mL in INS children’s urine and kidney function parameters (uCR, uPr/Cr, eGFR) were investigated (Fig. 2 A). Spearman’s analysis revealed a significant positive correlation between uEV numbers and uCr levels (R = 0.29, P < 0.01 ), as well as with the uPr/uCr (R = 0.22, P < 0.05 ) ratio. uEV dimensions negatively correlated only with the uPr/uCr levels (R=-0.27, P < 0.01 ) (Fig. 2 B). These associations persisted when INS patients were separated from controls (Additional File 1: Figure S1 ). NTA analysis (Fig. 2 C) showed increased uEV numbers (uEVs/uCr) in SSNS Onset compared to CTRL ( P < 0.01 ), and the other groups and clinical time-points ( P < 0.001 ). No gender-based differences were observed in uEV numbers and size in INS (Fig. 2 C, D). EV size distribution decreased only between SRNS and SSNS in remission ( P < 0.05 , SSNS Rem vs SRNS Rem) (Fig. 2 D). uEV surface marker characterization in different INS groups. uEV surface marker profile was evaluated by flow cytometry in seventy-four INS patients. CD9-CD63-CD81 EV expression positively correlated with uPr/uCr (Fig. 3 A), with CD9 most significantly associated with the active state. Tetraspanin levels were higher in active INS compared to controls and inactive INS (CD9 and CD63 P < 0.05 vs CTRL and P < 0.001 vs inactive INS; CD81 P < 0.001 vs inactive INS) (Fig. 3 B). Global EV-surface antigens distribution in each INS, analyzed through PCA, distinctly separated SSNS in relapse from onset and the SRNS group (Fig. 3 C). Unsupervised hierarchical clustering confirmed the clustering of INS patients in the active phase (Fig. 3 D). Marker frequency analysis revealed exclusive markers in INS children (CD25, CD20, CD11c, CD2, CD49e, CD62p, and CD42a) with higher frequencies during the active phase than during remission (Frequency > 50%). SSNS showed prominent immune markers (CD25, CD11c, CD20, CD1c, CD40) and adhesion molecules (CD49e), while SRNS exhibited higher levels of adaptive immune-related proteins (CD4, CD8, CD19), monocyte markers (CD11c, CD2, CD1c), and adhesion molecules (CD49e, CD146) compared to the SSNS group (Fig. 3 E). Identification of uEV-associated markers to distinguish the active phase of INS disease. Nineteen core EV-surface markers were identified as potential discriminants between active and inactive INS (Fig. 4 a), with eight effectively distinguishing the active phase from CTRL (Fig. 4 A). Among them, the three markers that achieved the best discrimination were CD41b, CD105, and CD29 with an AUC > 0.88 (Fig. 4 B). These core markers, excluding CD24, CD1c, CD11c, CD25, and CD40, and with the addition of CD19 and CD69 molecules, exhibited a significant positive correlation with proteinuria levels in INS children (Table 2 ), suggesting an association between kidney damage and uEVs of distinct cellular origin. Seven markers effectively separated SSNS at the onset from CTRL (Supplementary material; Table S1 ), with CD41b achieving the highest performance (AUC > 0.9). The same cluster of markers except for CD20 and CD42a, but including CD326, CD9, CD133, CD63, CD81, and CD24 differentiated between SSNS Rel and SSNS Rem groups (Supplementary material; Table S2), with CD29 showing the best AUC. When the SRNS group was analyzed, almost half of the markers were differentially expressed between patients with persistent proteinuria or in remission after second-line immunosuppressive treatments (best AUC for CD9, CD19, and CD146) (Supplementary material, Table S3). Table 2 Urine-EV biomarkers and their correlation to pathological proteinuria. P value < 0.05 was considered significant. Cellular Origin Markers R p-value Immune cell compartment CD19 0.43 < 0.001 CD20 0.38 < 0.001 HLA-DR 0.62 < 0.001 CD69 0.29 < 0.01 CD86 0.26 < 0.05 Endothelial/platelet activation CD62p 0.32 < 0.01 CD41b 0.71 < 0.001 CD42a 0.33 < 0.01 CD105 0.44 < 0.001 CD142 0.23 < 0.05 Adhesion cell activation CD29 0.68 < 0.001 CD326 0.59 < 0.001 MCSP 0.32 < 0.01 Stem/Progenitor cell activation CD133 0.43 < 0.001 SSEA-4 0.48 < 0.001 ROR1 0.44 < 0.001 Identification of a candidate uEV panel to discriminate patient’s sensitivity to the steroid therapy. uEVs from SSNS Rel and SRNS patients were compared to identify biomarkers that differentiated significantly the two groups. Markers encompassing both innate (CD11c, CD209, and CD1c) and adaptive immune responses (CD19, CD4, and CD8), along with those involved in angiogenesis/adhesion and stemness processes (CD31, CD44, CD146, and ROR1) were identified (Fig. 5 A). CD146 emerged as a significant predictor for SRNS (Fig. 5 B). However, the best separation between SRNS and SSNS Rel groups was achieved by the combination of CD19-CD44-CD8 (AUC = 0.87) (Fig. 5 C and Table 3 ). Table 3 Diagnostic performance of individual, combined uEV markers and proteinuria in steroid sensitivity classification. Markers AUC Sensitivity Specificity ACC CD19-CD44-CD8 0.873 0.833 0.913 0.886 CD146 0.844 0.75 0.826 0.8 CD44 0.797 0.833 0.826 0.829 CD19 0.777 0.917 0.696 0.771 CD8 0.75 0.583 0.913 0.8 uPr/uCr 0.569 0.583 0.696 0.657 Generation of INS expression signature using the serum and urinary EVs from the same patient. EVs enriched in the serum (sEVs) were also characterized in eighty-three INS patients. NTA analysis revealed significantly higher sEV numbers in SSNS at onset (7.87e11 ± 3.13e11 part/mL, P < 0.01 ) and relapse compared to CTRL (6.50e11 ± 2.92e11 part/mL vs 3.79e11 ± 1.38e11, P < 0.05) (Fig. 6 A). sEVs were also increased in SRNS with persistent proteinuria compared to CTRL ( P < 0.001 ) and SSNS Rem ( P < 0.05 ). No differences in sEV size were observed among groups or after gender-related INS separation (Fig. 6 B). CD9 expression in sEVs decreased during the active phase compared to CTRL and inactive INS ( P < 0.01 and < 0.05 , respectively) (Fig. 6 C). Three markers on serum EV surfaces were identified as potential discriminants for active INS (not shown), with lower discriminating power compared to urine in distinguishing INS patients from controls. The combination of serum and urine markers from the same patient was able to reach a statistical correlation as observed in Fig. 6 D, with CD3 and CD29 exhibiting significant positive/negative relations in both matrices (R = 0.25, P = 0.047 and R=-0.26, P = 0.039 , Pearson correlation) (Fig. 6 D). Likewise, the combination of sCD146 and uCD29-uCD41b separated active INS patients from healthy children with higher sensitivity (SE ~ 1) compared to single markers; the combination of CD41b in serum and urine distinguished SSNS at onset from CTRL, and in SSNS, uCD326, and sCD8 effectively differentiated disease activity (Table 4 ). Table 4 Diagnostic performance of uEV and sEV markers in pediatric INS between the different analyzed groups. s-uEVs Markers AUC Sensitivity Specificity ACC Groups sCD146-uCD29-uCD41b 1 1 1 1 Active vs CTRL sCD146 0.8 0.6 1 0.676 uCD29 0.914 0.867 1 0.89 uCD41b 0.933 0.867 1 0.89 sCD41b-uCD41b 1 1 1 1 sCD41b 0.964 0.875 1 0.933 Onset vs CTRL uCD41b 0.911 0.875 1 0.933 sCD8-uCD326 1 1 1 1 SSNS Rel vs SSNS Rem sCD8 0.641 0.533 0.778 0.625 uCD326 0.97 0.867 1 0.917 Discussion In the present study, we analyzed the surface antigen profile of urine and serum EVs in a cohort of pediatric INS patients, searching for new biomarkers for the accurate subclassification of INS sub-cohorts for clinical studies. Through the combination of a standardized surface proteomic analysis with a bioinformatic approach, a urine EV-based signature was generated, discriminating different forms of childhood INS compared with a cohort of pediatric controls. Urine-EV signature was mainly characterized by markers of endothelial/platelet and immune stimulation during proteinuria events. Conversely, the combination of serum and urine EVs was able to improve the separation between the different groups of childhood INS. We found that at the onset of the disease, patients present more EVs in their urine compared to healthy children. This was in line with previous studies showing the association of EV abundance with different pathological conditions related to the kidney 25 . EV concentration can be influenced by age 26 . In our patient’s cohort, the age was uniformly distributed among the different phases of the disease and comparable to the control group. The only form showing a different age distribution was the SRNS group classically characterized by a higher age of appearance of the disease 27 . However, no differences were highlighted in their EV number in respect to the SSNS group. The number of uEVs also showed a positive correlation with proteinuria levels in the different INS groups. The classical tetraspanin members were detectable in the urine EVs, with the highest expression observed from CD9, followed by CD81 and CD63 in proteinuric INS. When a co-expression pattern was investigated, double-positive CD63/CD81 vesicles were identified as the most enriched EVs in the INS active phase, with a significant reduction during the inactive phase. Interestingly, all the tetraspanins showed a stronger association with proteinuria level, where the highest correlation was achieved for CD9, displaying a direct relation between tetraspanins and kidney dysfunction. We here, for the first time, characterized uEVs in INS patients, tracking their cellular sources using a standardized flow cytometric assay able to simultaneously analyze 37 different markers. A characteristic uEV protein electrophoresis profile was previously found able to discriminate INS from other non-glomerular kidney diseases 28 . Previous studies by Burello et al. used the same cytofluorimetric technology to investigate the surface antigen profile of blood and urine EVs in ischemic brain injury 29 and rejection episodes associated with heart and kidney transplantation 30,31 . The uEV concentration can be dependent on the excretion/fusion EV rate and the overall urine concentration. Blijdorp et al. demonstrated that uEV concentration highly correlates with urine creatinine, potentially replacing the need for uEV quantification to normalize spot urines 32 . Indeed, urine creatinine levels are commonly used to normalize the excretion rate of different urinary analytes 33,34 . In our study, a positive correlation between the concentration of released EVs and urine creatinine was observed. Therefore, we employed the urinary creatinine measure to normalize the relative excretion rate of uEV proteins. We found that INS patients in the active phase were separated from healthy children based on an exclusive surface signature of 8 markers with a strong overrepresentation of adhesion/endothelial activation markers. Three markers, CD41b (platelets origin), CD105 (endothelial marker), and CD29 (adhesion molecule), showed the best diagnostic score. The CD41b was the principal marker, separating treatment-naïve INS patients at the onset of the disease from controls, thus representing a promising diagnostic biomarker. Interestingly, thromboembolic events represent a possible complication of INS 35 . Moreover, serum endothelial and platelet microparticles were previously described to inversely correlate with kidney function recovery in transplanted patients 31,36 . The EV detected in our samples could potentially derive from the serum since EVs can pass through the membrane pores of the glomerular filtration barrier when kidney damage occurs 19 . Urine EVs can also derive directly from the kidney compartment, where alterations of the glomerular endothelium have been identified in patients with SSNS in relapse and correlate with poor clinical outcomes 37,38 . Concomitantly, in children with minimal change disease, activation of the integrin CD29/FAK axis was detected in damaged podocytes, suggesting that CD29-enriched EVs could originate from this population 39 . When the EV profile was performed in the same patient using both serum and urine as sources, a better separation of the different stages of INS was achieved. The combination of serum-derived CD146 and urinary-derived CD29 and CD41b reached the best score in separating INS patients in the active phase from healthy children compared to the single markers. While the combination of a unique marker in serum and urine, the CD41b, better distinguished SSNS patients at the onset from CTRL. This approach was also proposed for the discrimination of different pathological states in other diseases 31,40 . A signature of 30 miRNAs was previously described to distinguish patients with active proteinuria from patients in clinical remission 41 and correlated with the disease gravity 41 . Similarly, in our experiments, the surface protein markers, CD29, CD41b, and HLA-DR, better correlated with proteinuria levels in our patients’ cohort, presenting a disease signature that might potentially anticipate the clinical course of the disease. When each INS subgroup was analyzed, we identified the best classification marker to distinguish the different disease stages in both SRNS and SSNS patients. We found that CD29 was the marker that better discriminates relapse episodes from remission in SSNS patients with an AUC of 0.913, while the SRNS patients were sorted based on the expression of the tetraspanin molecule CD9 (AUC = 1). The expression of integrins was already implicated in the pathogenesis of multiple kidney diseases 42 . Our data corroborates with the recent finding of the group of J. Kennedy, which demonstrated that urinary podocyte-derived large EVs were able to distinguish between relapse and remission phases in children with INS 43 . Additionally, the analysis of biofluids from the same patients in our cohort was able to generate a multi-biomarker combination (CD8 sEVs and CD326 uEVs), which could predict the rate of remission and relapse in SSNS. We also investigated the role of uEVs surface proteomes in stratifying INS-affected subjects according to steroid sensitivity. Prior investigations identified WT-1 as a marker of urinary exosomes in INS. Despite that, WT-1 expression was ineffective in the prediction of steroid responsiveness in these children 44 . In our study, uEVs were able to separate patients with SSNS from those affected by the SRNS form. Among them, the endothelial molecule CD146 better predicts SRNS than the urine Albumin: creatinine ratio, with an AUC equal to 0.84. Interestingly, plasma CD146 levels were shown to be progressively increased in early-stage diabetic nephropathy (DN), functioning as an optimal marker in the discrimination of DN severity 45 . However, SSNS and SRNS patients were again better separated by the combination of three markers, CD19, CD8, and CD44, which strongly classified patients based on steroid response. The CD19 + B cell levels were recently demonstrated to predict steroid responses in SRNS patients with high sensitivity and acceptable accuracy 46 . Our data were also in line with a previous study showing the correlation between CD44 expression in kidney biopsies and the higher prevalence of SRNS as well as a negative kidney outcome 47 . Some limitations should be acknowledged. Firstly, the results of our study were limited by the incidence of INS which is classified as a rare disease. Moreover, a longitudinal study on the same patient will be more instrumental in predicting the progression of the disease. However, this study sets the basis to apply the biofluid EV modeling to monitor the ongoing immune-related kidney damage after INS appearance. In conclusion, our study showed that urinary and serum EV-surface profiles may efficiently separate different forms of childhood INS at different stages of the disease. The urine EVs phenotype mirrored the ongoing immune and endothelial/platelet activation of the disease's active phase. Thus, the EV-surface proteins by reflecting disease-specific features may function as innovative biomarkers in this disease. Declarations Acknowledgments We acknowledge for their support: IMPACTsim S.p.A, ABN (Fondazione bambino nefropatico ONLUS), and Fondazione Nuova Speranza S.p.A ONLUS. The authors wish to thank the Cytofluorimetric facility of the Instituto Nazionale of Genetica Molecolare (INGM) for the cytofluorimetric analysis assistance. Authors’ contributions Conceptualization, G.C., W.M., F.C. Data curation, G.C., and A.G. Investigation, G.C. Writing—original draft, G.C. Methodology, G.C., A.G., S.B., L.B., S.T., C.T., T.N., I.P., and A.B. Resources, I.P., and A.B. Formal analysis, G.C., A.G., S.B., S.T., and F.Ca. Supervision, and Funding acquisition, F.C., and G.M. S.B., R.G., B.B., W.M., G.M., and F.C. participated in writing and editing the manuscript. All authors contributed to the article and approved the submitted version. All authors read and approved the final manuscript. Funding Ministero dell’Istruzione, dell’Università e della Ricerca (2022B9WC3F) and IMPACTsim S.p.A funding support (Grant P-0038). Data availability statement All data generated or analyzed during this study are included in the published article [and its supplementary information files]. Ethics approval and consent to participate statement The study was approved by the IRCCS Ca’ Granda Institutional Review Board (ID 2633, INSiDe protocol). An informed consensus has been signed by the parent and/or legal guardian of all the children enrolled in the study. Consent for publication Not applicable. Competing interests All authors have reviewed the journal’s policy concerning competing interests. The authors affirm that they have no competing interests to declare. References Eddy, A. A. & Symons, J. M. Nephrotic syndrome in childhood. The Lancet 362, 629–639, DOI: 10.1016/S0140-6736(03)14184-0 (2003). 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Minimal Change Disease Is Associated With Endothelial Glycocalyx Degradation and Endothelial Activation. Kidney Int Rep 7, 797–809, DOI: 10.1016/j.ekir.2021.11.037 (2022). Royal, V. et al. Ultrastructural characterization of proteinuric patients predicts clinical outcomes. Journal of the American Society of Nephrology 31, 841–854, DOI: 10.1681/ASN.2019080825 (2020). Cara-Fuentes, G. et al. β1-Integrin blockade prevents podocyte injury in experimental models of minimal change disease. Nefrología 44, 90–99, DOI: 10.1016/J.NEFRO.2022.11.004 (2022). Nagatani, K., Sakashita, E., Endo, H. & Minota, S. A novel multi-biomarker combination predicting relapse from long-term remission after discontinuation of biological drugs in rheumatoid arthritis. Sci Rep 11, 20771, DOI: 10.1038/s41598-021-00357-9 (2021). Chen, T. et al. Increased urinary exosomal microRNAs in children with idiopathic nephrotic syndrome. EBioMedicine 39, 552–561, DOI: 10.1016/j.ebiom.2018.11.018 (2019). Trevillian, P., Paul, H., Millar, E., Hibberd, A. & Agrez, M. V. αvβ6 integrin expression in diseased and transplanted kidneys. Kidney Int 66, 1423–1433, DOI: 10.1111/j.1523-1755.2004.00904.x (2004). Myette, R. L. et al. Urinary podocyte-derived large extracellular vesicles are increased in paediatric idiopathic nephrotic syndrome. Nephrology Dialysis Transplantation 38, 2089–2091, DOI: 10.1093/ndt/gfad086 (2023). Lee, H. K. et al. Urinary exosomal WT1 in childhood nephrotic syndrome. Pediatric Nephrology 27, 317–320, DOI: 10.1007/s00467-011-2035-2 (2012). Fan, Y. et al. Expression of Endothelial Cell Injury Marker Cd146 Correlates with Disease Severity and Predicts the Renal Outcomes in Patients with Diabetic Nephropathy. Cellular Physiology and Biochemistry 48, 63–74, DOI: 10.1159/000491663 (2018). Deng, Y. et al. Peripheral blood lymphocyte subsets in children with nephrotic syndrome: a retrospective analysis. BMC Nephrol 24, DOI: 10.1186/s12882-022-03015-y (2023). Roca, N. et al. CD44-negative parietal–epithelial cell staining in minimal change disease: association with clinical features, response to corticosteroids and kidney outcome. Clin Kidney J 15, 545–552, DOI: 10.1093/ckj/sfab215 (2022). Additional Declarations No competing interests reported. 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19:37:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4283782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4283782/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-76727-w","type":"published","date":"2024-10-28T16:05:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55634227,"identity":"2a4f910f-c82e-4132-ae9f-4d9bcf73924c","added_by":"auto","created_at":"2024-04-30 20:09:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4952993,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of EVs released into the urine of INS children. (\u003cstrong\u003eA\u003c/strong\u003e) TEM analysis of purified EVs from urines of CTRL, SSNS Rel, SRNS, and SSNS Rem patients; representative low‐ and high‐power field images (inset panel), showed heterogeneous EV population; scale bars= 500 µm and 100 µm. (\u003cstrong\u003eB\u003c/strong\u003e) Distribution visualization of tetraspanins in uEVs by Super‐Resolution Microscopy (SRM); representative SRM images of CTRL (up panel) and INS uEVs (bottom panel) showing the expression of CD9 (yellow), CD63 (blue), and CD81 (purple). (\u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e) High throughput fluorescence profiling of tetraspanins expression in single particles released into the urine of CTRL (n=3), and INS patients both in the active (n=6), and inactive phase of the disease (n=4); (\u003cstrong\u003eC\u003c/strong\u003e) number of fluorescent particles expressing each tetraspanin compared to non-specific capture IgG (IgG) and (\u003cstrong\u003eD\u003c/strong\u003e) tetraspanins co-expression pattern determined by multiple detection antibodies on single tetraspanins capture spot (n=3 technical replicates representing 3 capture spots each). *\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e, **\u003cem\u003eP\u0026lt;0.01\u003c/em\u003e, and ***\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e; One-way ANOVA with Tukey’s post-test.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/ce289141023c2ee9ae70609b.png"},{"id":55634224,"identity":"0b48c7a4-b7c7-4264-8276-3375a1514770","added_by":"auto","created_at":"2024-04-30 20:09:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1036304,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of uEVs from CTRL and INS patients by NTA and correlation with renal function parameters. Correlations of uEVs concentration (\u003cstrong\u003eA\u003c/strong\u003e) and size (\u003cstrong\u003eB\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003ewith the renal function biochemical parameters (uCr, uPr/uCr, and eGFR) are reported. R=Spearman correlation coefficient.\u003cem\u003e P\u0026lt;0.05 \u003c/em\u003ehas been considered statistically significant. uEVs concentration (number of particles/urine creatinine, uEV/ uCr) (\u003cstrong\u003eC\u003c/strong\u003e) and size distribution (mode) (\u003cstrong\u003eD\u003c/strong\u003e) in the INS group and CTRL are reported. Analysis of the number (\u003cstrong\u003eC\u003c/strong\u003e, right panel) and size (\u003cstrong\u003eD\u003c/strong\u003e, right panel) of uEVs between males and females in the INS cohort is also shown. Data are presented as means ± standard deviation (SD) of individual data points. \u003cem\u003e**P\u0026lt;0.01\u003c/em\u003e vs. CTRL, \u003cem\u003e***P\u0026lt;0.001 \u003c/em\u003evs. SSNS Rel, SRNS, SSNS, and SRNS Rem; \u003cem\u003eƒP\u0026lt;0.05\u003c/em\u003e, vs. SRNS Rem; One-way ANOVA with Tukey’s post-test.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/f9c372d295c11d80368068fb.png"},{"id":55634225,"identity":"ccf391f2-d165-4464-a29f-bb2cb5fd113b","added_by":"auto","created_at":"2024-04-30 20:09:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2835309,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of urinary EV signature in INS children. (\u003cstrong\u003eA\u003c/strong\u003e) Spearman’s correlation analysis between tetraspanins expression (Median Fluorescence Intensity, MFI on X axis) and biochemical parameters of renal function (uCr, uPr/uCr, eGFR);R=Spearman correlation coefficient, \u003cem\u003eP\u0026lt;0.05 \u003c/em\u003ehas been considered statistically significant. (\u003cstrong\u003eB\u003c/strong\u003e) Flow cytometry characterization of tetraspanins in EVs from INS patients at different clinical time points, expressed as normalized MFI (MFI/ uCr) (CTRL n=7, Active INS n=51 and Inactive INS n=23). *\u003cem\u003eP\u0026lt;0.05\u003c/em\u003evs. CTRL; ###\u003cem\u003eP\u0026lt;0.001 \u003c/em\u003evs. Active INS; Kruskal-Wallis non-parametric with Dunn’s post-test. (\u003cstrong\u003eC\u003c/strong\u003e) Principal component analysis (PCA) plots individuals' urine EVs protein content (dim1 vs dim2, dim1 vs dim3, dim2 vs dim3). CTRL, active, and inactive INS patient groups were analyzed (each patient's subgroup is defined by color). (\u003cstrong\u003eD\u003c/strong\u003e) Heatmap analysis of uEV signature distribution detected by cytofluorimetric analysis in the analyzed cohort (z-score distribution for each protein). (\u003cstrong\u003eE\u003c/strong\u003e) The frequency distribution (%) of the 34 exosomal protein markers in INS patients and control children is indicated in each box. Darker red plots represent higher marker frequency.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/2da498c8490ca769751d7991.png"},{"id":55634228,"identity":"60ccc757-5591-4ce8-85e5-4dad70b31b61","added_by":"auto","created_at":"2024-04-30 20:09:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":757197,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of uEV markers between INS at different clinical stages and pediatric controls. (\u003cstrong\u003eA\u003c/strong\u003e) Boxplots showing expression of a cluster of immune, platelets, and adhesion/progenitor cell markers in CTRL, and active or inactive INS. *\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e, \u003cem\u003e**\u0026lt;0.01\u003c/em\u003e, and \u003cem\u003e***\u0026lt;0.001\u003c/em\u003evs. CTRL; \u003cem\u003e#P\u0026lt;0.05, ##\u0026lt;0.01, ###\u0026lt;0.001\u003c/em\u003e vs. active INS. Kruskal-Wallis non-parametric with Dunn’s post-test. (\u003cstrong\u003eB\u003c/strong\u003e) ROC curve analysis of the performance of the CD41b, CD29, and CD105 markers, in separating the active INS group from CTRL.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/d940a999ad5bdc9c243b41f2.png"},{"id":55634822,"identity":"8684961d-52f7-4cfd-8446-93c2e123db45","added_by":"auto","created_at":"2024-04-30 20:17:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":640622,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of uEV markers discriminating steroid-sensitive and steroid-resistant forms of INS. (\u003cstrong\u003eA\u003c/strong\u003e) Boxplot showing the expression of CD11c, CD209, CD1c, CD19, CD4, CD8, CD31, CD44, CD146, and ROR1 that discriminates between SRNS and SSNS Rel patients. *\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e, **\u003cem\u003eP\u0026lt;0.01\u003c/em\u003e, and ***\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e vs. SSNS Rel. Non-parametric Mann-Whitney t-test. (\u003cstrong\u003eB\u003c/strong\u003e) Forest plot of the odds ratio (OR) and its 95% confidence interval for the risk associated with steroid resistance in INS children (OR \u0026gt;1, positive association; OR \u0026lt;1, negative association). \u003cem\u003eP\u0026lt;0.05 \u003c/em\u003eis highlighted in red. (\u003cstrong\u003eC\u003c/strong\u003e) ROC curve analysis of the diagnostic performance of the urinary marker combination (CD19-CD44-CD8, red line) and the single marker (CD146, green line). The performance of the proteinuria in separating patients based on steroid sensitivity was also reported (blue line).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/b4f77a013f0345351ccfbbfa.png"},{"id":55634223,"identity":"8aea5110-630d-4ac7-a63c-d3d3034b82f1","added_by":"auto","created_at":"2024-04-30 20:09:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2154394,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analyses of urine and serum-derived markers in the same INS patient. (a, b) NTA characterization of serum EVs (sEVs) from CTRL and INS patients at different clinical times and separated by gender. Results of sEVs concentration (\u003cstrong\u003eA\u003c/strong\u003e) and size (\u003cstrong\u003eB\u003c/strong\u003e) distribution are reported; each dot plot shows the mean ± SD \u003cem\u003e*P\u0026lt;0.05\u003c/em\u003e, **\u003cem\u003eP\u0026lt;0.01\u003c/em\u003e, \u003cem\u003e***P\u0026lt;0.001\u003c/em\u003e vs. CTRL; #\u003cem\u003eP\u0026lt;0.05\u003c/em\u003evs. SSNS Rem. One-way ANOVA with Tukey’s post-test. (\u003cstrong\u003eC\u003c/strong\u003e) Flow cytometry characterization of tetraspanins in EVs from INS patients at different clinical time points, expressed as MFI (CTRL, n=11; active INS, n= 54; inactive INS, n= 29) **\u003cem\u003eP\u0026lt;0.01 \u003c/em\u003evs. CTRL; #\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e vs. active INS. Kruskal-Wallis non-parametric with Dunn’s post-test. (\u003cstrong\u003eD\u003c/strong\u003e) Correlation matrix of surface proteins in uEV (Y-axis) and sEVs (X-axis) from the same patient (CTRL, n=7, and INS group n=55). R=Pearson correlation coefficient. Negative correlations (red color) and positive correlations (blue color) were highlighted in bold where statistically significant (\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/c1fe548d6952fdfb5f7b8eb2.png"},{"id":68206598,"identity":"ab2d7637-e072-4e60-9110-6d1c26ede2d8","added_by":"auto","created_at":"2024-11-04 16:32:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14163856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/1482b6bc-8122-447a-ad7b-7e70f4a6c333.pdf"},{"id":55634229,"identity":"66bf7fbc-af54-4af7-9d69-34b1c7e407b2","added_by":"auto","created_at":"2024-04-30 20:09:55","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":670548,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4283782/v1/da7ef8983b28d09468f8f47f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling a biofluid-derived extracellular vesicle surface signature to differentiate pediatric Idiopathic Nephrotic Syndrome clinical subgroups","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic Nephrotic Syndrome (INS) is the most common form of glomerular disease in childhood, characterized by massive proteinuria, hypoalbuminemia, hyperlipidemia, and tissue edema \u003csup\u003e1\u003c/sup\u003e. The biological mechanisms leading to INS are poorly defined, but an immunological dysfunction has been advocated \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe current INS therapy is based on oral corticosteroids, which leads to complete remission in up to 80% of children, classified as steroid-sensitive (SSNS). However, SSNS children experience multiple relapses or even steroid dependence for which long-term immunosuppressive steroid-sparing agents are indicated \u003csup\u003e3\u003c/sup\u003e. Patients unresponsive to steroids (SRNS) show the most severe form and the need for kidney replacement therapy in 50% if remission is not achieved \u003csup\u003e4\u003c/sup\u003e. Both SSNS and SRNS can experience multiple adverse effects secondary to the immunosuppressive treatment \u003csup\u003e5,6\u003c/sup\u003e. There is therefore a major medical need to find early biomarkers to allow for a more precise selection and duration of the treatment and to characterize the INS subgroups for clinical studies.\u003c/p\u003e \u003cp\u003eExtracellular vesicles (EVs) are nanosized particles naturally released by almost all cell types \u003csup\u003e7,8\u003c/sup\u003e and are easily found in many body fluids. EVs can carry selective surface markers inherited from their parent cells \u003csup\u003e9\u003c/sup\u003e, mirroring the functional state of the originating tissue. EVs contain lipids, proteins, and different forms of nucleic acids that are actively released from the cells from which they are derived \u003csup\u003e10\u003c/sup\u003e. Extensive investigation has been conducted on the wide array of molecules that can be enclosed within EVs, owing to their substantial relevance as biomarkers for various diseases \u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUrinary EVs (uEVs) originate mainly from the kidney and urinary tract cells \u003csup\u003e12\u003c/sup\u003e and can be a source of important urinary biomarkers reflecting the molecular processes activated by kidney diseases \u003csup\u003e13\u003c/sup\u003e. uEVs have been used for an early diagnosis of chronic kidney disease (CKD) \u003csup\u003e14\u003c/sup\u003e, polycystic kidney disease \u003csup\u003e15\u003c/sup\u003e, active glomerulonephritis \u003csup\u003e16\u003c/sup\u003e, and tubulopathies \u003csup\u003e17\u003c/sup\u003e. Moreover, uEVs can be secreted by kidney-resident immune cells, acting as biomarkers of immune activation and tissue remodelling \u003csup\u003e18\u003c/sup\u003e. When the glomerular and tubular basement membranes are disrupted, uEVs may also derive from the bloodstream and represent other body compartments \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aimed to identify the expression signature of uEVs reflecting the different forms of INS in childhood and their possible response to the treatment before the start of therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Recruitment Strategy and clinical data\u003c/h2\u003e \u003cp\u003eChildren with INS (first episode below 18 years old) were enrolled at the Pediatric Nephrology, Dialysis and Transplant Unit (Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico of Milano) in the period January 2022 to February 2023. A control group of age-matched children, with no kidney-related or immunological disease, was included in the study (CTRL). This study was conducted according to the principles expressed in the Declaration of Helsinki. Approve to the study was obtained by the IRCCS Ca\u0026rsquo; Granda Institutional Review Board (ID 2633, INSiDe protocol). An informed consensus was obtained for all the participants enrolled in the study. Patients were treated with standard therapy with oral prednisone and classified according to the international guidelines as SSNS or SRNS \u003csup\u003e3,20\u003c/sup\u003e. Demographic data, current therapies, and responses to ongoing therapy were collected. Routine clinical and biochemical parameters were measured according to the clinical practice. Proteinuria was defined as urine protein/creatinine ratio (uPr/uCr)\u0026thinsp;\u0026ge;\u0026thinsp;0.2 mg/mg (mild proteinuria, 0.21\u0026ndash;1.99 mg/mg and nephrotic range proteinuria, \u0026gt;\u0026thinsp;2 mg/mg). eGFR was calculated using the modified Schwartz formula \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Sample Collection\u003c/h2\u003e \u003cp\u003eBlood and urine were collected from children with INS and an age-matched control group (CTRL). Urine samples were processed within 6 hours (h) of collection according to established protocols \u003csup\u003e21,22\u003c/sup\u003e. Serum was obtained by activating the coagulation cascade from peripheral whole blood, followed by centrifugation at 3000 g for 20 min to remove corpuscular components. Aliquots of serum and urine were stored at -80\u0026deg;C until use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUrine and serum EVs characterization\u003c/h2\u003e \u003cp\u003eUnpurified extracellular vesicle (EV) from urine and serum samples were used. Dedicated urine samples underwent ultracentrifugation at 100.000 g for 2 h at 4\u0026deg;C for EV isolation (Beckman Coulter, OPTIMA XPN-90 Ultracentrifuge, Rotor Type 70-Ti, Brea, CA). EVs were then resuspended in PBS (Sigma-Aldrich) and freshly used or in 1% dimethyl sulfoxide (DMSO) (Sigma-Aldrich) and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Nanoparticle tracking analysis (NTA) was conducted on unpurified EVs using the Nanosight NS300 and analyzed as previously reported [24]. uCr was used as a normalization variable for particles number quantification in urines from both healthy subjects and patients with INS. Characterization involved transmission electron microscopy, super-Resolution Microscopy, and exoview analysis. Detailed procedures are in the Supplementary material (Supplementary technical method description).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCytofluorimetric analysis of EVs\u003c/h2\u003e \u003cp\u003eUnpurified uEVs (100 \u0026micro;L) and sEVs (1x 10\u003csup\u003e10\u003c/sup\u003e) were analyzed using a MACSPlex human Exosome kit (Miltenyi Biotec) according to manufacturer\u0026rsquo;s instructions. For uEVs, surface marker median fluorescence intensity (MFI) was normalized against uCr to account for inter-patient EV variations based on the daily water intake. Flow cytometry was conducted on Cytoflex using CytExpert Software (Beckman Coulter, Brea, CA, USA), and data were analyzed with Flowjo software (Tree Star, Inc. Ashland, OR, USA). Raw data were reported in the Supplementary material (Supplementary cytofluorimetric data).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eAnalysis was performed using RStudio (R v4.0.3) or Prism with GraphPad v9.0 (GraphPad Software, USA). Normalized cytofluorimetric signals were log-transformed for urine and serum markers. Transformed markers were used both for multivariate and univariate analysis.\u003c/p\u003e \u003cp\u003ePrincipal components analysis (PCA) and clustering were done using FactoMineR (v2.4) and Complex Heatmap (v2.6.2), respectively. Two-sided Student\u0026rsquo;s or Mann-Whitney tests for pairwise comparisons and one-way ANOVA with Tukey\u0026rsquo;s post hoc tests or Kruskal-Wallis\u0026rsquo;s test with Dunn\u0026rsquo;s post hoc for multiple comparisons were selected based on data distribution.\u003c/p\u003e \u003cp\u003eCorrelation with biochemical variables was assessed using Pearson or Spearman coefficients.\u003c/p\u003e \u003cp\u003eMarker frequency among patient groups was determined with a\u0026thinsp;\u0026lt;\u0026thinsp;50% or \u0026gt;\u0026thinsp;50% threshold as previously reported \u003csup\u003e23\u003c/sup\u003e. The classification performance of both single markers and their combinations was evaluated using combiROC (v0.2.3) (sensitivity\u0026thinsp;\u0026ge;\u0026thinsp;40, specificity\u0026thinsp;\u0026ge;\u0026thinsp;70, AUC).\u003c/p\u003e \u003cp\u003eOdds ratios (ORs) were calculated with univariate logistic regression (OR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates increased likelihood of association with SRNS appearance, OR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates decreased association). Significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Variables\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the clinical and biochemical characteristics of the INS study cohort (n\u0026thinsp;=\u0026thinsp;105, 58% males) and healthy controls (CTRL) (n\u0026thinsp;=\u0026thinsp;19, 94% males). The study cohort includes SSNS (n\u0026thinsp;=\u0026thinsp;80) and SRNS (n\u0026thinsp;=\u0026thinsp;25).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the enrolled patients at the time of collection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy children CTRL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive INS disease (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSSNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInactive INS disease (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSSNS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSRNS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnset (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRel (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRem (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRem (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic and clinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18, (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43, (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11, (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24, (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8, (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18, (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13, (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5, (62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28, (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6, (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13, (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9, (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16, (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13, (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3, (37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yr), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9, (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (12-18.5) \u003cb\u003ea, b, c\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11, (8-14.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11 (6-13.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15 (8.25\u0026ndash;17.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at onset (yr), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4, (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) \u003cb\u003ec, d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5, (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (2.37\u0026ndash;7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7 (5.25\u0026ndash;12.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrugs treatment at the time of collection, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppressants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25, (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14, (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11, (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30, (88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24, (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6, (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4, (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4, (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0, (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19, (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42, (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17, (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23, (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2, (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4, (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, (7.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein-to-creatinine ratio, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85, (1.86\u0026ndash;8.7) \u003cb\u003ef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5 (4.96\u0026ndash;9.27) \u003cb\u003ed, e\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7 (1.60\u0026ndash;8.52) \u003cb\u003ed, e\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.68 (0.96\u0026ndash;8.78) \u003cb\u003ed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15, (0.12\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14 (0.11\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31 (0.19\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine creatinine (g/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.42\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99, (0.59\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.47\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (0.63\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.57\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19, (0.8\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05 (0.57\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.49 (1.02\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.29\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45, (0.31\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31 (0.26\u0026ndash;0.50) \u003cb\u003ee\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.32\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84 (0.56\u0026ndash;1.06) \u003cb\u003ea, b, c\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52, (0.46\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51 (0.43\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67 (0.47\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (86\u0026ndash;132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118, (91\u0026ndash;145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (101.5-163.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (106.8-149.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74 (58.50\u0026ndash;106) \u003cb\u003eb, c, d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e115, (93\u0026ndash;126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e117 (96.45\u0026ndash;126.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e99 (78-119.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunoglobulins (mg/dL), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383, (148\u0026ndash;618) \u003cb\u003ef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (93\u0026ndash;230) \u003cb\u003ec, d, e\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e487 (365.5-719.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e571 (186.5\u0026ndash;679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e695, (500.3\u0026ndash;919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e695 (465-849.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e763 (644.8\u0026ndash;1128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127, (96.3\u0026ndash;184) \u003cb\u003ef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (100-201.5) \u003cb\u003ed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127 (72\u0026ndash;163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162 (114.8\u0026ndash;194) \u003cb\u003ed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66, (38\u0026ndash;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57 (37.5\u0026ndash;112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e82 (58.25\u0026ndash;118.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92, (72.3-138.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (57-168.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (59\u0026ndash;129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (69-192.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98, (46\u0026ndash;151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71 (31.5\u0026ndash;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143 (95.50\u0026ndash;234)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eImmunosuppressant drugs: prednisone, mycophenolate, tacrolimus, Others: ramipril, NT\u0026thinsp;=\u0026thinsp;not treated. IQR\u0026thinsp;=\u0026thinsp;interquartile range. N/A\u0026thinsp;=\u0026thinsp;not applicable. Age: SRNS vs. \u003cb\u003e(a)\u003c/b\u003e CTRL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), \u003cb\u003e(b)\u003c/b\u003e patients with SSNS onset (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cb\u003e(c)\u003c/b\u003e SSNS Rel (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Age at the onset: SRNS vs. \u003cb\u003e(c)\u003c/b\u003e SSNS Rel (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cb\u003e(d)\u003c/b\u003e SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Protein-to-creatinine ratio: SSNS Onset vs. \u003cb\u003e(d)\u003c/b\u003e SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cb\u003e(e)\u003c/b\u003e with SRNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); SNSS Rel vs. \u003cb\u003e(d)\u003c/b\u003e SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cb\u003e(e)\u003c/b\u003e SRNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); SRNS vs. \u003cb\u003e(d)\u003c/b\u003e SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Active INS vs. \u003cb\u003e(f)\u003c/b\u003e Inactive INS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Serum Creatinine: SSNS Onset vs. \u003cb\u003e(e)\u003c/b\u003e SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); SRNS vs. \u003cb\u003e(a)\u003c/b\u003e CTRL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), \u003cb\u003e(b)\u003c/b\u003e SSNS onset (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cb\u003e(c)\u003c/b\u003e SSNS Rel (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). eGFR: SRNS vs. \u003cb\u003e(b)\u003c/b\u003e SSNS onset (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cb\u003e(c)\u003c/b\u003e SSNS Rel (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cb\u003e(d)\u003c/b\u003e SNSS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). IgG: SNSS Onset vs. \u003cb\u003e(c)\u003c/b\u003e SSNS Rel (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), \u003cb\u003e(d)\u003c/b\u003e vs. SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cb\u003e(e)\u003c/b\u003e SRNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Active INS vs. \u003cb\u003e(f)\u003c/b\u003e Inactive INS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). IgM: SSNS Onset and SRNS vs. \u003cb\u003e(d)\u003c/b\u003e SSNS Rem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Active INS vs. \u003cb\u003e(f)\u003c/b\u003e Inactive INS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe INS population was then subdivided into five groups: Group 1 SSNS patients at the onset of the disease (n\u0026thinsp;=\u0026thinsp;17, SSNS Onset); -Group 2 SSNS at relapse (SSNS Rel, n\u0026thinsp;=\u0026thinsp;37); -Group 3 SRNS patients with persistent proteinuria (above 0.5 mg/mg) (SRNS, n\u0026thinsp;=\u0026thinsp;17); -Group 4 SSNS in remission (SSNS Rem, n\u0026thinsp;=\u0026thinsp;26); -Group 5 SRNS patients who achieved complete response to second-line treatments (SRNS Rem, n\u0026thinsp;=\u0026thinsp;8). A control group of age-matched children, with no kidney-related or immunological disease, was included in the study (CTRL). In selected experiments, children in groups 1, 2, and 3 were selected as patients with active INS (n\u0026thinsp;=\u0026thinsp;71), while children in groups 4 and 5 were in the remission phase of the disease (inactive INS, n\u0026thinsp;=\u0026thinsp;34). In our cohort, the age was homogenously distributed with a median of 8.5 years (IQR: 4\u0026ndash;13) in SSNS and 6 years (IQR: 2\u0026ndash;11) in CTRL, while SRNS patients had a higher median age of 15 years (IQR: 10\u0026ndash;18). eGFR was reduced (74 mL/min) in SRNS compared to SSNS subgroups (138 and 122 mL/min for SSNS Onset and SSNS Rel, respectively). Serum IgG and IgM levels varied according to disease phase \u003csup\u003e24\u003c/sup\u003e, as expected.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQualitative and quantitative evaluation of urine EVs in INS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEVs isolated from the urine of INS children (uEVs) exhibited a heterogeneous size and preserved membranes, confirmed by TEM analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). ​Super-resolution microscopy analysis revealed tetraspanin distribution on single vesicles in both CTRL and INS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Quantitative analysis showed significantly higher CD63\u0026thinsp;+\u0026thinsp;and CD81\u0026thinsp;+\u0026thinsp;uEVs in active INS (62,500\u0026thinsp;\u0026plusmn;\u0026thinsp;23,300 and 65,700\u0026thinsp;\u0026plusmn;\u0026thinsp;21,000 respectively, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e and \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) compared to CTRL (22,100\u0026thinsp;\u0026plusmn;\u0026thinsp;10,200, and 22,000\u0026thinsp;\u0026plusmn;\u0026thinsp;9,600) and inactive INS (only for CD81, 28,800\u0026thinsp;\u0026plusmn;\u0026thinsp;10,500, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Capture separation revealed different co-expression patterns among tetraspanins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), particularly significant for CD9/CD63 and CD81/CD63 in the active phase compared to CTRL (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e and \u0026lt;\u0026thinsp;\u003cem\u003e0.01\u003c/em\u003e, respectively). EVs co-expressing CD63/CD81 also showed a different distribution between the active and inactive INS stages (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Triple-positive uEVs were less abundant but significantly increased in the active phase when captured with CD9 (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e vs CTRL and \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01 vs\u003c/em\u003e inactive INS) and CD81 antibodies (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e vs both groups).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelations between uEVs/mL in INS children\u0026rsquo;s urine and kidney function parameters (uCR, uPr/Cr, eGFR) were investigated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Spearman\u0026rsquo;s analysis revealed a significant positive correlation between uEV numbers and uCr levels (R\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e), as well as with the uPr/uCr (R\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) ratio. uEV dimensions negatively correlated only with the uPr/uCr levels (R=-0.27, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These associations persisted when INS patients were separated from controls (Additional File 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). NTA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) showed increased uEV numbers (uEVs/uCr) in SSNS Onset compared to CTRL (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e), and the other groups and clinical time-points (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). No gender-based differences were observed in uEV numbers and size in INS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). EV size distribution decreased only between SRNS and SSNS in remission (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, SSNS Rem vs SRNS Rem) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003euEV surface marker characterization in different INS groups.\u003c/b\u003e \u003c/p\u003e \u003cp\u003euEV surface marker profile was evaluated by flow cytometry in seventy-four INS patients. CD9-CD63-CD81 EV expression positively correlated with uPr/uCr (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), with CD9 most significantly associated with the active state. Tetraspanin levels were higher in active INS compared to controls and inactive INS (CD9 and CD63 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e vs CTRL and \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs\u003c/em\u003e inactive INS; CD81 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs\u003c/em\u003e inactive INS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGlobal EV-surface antigens distribution in each INS, analyzed through PCA, distinctly separated SSNS in relapse from onset and the SRNS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Unsupervised hierarchical clustering confirmed the clustering of INS patients in the active phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Marker frequency analysis revealed exclusive markers in INS children (CD25, CD20, CD11c, CD2, CD49e, CD62p, and CD42a) with higher frequencies during the active phase than during remission (Frequency\u0026thinsp;\u0026gt;\u0026thinsp;50%). SSNS showed prominent immune markers (CD25, CD11c, CD20, CD1c, CD40) and adhesion molecules (CD49e), while SRNS exhibited higher levels of adaptive immune-related proteins (CD4, CD8, CD19), monocyte markers (CD11c, CD2, CD1c), and adhesion molecules (CD49e, CD146) compared to the SSNS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of uEV-associated markers to distinguish the active phase of INS disease.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNineteen core EV-surface markers were identified as potential discriminants between active and inactive INS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), with eight effectively distinguishing the active phase from CTRL (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Among them, the three markers that achieved the best discrimination were CD41b, CD105, and CD29 with an AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These core markers, excluding CD24, CD1c, CD11c, CD25, and CD40, and with the addition of CD19 and CD69 molecules, exhibited a significant positive correlation with proteinuria levels in INS children (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting an association between kidney damage and uEVs of distinct cellular origin. Seven markers effectively separated SSNS at the onset from CTRL (Supplementary material; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), with CD41b achieving the highest performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9). The same cluster of markers except for CD20 and CD42a, but including CD326, CD9, CD133, CD63, CD81, and CD24 differentiated between SSNS Rel and SSNS Rem groups (Supplementary material; Table S2), with CD29 showing the best AUC. When the SRNS group was analyzed, almost half of the markers were differentially expressed between patients with persistent proteinuria or in remission after second-line immunosuppressive treatments (best AUC for CD9, CD19, and CD146) (Supplementary material, Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUrine-EV biomarkers and their correlation to pathological proteinuria. \u003cem\u003eP value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellular Origin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eImmune cell compartment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHLA-DR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eEndothelial/platelet activation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD62p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD41b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD42a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD105\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD142\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAdhesion cell activation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD326\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCSP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eStem/Progenitor cell activation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCD133\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSEA-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eROR1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of a candidate uEV panel to discriminate patient\u0026rsquo;s sensitivity to the steroid therapy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003euEVs from SSNS Rel and SRNS patients were compared to identify biomarkers that differentiated significantly the two groups. Markers encompassing both innate (CD11c, CD209, and CD1c) and adaptive immune responses (CD19, CD4, and CD8), along with those involved in angiogenesis/adhesion and stemness processes (CD31, CD44, CD146, and ROR1) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). CD146 emerged as a significant predictor for SRNS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). However, the best separation between SRNS and SSNS Rel groups was achieved by the combination of CD19-CD44-CD8 (AUC\u0026thinsp;=\u0026thinsp;0.87) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of individual, combined uEV markers and proteinuria in steroid sensitivity classification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD19-CD44-CD8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD146\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euPr/uCr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGeneration of INS expression signature using the serum and urinary EVs from the same patient.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEVs enriched in the serum (sEVs) were also characterized in eighty-three INS patients. NTA analysis revealed significantly higher sEV numbers in SSNS at onset (7.87e11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13e11 part/mL, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) and relapse compared to CTRL (6.50e11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92e11 part/mL vs 3.79e11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38e11, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). sEVs were also increased in SRNS with persistent proteinuria compared to CTRL (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and SSNS Rem (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). No differences in sEV size were observed among groups or after gender-related INS separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). CD9 expression in sEVs decreased during the active phase compared to CTRL and inactive INS (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e and \u003cem\u003e\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Three markers on serum EV surfaces were identified as potential discriminants for active INS (not shown), with lower discriminating power compared to urine in distinguishing INS patients from controls. The combination of serum and urine markers from the same patient was able to reach a statistical correlation as observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, with CD3 and CD29 exhibiting significant positive/negative relations in both matrices (R\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003e0.047\u003c/em\u003e and R=-0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003e0.039\u003c/em\u003e, Pearson correlation) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Likewise, the combination of sCD146 and uCD29-uCD41b separated active INS patients from healthy children with higher sensitivity (SE\u0026thinsp;~\u0026thinsp;1) compared to single markers; the combination of CD41b in serum and urine distinguished SSNS at onset from CTRL, and in SSNS, uCD326, and sCD8 effectively differentiated disease activity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of uEV and sEV markers in pediatric INS between the different analyzed groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003es-uEVs Markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esCD146-uCD29-uCD41b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eActive vs CTRL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esCD146\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euCD29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euCD41b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esCD41b-uCD41b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esCD41b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOnset vs CTRL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euCD41b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esCD8-uCD326\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSSNS Rel vs SSNS Rem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esCD8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003euCD326\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we analyzed the surface antigen profile of urine and serum EVs in a cohort of pediatric INS patients, searching for new biomarkers for the accurate subclassification of INS sub-cohorts for clinical studies. Through the combination of a standardized surface proteomic analysis with a bioinformatic approach, a urine EV-based signature was generated, discriminating different forms of childhood INS compared with a cohort of pediatric controls. Urine-EV signature was mainly characterized by markers of endothelial/platelet and immune stimulation during proteinuria events. Conversely, the combination of serum and urine EVs was able to improve the separation between the different groups of childhood INS.\u003c/p\u003e \u003cp\u003eWe found that at the onset of the disease, patients present more EVs in their urine compared to healthy children. This was in line with previous studies showing the association of EV abundance with different pathological conditions related to the kidney \u003csup\u003e25\u003c/sup\u003e. EV concentration can be influenced by age \u003csup\u003e26\u003c/sup\u003e. In our patient\u0026rsquo;s cohort, the age was uniformly distributed among the different phases of the disease and comparable to the control group. The only form showing a different age distribution was the SRNS group classically characterized by a higher age of appearance of the disease \u003csup\u003e27\u003c/sup\u003e. However, no differences were highlighted in their EV number in respect to the SSNS group. The number of uEVs also showed a positive correlation with proteinuria levels in the different INS groups.\u003c/p\u003e \u003cp\u003eThe classical tetraspanin members were detectable in the urine EVs, with the highest expression observed from CD9, followed by CD81 and CD63 in proteinuric INS. When a co-expression pattern was investigated, double-positive CD63/CD81 vesicles were identified as the most enriched EVs in the INS active phase, with a significant reduction during the inactive phase. Interestingly, all the tetraspanins showed a stronger association with proteinuria level, where the highest correlation was achieved for CD9, displaying a direct relation between tetraspanins and kidney dysfunction.\u003c/p\u003e \u003cp\u003eWe here, for the first time, characterized uEVs in INS patients, tracking their cellular sources using a standardized flow cytometric assay able to simultaneously analyze 37 different markers. A characteristic uEV protein electrophoresis profile was previously found able to discriminate INS from other non-glomerular kidney diseases \u003csup\u003e28\u003c/sup\u003e. Previous studies by Burello et al. used the same cytofluorimetric technology to investigate the surface antigen profile of blood and urine EVs in ischemic brain injury \u003csup\u003e29\u003c/sup\u003e and rejection episodes associated with heart and kidney transplantation \u003csup\u003e30,31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe uEV concentration can be dependent on the excretion/fusion EV rate and the overall urine concentration. Blijdorp et al. demonstrated that uEV concentration highly correlates with urine creatinine, potentially replacing the need for uEV quantification to normalize spot urines \u003csup\u003e32\u003c/sup\u003e. Indeed, urine creatinine levels are commonly used to normalize the excretion rate of different urinary analytes \u003csup\u003e33,34\u003c/sup\u003e. In our study, a positive correlation between the concentration of released EVs and urine creatinine was observed. Therefore, we employed the urinary creatinine measure to normalize the relative excretion rate of uEV proteins.\u003c/p\u003e \u003cp\u003eWe found that INS patients in the active phase were separated from healthy children based on an exclusive surface signature of 8 markers with a strong overrepresentation of adhesion/endothelial activation markers. Three markers, CD41b (platelets origin), CD105 (endothelial marker), and CD29 (adhesion molecule), showed the best diagnostic score. The CD41b was the principal marker, separating treatment-na\u0026iuml;ve INS patients at the onset of the disease from controls, thus representing a promising diagnostic biomarker. Interestingly, thromboembolic events represent a possible complication of INS \u003csup\u003e35\u003c/sup\u003e. Moreover, serum endothelial and platelet microparticles were previously described to inversely correlate with kidney function recovery in transplanted patients \u003csup\u003e31,36\u003c/sup\u003e. The EV detected in our samples could potentially derive from the serum since EVs can pass through the membrane pores of the glomerular filtration barrier when kidney damage occurs \u003csup\u003e19\u003c/sup\u003e. Urine EVs can also derive directly from the kidney compartment, where alterations of the glomerular endothelium have been identified in patients with SSNS in relapse and correlate with poor clinical outcomes \u003csup\u003e37,38\u003c/sup\u003e. Concomitantly, in children with minimal change disease, activation of the integrin CD29/FAK axis was detected in damaged podocytes, suggesting that CD29-enriched EVs could originate from this population \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen the EV profile was performed in the same patient using both serum and urine as sources, a better separation of the different stages of INS was achieved. The combination of serum-derived CD146 and urinary-derived CD29 and CD41b reached the best score in separating INS patients in the active phase from healthy children compared to the single markers. While the combination of a unique marker in serum and urine, the CD41b, better distinguished SSNS patients at the onset from CTRL. This approach was also proposed for the discrimination of different pathological states in other diseases \u003csup\u003e31,40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA signature of 30 miRNAs was previously described to distinguish patients with active proteinuria from patients in clinical remission \u003csup\u003e41\u003c/sup\u003e and correlated with the disease gravity \u003csup\u003e41\u003c/sup\u003e. Similarly, in our experiments, the surface protein markers, CD29, CD41b, and HLA-DR, better correlated with proteinuria levels in our patients\u0026rsquo; cohort, presenting a disease signature that might potentially anticipate the clinical course of the disease. When each INS subgroup was analyzed, we identified the best classification marker to distinguish the different disease stages in both SRNS and SSNS patients. We found that CD29 was the marker that better discriminates relapse episodes from remission in SSNS patients with an AUC of 0.913, while the SRNS patients were sorted based on the expression of the tetraspanin molecule CD9 (AUC\u0026thinsp;=\u0026thinsp;1). The expression of integrins was already implicated in the pathogenesis of multiple kidney diseases \u003csup\u003e42\u003c/sup\u003e. Our data corroborates with the recent finding of the group of J. Kennedy, which demonstrated that urinary podocyte-derived large EVs were able to distinguish between relapse and remission phases in children with INS \u003csup\u003e43\u003c/sup\u003e. Additionally, the analysis of biofluids from the same patients in our cohort was able to generate a multi-biomarker combination (CD8 sEVs and CD326 uEVs), which could predict the rate of remission and relapse in SSNS.\u003c/p\u003e \u003cp\u003eWe also investigated the role of uEVs surface proteomes in stratifying INS-affected subjects according to steroid sensitivity. Prior investigations identified WT-1 as a marker of urinary exosomes in INS. Despite that, WT-1 expression was ineffective in the prediction of steroid responsiveness in these children \u003csup\u003e44\u003c/sup\u003e. In our study, uEVs were able to separate patients with SSNS from those affected by the SRNS form. Among them, the endothelial molecule CD146 better predicts SRNS than the urine Albumin: creatinine ratio, with an AUC equal to 0.84. Interestingly, plasma CD146 levels were shown to be progressively increased in early-stage diabetic nephropathy (DN), functioning as an optimal marker in the discrimination of DN severity \u003csup\u003e45\u003c/sup\u003e. However, SSNS and SRNS patients were again better separated by the combination of three markers, CD19, CD8, and CD44, which strongly classified patients based on steroid response. The CD19\u0026thinsp;+\u0026thinsp;B cell levels were recently demonstrated to predict steroid responses in SRNS patients with high sensitivity and acceptable accuracy \u003csup\u003e46\u003c/sup\u003e. Our data were also in line with a previous study showing the correlation between CD44 expression in kidney biopsies and the higher prevalence of SRNS as well as a negative kidney outcome \u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSome limitations should be acknowledged. Firstly, the results of our study were limited by the incidence of INS which is classified as a rare disease. Moreover, a longitudinal study on the same patient will be more instrumental in predicting the progression of the disease. However, this study sets the basis to apply the biofluid EV modeling to monitor the ongoing immune-related kidney damage after INS appearance.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed that urinary and serum EV-surface profiles may efficiently separate different forms of childhood INS at different stages of the disease. The urine EVs phenotype mirrored the ongoing immune and endothelial/platelet activation of the disease's active phase. Thus, the EV-surface proteins by reflecting disease-specific features may function as innovative biomarkers in this disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge for their support: IMPACTsim S.p.A, ABN (Fondazione bambino nefropatico ONLUS), and Fondazione Nuova Speranza S.p.A ONLUS. The authors wish to thank the Cytofluorimetric facility of the Instituto Nazionale of Genetica Molecolare (INGM) for the cytofluorimetric analysis assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, G.C., W.M., F.C. Data curation, G.C., and A.G. Investigation, G.C. Writing\u0026mdash;original draft, G.C. Methodology, G.C., A.G., S.B., L.B., S.T., C.T., T.N., I.P., and A.B. Resources, I.P., and A.B. Formal analysis, G.C., A.G., S.B., S.T., and F.Ca. Supervision, and Funding acquisition, F.C., and G.M. S.B., R.G., B.B., W.M., G.M., and F.C. participated in writing and editing the manuscript. All authors contributed to the article and approved the submitted version. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinistero dell\u0026rsquo;Istruzione, dell\u0026rsquo;Universit\u0026agrave; e della Ricerca (2022B9WC3F) and IMPACTsim S.p.A funding support (Grant P-0038).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e \u003cstrong\u003eavailability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in the published article [and its supplementary information files].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the IRCCS Ca\u0026rsquo; Granda Institutional Review Board (ID 2633, INSiDe protocol). An informed consensus has been signed by the parent and/or legal guardian of all the children enrolled in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed the journal\u0026rsquo;s policy concerning competing interests. The authors affirm that they have no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEddy, A. A. \u0026amp; Symons, J. M. Nephrotic syndrome in childhood. 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Clin Kidney J 15, 545\u0026ndash;552, DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ckj/sfab215\u003c/span\u003e\u003cspan address=\"10.1093/ckj/sfab215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic Nephrotic Syndrome, extracellular vesicles, protein biomarkers, steroid resistance","lastPublishedDoi":"10.21203/rs.3.rs-4283782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4283782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdiopathic Nephrotic Syndrome (INS) is a common childhood glomerular disease requiring intense immunosuppressive drug treatments. Prediction of treatment response and the occurrence of relapses remains challenging. Biofluid-derived extracellular vesicles (EVs) may serve as novel liquid biopsies for INS classification and monitoring. Our cohort was composed of 106 INS children at different clinical time points (onset, relapse, and persistent proteinuria, remission, respectively), and 19 healthy controls. The expression of 37 surface EV surface markers was evaluated by flow cytometry in serum (n=83) and urine (n=74) from INS children (mean age=10.1, 58% males) at different time points. Urine EVs (n=7) and serum EVs (n=11) from age-matched healthy children (mean age=7.8, 94% males) were also analyzed. Tetraspanin expression in urine EVs was enhanced during active disease phase in respect to the remission group and positively correlates with proteinuria levels. Unsupervised clustering analysis identified an INS signature of 8 markers related to immunity and angiogenesis/adhesion processes. The CD41b, CD29, and CD105 showed the best diagnostic scores separating the INS active phase from the healthy condition. Interestingly, combining urinary and serum EV markers from the same patient improved the precision of clinical staging separation. Three urinary biomarkers (CD19, CD44, and CD8) were able to classify INS based on steroid sensitivity.\u003cstrong\u003e \u003c/strong\u003eBiofluid EVs offer a non-invasive tool for INS clinical subclassification and “personalized” interventions.\u003c/p\u003e","manuscriptTitle":"Modeling a biofluid-derived extracellular vesicle surface signature to differentiate pediatric Idiopathic Nephrotic Syndrome clinical subgroups","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 20:09:50","doi":"10.21203/rs.3.rs-4283782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-17T03:55:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-16T11:29:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T23:29:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T12:40:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333349661123025386733156410895783781082","date":"2024-05-29T08:33:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284140794089314000035233468653314074465","date":"2024-05-28T04:49:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62333064435317115748164389684638947752","date":"2024-05-26T03:49:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-25T20:29:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-25T20:23:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-28T06:08:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-25T04:15:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-17T19:36:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da63e9f6-3ec0-4784-888d-9d71a4895cec","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31267682,"name":"Health sciences/Nephrology/Kidney diseases"},{"id":31267683,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":31267684,"name":"Biological sciences/Biological techniques/Proteomic analysis"},{"id":31267685,"name":"Health sciences/Medical research/Pre clinical studies"},{"id":31267686,"name":"Health sciences/Medical research/Paediatric research"}],"tags":[],"updatedAt":"2024-11-04T16:22:29+00:00","versionOfRecord":{"articleIdentity":"rs-4283782","link":"https://doi.org/10.1038/s41598-024-76727-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-28 16:05:00","publishedOnDateReadable":"October 28th, 2024"},"versionCreatedAt":"2024-04-30 20:09:50","video":"","vorDoi":"10.1038/s41598-024-76727-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-76727-w","workflowStages":[]},"version":"v1","identity":"rs-4283782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4283782","identity":"rs-4283782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}