Proteomic profile of mesothelial exosomes isolated from peritoneal dialysis effluent of children with focal segmental glomerulosclerosis

Peritoneal dialysis (PD) is the worldwide recognized preferred dialysis treatment for children affected by end-stage kidney disease (ESKD). However, due to the unphysiological composition of PD fluids, the peritoneal membrane (PM) of these patients may undergo structural and functional alterations, which may cause fibrosis. Several factors may accelerate this process and primary kidney disease may have a causative role. In particular, patients affected by steroid resistant primary focal segmental glomerulosclerosis, a rare glomerular disease leading to nephrotic syndrome and ESKD, seem more prone to develop peritoneal fibrosis. The mechanism causing this predisposition is still unrecognized. To better define this condition, we carried out, for the first time, a new comprehensive comparative proteomic mass spectrometry analysis of mesothelial exosomes from peritoneal dialysis effluent (PDE) of 6 pediatric patients with focal segmental glomerular sclerosis (FSGS) versus 6 patients affected by other primary renal diseases (No FSGS). Our omic study demonstrated that, despite the high overlap in the protein milieu between the two study groups, machine learning allowed to identify a core list of 40 proteins, with ANXA13 as most promising potential biomarker, to distinguish, in our patient population, peritoneal dialysis effluent exosomes of FSGS from No FSGS patients (with 100% accuracy). Additionally, the Weight Gene Co-expression Network Analysis algorithm identified 17 proteins, with PTP4A1 as the most statistically significant biomarker associated to PD vintage and decreased PM function. Altogether, our data suggest that mesothelial cells of FSGS patients are more prone to activate a pro-fibrotic machinery. The role of the proposed biomarkers in the PM pathology deserves further investigation. Our results need further investigations in a larger population to corroborate these findings and investigate a possible increased risk of PM loss of function or development of encapsulating peritoneal sclerosis in FSGS patients, thus to eventually carry out changes in PD treatment and management or implement new solutions.

www.nature.com/scientificreports/ Despite considerable overlapping of protein identity between the two clinical groups, multidimensional scaling analysis evidenced a clear discrimination of the two conditions (Supplemental Figure S2B).
A co-expression network was constructed using the weighted gene co-expression network analysis (WGCNA). WGCNA clusters proteins into modules of co-expression profile, considered to be in a functional relationship with each other 20 .
We used WGCNA to identify which module and protein expression profile was functionally associated with dialysis vintage, peritoneal equilibration test of glucose dialysate to initial dialysate concentration ratio (PET D/ D0 glucose) and with peritoneal equilibration test of creatinine dialysate-to-plasma concentration ratio (PET D/P creatinine) at PET, and FSGS or No FSGS samples.
This analysis revealed 28 modules encompassing proteins with similar co-expression profiles. To distinguish between modules, an arbitrary color was chosen for each module (Supplemental Table S1). The number of proteins included in each module ranged from 21 (white) to 273 (turquoise). The brown, purple, pink, tan and lightgrey modules showed closer relationships with the FSGS (r = 0.61), No FSGS (0.81), time of peritoneal dialysis (r = 0.61), PET D/D0 glucose (r = 0.81) and PET D/P creatinine (r = 0.81) respectively (Fig. 1A). No other statistical relationship was found with variables such as age, weight, height, body mass index and body surface area. The proteome profile of the 57 proteins significantly correlated (Spearman's correlation coefficient values > 0.7 and two sides p values ≤ 0.05. See detail in Table 1) to either condition is visualized in a heatmap diagram (Fig. 1B). In the heatmap, each row represents a protein and each column corresponds to a sample. Normalized Z-scores of protein abundance are depicted by a pseudocolor scale, with red, white and blue indicating positive, absence and negative correlation, respectively. The tree dendrogram displays the results of unsupervised hierarchical clustering analysis, placing similar Spearman's correlation coefficient values next to each other. Then, we applied the T-test to identify the proteins that best distinguish the type of disease.
A total of 40 proteins that maximized the discrimination between FSGS and No FSGS patients were highlighted ( Fig. 2A and Table 1). Their expression profile after Z-score normalization is reported in a heatmap diagram (Fig. 2B). In the heatmap, each row represents a protein and each column corresponds to a sample. Normalized Z-scores of protein abundance are depicted by a pseudocolor scale with red, white and blue indicating  Table 1). The correlation with each clinical trait is highlighted in red on the right of the heatmap. P27216 www.nature.com/scientificreports/ positive, equal and negative expression, respectively. The tree dendrogram displays the results of the unsupervised hierarchical clustering analysis, placing similar sample/proteome profile values next to each other. SVM learning and PLS-DA were then used to make a rank list of these proteins (Table 1 and Supplemental Table S1). Among these, Annexin A13 (ANXA13) resulted the most promising up-regulated potential biomarker to distinguish peritoneal dialysis effluent exosomes of FSGS patients as it displays the highest p value in the T-test, maximal discrimination power in SVM and PLSDA, respectively with first position in rank and maximal value of variable importance (VIP) score. Instead, Tissue inhibitor matrix metalloproteinase 1 (TIMP1) was one of the most significant down-regulated protein in FSGS patients.
On the other hand, out of the 40 proteins maximizing the discrimination between the two groups of patients, 11, 5 and 4 proteins were highly correlated respectively with dialysis vintage, D/D0 glucose and with D/P creatinine at PET ( Fig. 1B and Table 1). Among those related to D/D0 glucose and D/P creatinine values at PET, the expression of 4 proteins (PTP4A1, TXNDC12, GNAS and CASC4) was proportional to D/D0 glucose and inversely proportional to D/P creatinine, whereas 2 proteins (TRIP12 and LRRC17) had an opposite relationship (Table 1). In particular, protein tyrosine phosphatase type IVA 1 (PTP4A1) was the only one statistically related to dialysis vintage, D/D0 glucose and D/P creatinine at PET ( Table 1).
The high diversity of protein profile expression between FSGS and No FSGS could imply a different biochemical role of these proteins. To assess this, we performed Gene Ontology (GO) enrichment analysis. This analysis identified 118 significantly enriched gene signatures. Among these, 61, 49 and 8 were enriched in FSGS, No FSGS or in both, respectively (Supplemental Table S2). These signatures are visualized using a scatter plot (Supplemental Figure S3). Interestingly, exosomes isolated from FSGS patients were enriched in proteins associated to their pathology (251 proteins 34 ) and both groups were enriched in proteins already described in Exocarta (1873 proteins 35

PTP4A1 hyper-expression in TGF-beta-induced exosomes from PD effluent of patients with long dialysis vintage.
To evaluate the potential role of PTP4A1 in fibrosis, immortalized HPMC were treated with TGFβ, a well-known inducer of fibrosis. The expression of PTP4A1 was up-regulated, together with www.nature.com/scientificreports/ reduced expression of the epithelial marker E-cadherin and increased expression of VIME and α-SMA, mesenchymal markers (Fig. 4A, B).
To confirm the correlation between PTP4A1 expression and dialysis vintage, its content was measured by western blot in PD exosomes from an independent group of 20 patients with same range of dialysis vintage of the patients used in the discovery approach. As shown in Fig. 4C PTP4A1 was statistically more abundant (p < 0.05) in exosomes from patients with long dialysis vintage and high D/D0 glucose and low D/P creatinine values at PET, compared to patients with short dialysis vintage but low D/D0 glucose and high D/P creatinine values at PET respectively being: 1.2E+04 (9.2E+03-1.6E+03) and 4.0E+03 (2.5E+0.3-5.2E+0.3) Optical Density Unit (Fig. 4D). The AUC, its CI and p value were 0.89 (0.7-0.98) and p < 0.05, respectively. Finally, the precent sensitivity and specificity of the assay were 95 (69-98) (%), 60 (55-99) (%), respectively.

Discussion
Our proteomic study demonstrated, for the first time, that, despite the high overlap of the protein milieu between FSGS and No FSGS samples, the combined use of different analyses allowed a complete distinction (100% accuracy) of the proteomic profile of mesothelial exosomes in our two study groups.
Beyond the increasing evidence on the biological role of exosomes, the rationale of utilizing a purified fraction of PDE exosomes, is to reveal the huge amount of proteins probably masked by abundant high molecular weight proteins (i.e. Albumin). Indeed, a proteomic analysis on extracellular vesicles revealed a tenfold number of proteins 36 compared to total 37 .
Out of the 2490 identified proteins, 10% (251) were FSGS-associated and 40% (995) were involved in fibrosis. Most of them were included in the transforming growth factor β (TGFβ) signalling pathway, playing a major role in EMT onset 16,38,39 . Additionally, the WGCNA algorithm identified a group of mesothelial exosome proteins  www.nature.com/scientificreports/ that maximized the discrimination between FSGS and No FSGS and were highly correlated to peritoneal dialysis vintage, fibrosis, EMT and PM disease. Interestingly, metalloproteinase inhibitor 1 (TIMP1), down-regulated in FSGS, was significantly associated to all the above-mentioned conditions. TIMP1 is a metalloprotease (zinc metalloendopeptidase, MMP) inhibitor that binds the catalytic zinc ion, functioning in integrin signaling and in regulation of cell death and differentiation 40 . Matrix MMPs are linked to fibrosis, being the main groups of ECM-degrading enzymes 41 . In particular, MMP9 activates TGFβ1 and its expression correlates positively with experimental fibrosis 42 , suggesting its pro-fibrotic role 22 .
Other down-regulated proteins in FSGS were: CTHRC1 (Collagen triple helix repeat-containing protein 1), SPARC (secreted protein acidic and rich in cysteine), CHMP4B (fibrosisCharged multivesicular body protein 4b), COL5A2 (Collagen alpha-2(V) chain). CTHRC1 is a negative regulator of collagen matrix deposition and a promigratory protein involved in vascular remodeling, anti-fibrosis, and cancer, therefore its down-regulation is detrimental 21 . SPARC (secreted protein acidic and rich in cysteine) has been long known to possess antiproliferative properties, acting against interstitial fibroblast proliferation by inducing ECM and predisposing to fibrosis 43,44 . CHMP4B is a component of the endosomal sorting complex involved in the formation of multivesicular bodies (MVBs) that generate exosomes as intraluminal vesicles by inward budding of their limiting membrane. COL5A2 is a regulatory fibrillar forming collagen and a key determinant in the assembly of ECM. COLV overexpression has been found in cancer, granulation tissue, inflammation, atherosclerosis, and fibrosis of lung, skin, kidney and liver.
Annexins comprise a family of proteins structurally characterized by the annexin repeat motif, able to bind to negatively charged phospholipids in a Ca 2+ -dependent manner. Annexins are classified into A-E groups, with group A expressed in vertebrates, comprising 12 members (ANXA1-A13, being ANXA12 unassigned). Annexins link cytosolic Ca 2+ dynamics to cytoskeletal responses, being variably implicated in proliferation, differentiation, migration, therefore in pathologies as autoimmunity, and infection 45 . Here, ANXA 1,5,6,7,8,11 and 13 have been identified, with ANXA 1,5,6,7,8 down-regulated and 2,3,4,11,13 up-regulated. In particular, ANXA13 is a protein that can self-associate in a calcium-dependent manner and form complexes with proteins possessing EF hands motives. Annexin A13 is a myristoylated member of the family present in two splice variants (a and b expressed www.nature.com/scientificreports/ in polarized epithelial cells), well represented in the kidney. Annexin A13 is a lipid raft-associated protein that plays a role in membrane transport events and in the organization of membrane dynamics of epithelial cells 46 . FNC2 is a member of the Ficolins, a member of the collectin family of proteins, able to recognize pathogenassociated molecular patterns (PAMPs) on microbial surfaces. Upon binding to their specific PAMP, ficolins may trigger activation of the immune system by either binding to cellular receptors for collectins or by initiating activation of complement via the lectin pathway. FCN2 could be involved in fibrosis by complement activation, associated to myofibroblast activation and fibrogenesis in various animal models of glomerulopathies and FSGS 47,48 . It has also been associated to the development of PD-induced arteriolar vasculopathy in children on chronic PD 49 .
CENP-E is a mitotic kinesin necessary for the mitotic microtubule capture at kinetochores, whose removal results in mitotic arrest.
Furthermore, mesothelial exosomes from FSGS patients expressed fewer proteins statistically associated with the length of dialysis treatment and/or PET test alteration. This appears in line with previous studies in which the protein milieu of extracellular vesicles obtained from PDE was analysed 25,36 . Notably, in one of these studies, the most enriched GO was "Exosome" 25 .
Data seem confirmed by the observation that HPMC treated with TGFβ1 showed increased expression of proteins associated with mesenchymal phenotype (VIME, α-SMA and PTP4A1) displaying the same PTP4A1 HPMC treated with TGFβ1 showed a reduced expression of the epithelial marker E-cadherin and increased expression of VIME, α-SMA and PTP4A1 (p < 0.0001); (C) Representative western blot analysis of full length gel (8-16 T%) for GAPDH (membrane and plot 1) and PTP4A1 (membrane and plot 2) in exosomes from patients with short (lane 1 or white circle) or long (lane 2 or dark grey circle) dialysis vintage and (D) their densitometry analysis visualized as box plots (20 independent patients' cohort). Exosomes with long dialysis vintage and high D/D0 glucose and low D/P creatinine values at PET or with short dialysis vintage and low D/D0 glucose and high D/P creatinine values at PET displayed the same PTP4A1 expression profile of HPMC treated or not with TGFβ1 (p < 0.05). GAPDH was used as loading control. All nitrocellulose membranes were cut perpendicular to the electrophoresis migration front to obtain a full length strips of samples and to allow the individually labeled and detection with the different antibodies. www.nature.com/scientificreports/ expression profile of the exosomes from patients with long dialysis vintage and high D/D0 glucose and low D/P creatinine values at PET. PTP4A1 belongs to a class of prenylated protein tyrosine phosphatases (PTP4A1/2/3) key promoters of TGFβ signaling in fibroblasts, involved in cell migration 50 . PTP4A1 was also shown to be able to activate EMT, in a model of intrahepatic cholangiocarcinoma (ICC) 50 . The under-expression of E-cadherin in face of overexpressed PTP4A1 is consistent with the report showing that PTP4A1 represses E-cadherin through the PI3K/AKT signaling pathway 51 . Although PTP4A1 has been already described in other fibrotic models, its role in kidney or peritoneal membrane disease has not been investigated until now. Interestingly, PTP4A1 was related to both dialysis vintage and PET values. Notably, PTP4A1 is associated to high values of D/D0 glucose and low values of D/P creatinine, that identify the specific pattern of ultrafiltration failure type II, characterized by "ineffective transcapillary ultrafiltration associated with very low peritoneal transport rates" 52 . Pro-fibrotic mediators such as TGFβ1, and EMT seem to be involved in this process that leads to a diffuse hypopermeability of the PM. In rare and extreme cases, this type of ultrafiltration failure develops in EPS 53 .
Notably, the possibility to clearly discriminate PD patients with FSGS and those affected by other PRDs could be useful for the stratification of the 30% of children with ESKD who are referred for transplantation but had not undergone a renal biopsy, and around to 20% of which are diagnosed with possible glomerulonephritis 26,54 . In fact, notwithstanding the advances in immune-modulating and extracorporeal therapies, FSGS remains a challenge for pediatric nephrologists, as response to treatment is low and recurrence can occur after renal transplantation in a significant percentage of cases 13 .
Our data are consistent with the widely shared hypothesis that PDE is a promising source of protein biomarkers 55 . A seminal study demonstrated the presence of extracellular vesicles in PDE and examined them from 12 adult patients. The proteomic analysis of the extracellular vesicles showed an enrichment by size-exclusion chromatography of over 2,000 proteins normally masked by abundant proteins in PDE such as albumin, and identified about 3,700 proteins many of which involved in PM pathology, among which CD81, integrin 3A, ADAM10 (disintegrin and metalloproteinase domain-containing protein 10, that mediates the proteolytic cleavage of IL6R, releasing it) the exosomal marker protein ALIX (ALG-2-interacting protein 1), mesothelin and MUC16 (two markers for mesothelial cells), and CA-125 36 .
There are some limitations of this study that we have to underline. First of all, the small size of the studied patient population, and some differences in the clinical characteristics between the two groups, even if the impact of these differences didn't affect the results of our cutting-edge analysis, based on SVM learning. The wide range of PD duration at the moment of the study, and the absence of evaluation of the impact of the treatment and renal function on our results.
In conclusion, the present proteomic study, although performed on a limited number of patients, demonstrated the existence of a multi-factorial biological machinery (mainly pro-fibrotic) in mesothelial cells of our pediatric patients affected by FSGS. However, this study represents a pilot, and the results, although supported by innovative and high-performance analysis methods deserve further prospective, proteomic and clinical studies to confirm the present data on a larger pediatric PD patient population, to clarify if patients with idiopathic FSGS may be more exposed to the risk of developing PM dysfunction and/or fibrosis when treated with long-term PD. Corroborate our findings, could be essential to eventuallyoptimize replacement therapy in this patient population, for example, by introducing the use of more biocompatible PD solutions known to reduce the activation of the pro-fibrotic machinery and mesothelial to mesenchymal transition or accelerating, as much as possible, their inclusion in the transplant waiting list for kidney transplantation to avoid peritoneal complications. Some of the identified proteins (such as PTP4A1) may be also proposed as biomarkers of mesothelial integrity.

Materials and methods
Patients and isolation of enriched exosome fractions. A total of 52 ESKD patients in PD treatment followed up at the Nephrology Department of the Gaslini Children's Hospital were included in the study. Written informed parental consent was obtained before enrolment. The main demographic and clinical features are summarized in Table 2. The inclusion criteria were defined as follows: patients up to 18 years old, on stable PD for more than one month, without peritonitis in the three months preceding the study, and who had not received a previous kidney transplant. Twelve randomly selected patients were included in the proteomic analysis: six patients had primary focal segmental glomerulosclerosis as baseline nephropathy (FSGS group) and 6 have been affected by other diseases (No FSGS group) ( Table 2). A group of 40 patients (20 FSGS and 20 No FSGS) were included in the validation part of the study.
PM function was evaluated through a 4-h peritoneal equilibration test (PET) conducted by using 1000 ml per m 2 of patient's body surface area of a 2.27% glucose PD solution 56 . Urea and creatinine dialysate-to-plasma concentration ratio (D/P) and glucose dialysate to initial dialysate concentration ratio (D/D0) were calculated.
Patients have been treated with automated peritoneal dialysis (APD) using biocompatible glucose-based solutions, with different glucose concentrations (1.36%, 2.27% and 3.86%) according to the required fluid removal, and with bicarbonate/lactate buffer.
The study was carried out in accordance to Italian and international ethical guidelines and approved by the Comitato Etico Regione Liguria (number: 408REG2014).
Sample collection was standardized by performing the analyses on the PDE obtained at the end of a 4-h PET (see above). Exosomes from mesothelial peritoneal cells were isolated by centrifugation plus immuno-magnetic beads affinity capture. Aliquots (100 ml) of PDE at PET were centrifuged at 22,000×g for 120 min at 16 °C to remove cells, debris, microvesicles and organelles such as mitochondria. Supernatants were then centrifuged at 100,000×g for 120 min at 16 °C to pellet the exosomes. The exosomal pellet was resuspended in 1 ml 0.25 M sucrose, loaded onto 1 ml 30% sucrose cushion and centrifuged at 100,000×g for 120 min at 16  www.nature.com/scientificreports/ was rinsed in PBS and centrifuged again at 100,000×g for 120 min at 16 °C.The final pellet was stored at − 80 °C until use.
Immuno-magnetic beads capture. The method is based on the capture of a specific subset of exosomes from peritoneal dialysis effluent using a biotinylated antibody and streptavidin magnetic beads. Enriched exosomes fractions were mixed with polyclonal biotin-conjugated anti-human mesothelin (MSLN) antibody (LifeSpan BioSciences, Seattle, WA, USA) and incubated 4 h at RT with gentle rotation. Then, streptavidin Dynabeads (ThermoFisher) were added according to the procedure of the manufacturer. Briefly, exosomeantibody-dynabeads complexes were incubated for 30 min at RT with gentle rotation, placed on the magnet and rinsed five times to remove unspecific exosomes and unbound antibodies.Then, sample was removed from the magnet and bound exosomes collected by adding 250 µl of elution buffer. Finally, supernatant was centrifuged at 100,000×g for 120 min at 16 °C to pellet the exosomes pelleted. Such rinse/centrifugation cycle was carried out five times to obtain a clean anti-human mesothelin-positive exosome fraction. Size and purity of the isolated exosomes were assessed by DLS.
Dynamic light scattering. Exosome size was determined by DLS using a Zetasizer nano ZS90 particle sizer at a 90° fixed angle (Malvern Instruments, Worcestershire, UK). The particle diameter was calculated using the Stokes-Einstein equation. For particle sizing in solution, exosome aliquots were diluted in 10% PBS and analyzed at a constant 25 °C.
Western blotting. Expression of exosomal or human peritoneal mesothelial cells (HPMC) markers were detected by western blot. Aliquots of exosome fractions or whole lysate of HPMC were solubilized in 2% w/v SDS, 10% glycerol and 62.5 mM Tris-HCl pH 6.8 and separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to a nitrocellulose membrane. Full length membrane was  MS data were acquired on an Orbitrap Fusion Tribrid mass spectrometer (ThermoScientific). MS1 was performed with Orbitrap detection at a resolving power of 120 K, while MS2 was performed with Ion Trap detection with Rapid Ion Trap Scan Rate. Top speed mode with a 2 s. cycle time was performed for data dependent MS/ MS analysis, during which precursors detected within the range of m/z 375 − 1500 were selected for activation in order of abundance. Quadrupole isolation with a 1.6 m/z isolation window was used, and dynamic exclusion was enabled for 30 s. Automatic gain control targets was set at 4 × 10 5 for MS1 and at 1 × 10 4 for MS2 with 50 and 45 ms maximum injection times respectively. The signal intensity threshold for MS2 was 1 × 10 4 . HCD was performed using 28% normalized collision energy. One microscan was used for both MS1 and MS2 events.
Raw data were processed with MaxQuant 58 software version 1.6.10.0. A false discovery rate (FDR) of 0.01 was set for the identification of proteins, peptides and PSM (peptide-spectrum match). For peptide identification a minimum length of 6 amino acids was required. Andromeda engine, incorporated into MaxQuant software, was used to search MS/MS spectra against Uniprot human database (release UP000005640_9606 April 2019). In the processing the Acetyl (Protein N-Term), Oxidation (M) and Deamidation (NQ) were selected as variable modifications and the fixed modification was Carbamidomethyl (C).
Whole Mass spectrometry data are friendly available at ProteomeXchange Consortium 59 via the PRIDE58 partner repository with the dataset identifier PXD024556 (Reviewer account details: Username: reviewer_ pxd024556@ebi.ac.uk, Password: 1BfsyHkc). ELISA assay. To quantify ANXA13 in undiluted serum of an independent group of 20 patients with FSGS and 20 No FSGS patients, commercial ELISA kit was used (Abnova, KA6081). Assay was performed following the manufacturer's instructions. The Reference standards were run in triplicate and test samples were run in duplicate. Box plot was used to visualize the protein concentration. In box plot each point indicates the mean obtained from duplicate measurement. The lower detection limit was determined as the lowest protein concentration that could be differentiated from blank.
Cell culture. Human peritoneal mesothelial cells (HPMC) were purchased from Creative Bioarray (Shirley, New York, USA). They were grown in DMEM/F12 medium supplemented with 10% FBS, penicillin (100 U/ml), and streptomycin (100 μg/ml), and maintained at 37 °C in a humidified incubator supplied with 5% CO 2 . After cells reached 70% confluence, they were cultured in serum-free medium in the presence or absence of 10 ng/ml TGFβ1 (R&D Systems, Minneapolis, MN) to 96 h and then processed for western blots. Six biological replicates have been done.
Statistical analysis. After normalization, whole mass spectrometry data were analyzed by unsupervised hierarchical clustering using multidimensional scaling (MDS) with k-means and Spearman's correlation, in order to identify outliers and dissimilarity between samples. Then, the normalized whole dataset was used to construct a co-expression network using the weight gene co-expression network analysis (WGCNA) package in R 20 . A weighted adjacency matrix was constructed using the power function. After choosing the appropriate β parameter of power (with the value of independence scale set to 0.8) the adjacency matrix was transformed into a topological overlap matrix (TOM), which measures the network connectivity of all proteins.
To classify proteins that display co-expression profiles into protein modules, hierarchical clustering analysis was conducted according to the TOM dissimilarity, with a minimum size of 20 proteins per module. To identify the relationship between each module and each clinical trait, we used module eigengenes (MEs) and calculated the Spearman's correlation between MEs and the clinical traits, namely: dialysis vintage; PET values; D/D0 glucose and D/P creatinine; FSGS and No FSGS patients. A heatmap was then used to visualize each degree of relationship. Same analysis was done for each protein. Proteins were considered in correlation with at least one www.nature.com/scientificreports/ clinical traits with a significant (two sides p values ≤ 0.05 after Benjamini-Hochberg correction for multiple interactions) Spearman's correlation coefficient values > 0.7. To identify the hub proteins of modules that maximize the discrimination between FSGS and No FSGS samples, we applied T-test, machine learning methods such as non-linear support vector machine (SVM) learning, and partial least squares discriminant analysis (PLS-DA). For the T-test, proteins were considered to be significantly differentially expressed between two conditions with power of 80% and an adjusted p value ≤ 0.05 after correction for multiple interactions (Benjamini-Hochberg) and a fold change of ≥ 2. In addition, the proteins needed to show at least 70% identity in the samples in one of two conditions and area under the curve (AUC) in the received operating characteristic (ROC) analysis > 0.8. Volcano plots were used to visualize the expression fold change differences between FSGS and No FSGS samples.
In SVM learning, a fourfold cross-validation approach was applied to estimate the prediction and classification accuracy. Besides, the whole matrix was randomly divided into two parts: one for learning (65%) and the other (35%) to determine the prediction accuracy.
Finally, gene set enrichment analysis 60 was done to build a functional proteins network based on their Gene Ontology (GO) annotations extracted from the Gene Ontology Consortium (http:// www. geneo ntolo gy. org/). The protein profile expression data were loaded in the dataset and a ranked list was assigned to each GO annotation/pathway. These ranks take into account the number of proteins associated with each gene signature with respect to all proteins, their mean of fold change and the p value after False Discovery Rate (FDR) correction for multiple interactions. These ranks are confined between − 1 and 1, corresponding to minimal and maximal enrichment in each group. In the two-dimensional scatter plot utilized to visualize this analysis, the points located on the straight line passing through the coordinates (1 x , 1 y ) and (− 1 x , − 1 y ) represent the equally enriched signatures. The distance from this line is proportional to the increase in signature enrichment in one of the two groups (over or under the straight line are the GO annotation/pathway positively enriched in FSGS or No FSGS samples respectively).
For the ELISA assay, Mann-Whitney U-test for unpaired samples was used to assess the difference in the concentration of the potential biomarker of FSGS samples. Results were expressed as medians and interquartile range (IQr). Receiver operating characteristic (ROC) curves were generated to assess the diagnostic efficiency of assay. AUC value was classified as: 0.5, not discriminant; 0.5-0.6, fail; 0.6-0.7, poor; 0.7-0.8, fair; 0.8-0.9, good and 0.9-1, excellent. Youden's index and Likelihood ratio were used to identify the cutoff and the diagnostic performance of each assay, respectively. Two sides p values ≤ 0.05 were considered as significant. All statistical tests were performed using Origin Lab V9 and the latest version of software package R available at the time of the experiments 61 .