METHOD OF SCREENING FOR A CHRONIC KIDNEY DISEASE OR GLOMERULOPATHY METHOD OF MONITORING A RESPONSE TO TREATMENT OF A CHRONIC KIDNEY DISEASE OR GLOMERULOPATHY IN A SUBJECT AND A METHOD OF TREATMENT OF A CHRONIC KIDNEY DISEASE OR GLOMERULOPATHY

Abstract

The object of the present invention is a method of diagnosis of a chronic kidney disease (CKD) or glomerulopathy in a subject, comprising the following steps: (a) determination of the level of at least three or four or five protein markers selected from the group consisting of serum albumin (ALB), alpha-1-antitrypsin (serpinal), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF), wherein said markers also comprise the non-full-length fragments thereof, in a urine sample from said subject and (b) assigning a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy or not having nor being at a risk thereof based on the results of the assay of step (a), wherein this involves estimating a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy or not having nor being at a risk thereof based on the level of each of the marker levels determined in (a)), the probability being estimated based on the levels of each of the markers as determined in subjects known to suffer from a glomerulopathy or a chronic kidney disease; and determining the probability of the subject, providing the urine sample tested in step (a), having or being at a risk of a glomerulopathy or a chronic kidney disease or not having nor being at a risk thereof as a product of the corresponding probabilities obtained from each marker. A further object of the present invention is a method of monitoring a response to treatment of a chronic kidney disease (CKD) or glomerulopathy in a subject, comprising the following steps: a) measurement of the level, at a first point in time, for three or four or five of the markers selected from a group consisting of serum albumin (ALB), alpha-1-antitrypsin (serpinal), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF), wherein said markers also comprise the non-full-length fragments thereof, in a urine sample from a subject; b) repeating the assay of step (a) at a later point in time after a period wherein the subject was undergoing a treatment; c) assessing a response to said treatment by comparing the results of the assays of steps (a) and (b), wherein lower marker levels after treatment are indicative of a positive response to treatment. A further object of the present invention is a method of treatment of a chronic kidney disease (CKD) or glomerulopathy in a subject, comprising the following steps: (a) determination of the level of at least three or four or five protein markers selected from the group consisting of serum albumin (ALB), alpha-1-antitrypsin (serpinal), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF), wherein said markers also comprise the non-full-length fragments thereof, in a urine sample from said subject and (b) assigning a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy based on the results of the assay of step (a); (c) administering treatment against a chronic kidney disease (CKD) or glomerulopathy in the subject evaluated in step (b) as having or being at a risk of chronic kidney disease or glomerulopathy.

Claims

1. A method of diagnosis of a chronic kidney disease (CKD) or glomerulopathy in a subject, comprising the following steps: (a) determination of the level of at least three or four or five protein markers selected from the group consisting of serum albumin (ALB), alpha-1-antitrypsin (serpina1), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF), wherein said markers also comprise the non-full-length fragments thereof, in a urine sample from said subject and (b) assigning a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy or not having nor being at a risk thereof based on the results of the assay of step (a), wherein this involves estimating a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy or not having nor being at a risk thereof based on the level of each of the marker levels determined in (a), the probability being estimated based on the levels of each of the markers as determined in subjects known to suffer from a glomerulopathy or a chronic kidney disease; and determining the probability of the subject, providing the urine sample tested in step (a), having or being at a risk of a glomerulopathy or a chronic kidney disease or not having nor being at a risk thereof as a product of the corresponding probabilities obtained from each marker.

2. The method of claim 1, wherein the level of said markers in step (a) is determined by mass spectrometry (MS).

3. The method of claim 1, wherein step (a) involves measurement of the level of all five protein markers serum albumin (ALB), alpha-1-antitrypsin (serpina1), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF).

4. The method of claim 3, wherein the probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy is assigned in step (b) using the following formula: $p\left(\mathrm{disease}\right)=\frac{\exp \left(E\right)}{\left(1+\exp \left(E\right)\right)}$ wherein:
E=17.204550857965−5.75799550569336*10.sup.−10*x.sub.1−9.37976121221068*10.sup.−9*x.sub.2+1.32966288022553*10.sup.−8*x.sub.3+2.5638225555611*10.sup.−8*x.sub.4+4.03113433888467*10.sup.−7*x.sub.5; wherein x.sub.1 is the determined level for Serum albumin (ALB); x.sub.2 is the determined level for alpha-1-antitrypsin (serpina1); x.sub.3 is the determined level for alpha-1-acid glycoprotein 1 (ORM1); x.sub.4 is the determined level for serotransferrin (TF); x.sub.5 is the determined level for Trefoil factor 1 (TFF1).

5. The method of claim 1, wherein the method further involves classification of the analysed sample as derived from a subject having or being at a risk of a particular glomerulopathy, the classification involving the following steps: (c) determining the probability of the subject having or being at a risk of a particular glomerulopathy based on the level of a first marker one of the markers determined in step (a), the probability being estimated based on the levels of said first marker determined in subjects known to have the particular glomerulopathy; (d) determining the probability of the patient having or being at a risk of a particular glomerulopathy based on the level of at least one another marker of the markers determined in step (a), the probability being estimated based on the levels of said at least one another marker determined in subjects known to have the particular glomerulopathy; (e) classifying the sample as derived form a subject having or being at a risk of a particular glomerulopathy based on results from the preceding steps.

6. The method of claim 5, wherein the particular glomerulopathy is selected from the group consisting of IgAN, membranous nephropathy (MN) or lupus nephritis (LN).

7. A method of monitoring a response to treatment of a chronic kidney disease (CKD) or glomerulopathy in a subject, comprising the following steps: (a) measurement of the level, at a first point in time, for three or four or five of the markers selected from a group consisting of serum albumin (ALB), alpha-1-antitrypsin (serpina1), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF), wherein said markers also comprise the non-full-length fragments thereof, in a urine sample from a subject; (b) repeating the assay of step (a) at a later point in time after a period wherein the subject was undergoing a treatment; (c) assessing a response to said treatment by comparing the results of the assays of steps (a) and (b), wherein lower marker levels after treatment are indicative of a positive response to treatment.

8. The method of claim 7, wherein the level of said markers in step) and (b) is determined by mass spectrometry (MS).

9. The method of f claim 7, wherein step c) involves assigning a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy based on the results of the assay for the results of steps (a) and (b) and assessing a response to said treatment by comparing the results of probability for steps (a) and (b).

10. The method of claim 9, wherein the probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy is assigned in step c) using the following formula: $p\left(\mathrm{disease}\right)=\frac{\exp \left(E\right)}{\left(1+\exp \left(E\right)\right)}$ wherein:
E=17.204550857965−5.75799550569336*10.sup.−10*x.sub.1−9.37976121221068*10.sup.−9*x.sub.2+1.32966288022553*10.sup.−8*x.sub.3+2.5638225555611*10.sup.−8*x.sub.4+4.03113433888467*10.sup.−7*x.sub.5; wherein x.sub.1 is the determined level for Serum albumin (ALB); x.sub.2 is the determined level for alpha-1-antitrypsin (serpina1); x.sub.3 is the determined level for alpha-1-acid glycoprotein 1 (ORM1); x.sub.4 is the determined level for serotransferrin (TF); x.sub.5 is the determined level for Trefoil factor 1 (TFF1).

11. A method of treatment of a chronic kidney disease (CKD) or glomerulopathy in a subject, comprising the following steps: (a) determination of the level of at least three or four or five protein markers selected from the group consisting of serum albumin (ALB), alpha-1-antitrypsin (serpina1), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF), wherein said markers also comprise the non-full-length fragments thereof, in a urine sample from said subject and (b) assigning a probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy based on the results of the assay of step (a); (c) administering treatment against a chronic kidney disease (CKD) or glomerulopathy in the subject evaluated in step (b) as having or being at a risk of chronic kidney disease or glomerulopathy.

12. The method of treatment of claim 11, wherein the presence of the abovementioned markers in the urine sample is determined in step (a) by mass spectrometry (MS).

13. The method of treatment of claim 11, wherein step (a) involves determination of the level of all five protein markers serum albumin (ALB), alpha-1-antitrypsin (serpina1), alpha-1-acid glycoprotein 1 (ORM1), serotransferrin (TF) and trefoil factor 1 (TFF).

14. The method of treatment of claim 13, wherein the probability of the subject having or being at a risk of chronic kidney disease or glomerulopathy is assigned in step (b) using the following formula: $p\left(\mathrm{disease}\right)=\frac{\exp \left(E\right)}{\left(1+\exp \left(E\right)\right)}$ wherein:
E=17.204550857965−5.75799550569336*10.sup.−10*x.sub.1−9.37976121221068*10.sup.−9*x.sub.2+1.32966288022553*10.sup.−8*x.sub.3+2.5638225555611*10.sup.−8*x.sub.4+4.03113433888467*10.sup.−7*x.sub.5; wherein x.sub.1 is the determined level for Serum albumin (ALB); x.sub.2 is the determined level for alpha-1-antitrypsin (serpina1); x.sub.3 is the determined level for alpha-1-acid glycoprotein 1 (ORM1); x.sub.4 is the determined level for serotransferrin (TF); x.sub.5 is the determined level for Trefoil factor 1 (TFF1).

Description

BRIEF DESCRIPTION OF DRAWINGS

[0138] FIG. 1 shows Signal intensity (Mean±SD) of A1BG, ORM-1 and TF in SPOT urine samples. Protein A=A1BG, Protein B=ORM-1, Protein C=TF.

[0139] FIG. 2 shows delta GFR to years of observation vs. ORM1 level (indicated as protein B).

[0140] FIG. 3 shows a proteinogram for MS measurements for a control group (FIG. 3A), patients with IgAN (FIG. 3B), patients with MN (FIG. 3C) and patients with LS (FIG. 3D); FIG. 3E shows a comparison of proteinogram patterns for the control group and the three glomerulopathies as above; FIG. 3F shows a comparison of proteinogram patterns on a smaller scale and without demonstrating the full results for albumin in order to better visualize differing patterns between conditions.

[0141] FIG. 4 shows a comparison of the most extreme (most discriminating) proteins obtaining 18 unique proteins, as found for patients from groups 1=control, 3=IgAN, 4=MN and 5=LN as the control group was separated from others.

[0142] FIG. 5 shows a model employing seven proteins (groups 3=IgAN, 4=MN and 5=LN).

[0143] FIG. 6 shows an illustrative diagnostic scheme for the present methods. Panel A. Screening, panel B. Discrimination between IgAN, MN or LN, panel C. Decision making after establishing diagnosis.

[0144] FIG. 7 shows a decision tree allowing estimating probability of different glomerulopathies, based on measured levels of the measured protein markers in a urine sample. Group 3: IgAN, Group 4: MN, Group 5: LN.

EXAMPLES

Example 1. Urinary Proteomic Markers for Membranous Nephropathy (MN)

[0145] Methods

[0146] This study included patients with biopsy-proven MN (25) and healthy controls (7). Urine samples were obtained from a midstream of the second- or third-morning (SPOT) sample. The samples were processed up to 2 h after collection and stored at −80° C. for further measurements with MS. The results were related to demographic data, standard laboratory tests and GFR estimated with use of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.

[0147] Results

[0148] The signal intensity from A1BG, ORM-1, FTL and TF was found to be higher in MN patients than in controls. According to MS, MN patients had significantly (p<0.05) elevated signal of A1BG, ORM-1 and TF comparing to controls (FIG. 1). Mass spectrometry, according to the specific amino-acids fragments of each tested protein, confirmed the differences between tested and control group. Additionally, statistically significant differences exist between patients with different types of glomerulonephritides.

[0149] The signal intensity of A1BG, ORM-1, FTL and TF are elevated in MN and vary depending on types of nephropathies. This observation suggests their differential roles in the pathophysiology of the given disease, and its possible application as a non-invasive diagnostic and prognostic marker.

Example 2. Correlation of ΔGFR and a Protein Marker

[0150] ΔGFR (change in glomerular filtration rate, calculated as: (current GFR−initial GFR)/observation years) was estimated for several patients and its relation to various analysed protein marker levels as estimated by MS was analysed.

[0151] An exemplary result is shown on FIG. 2. FIG. 2 demonstrates that ORM1 level (indicated as protein B) as measured by MS in a urine sample may correlate with high ΔGFR. This suggests that a protein marker (such as ORM1) may indeed by utilized as a readily available and quicker prognostic tool, enabling estimation and prognosis of the rate in change in glomerular filtration rate for a given patient).

Example 3. Larger Scale Screening

[0152] For preliminary analysis samples from 84 patients were used. The samples were prepared and measured according to a modified, improved protocol to the one disclosed in Krzysztof Mucha, Bartosz Foroncewicz, Leszek Pgczek, How to diagnose and follow patients with glomerulonephritis without kidney biopsy?. Polskie Archiwum Medycyny Wewnetrznej, 2016, 126(7-8):471-473.

[0153] Briefly, samples were collected from all individuals according to a uniform study protocol, following the recommendations on urine proteomic sample collection. The second- or third morning midstream urine was collected to sterile urine containers 1 to 3 h after previous urination.

[0154] Sample Preparation:

[0155] Steps 1-4 are performed to concentrate protein/desalt/remove lipids and small organic/inorganic molecules.

[0156] 1. 200 ul of a urine sample is transferred to Vivavon 500 Hydrosart spin-unit 10 MWCO filter.

[0157] 2. The sample is centrifuge at the highest possible speed (14 kG) at 20 C for 30 minutes to achieve the fastest concentration also to avoid protein degradation. Flow-through is discarded.

[0158] 3. 200 ul of 8M solution of urea in 100 mM TEAB (Triethylammonium bicarbonate, Thermo #90114) is added to sample and spin for 20 minutes 14 kG, 20 C. The step is done twice. 4. Flow-through is discarded and 1.5 ml tube is replaced with a new one.

[0159] Protein Digestion:

[0160] 1. The mix of enzymes LysC/Trypsin (Promega V5071) 20 ug per unit is solubilized in 500 ul of 8M solution of urea in 100 mM TEAB.

[0161] 2. 50 ul of the solution was added to the spin unit and incubated for 5 hours at 37 C with mild shaking (70 rpm). LysC is a protease which cleaves peptide bond at C-side of lysine in a peptide. It has a unique ability to stay enzymatically active in denaturing conditions such as high urea concentration. It allows us to achieve higher peptide coverage thanks to the digestion of unfolded proteins.

[0162] 3. After 5 h incubation to each sample 400 ul of 100 mM TEAB solution was added to dilute urea bellow 1M, which allows trypsin to be enzymatically active. Samples are digested overnight at 37 C with mild shaking.

[0163] 4. Filtrating units are spin at 14 kG for 30 minutes at 20 C.

[0164] 5. 200 ul of 0.1 M NaCl in 100 mM TEAB is added and samples are spin at 14 kG for 30 minutes at 20 C.

[0165] 6. 10 ul of 5% formic acid solution is added to stop digestion.

[0166] Peptides Desalting:

[0167] 1. Final peptide solution from step 9 is transferred to Oasis HLB 96 well-plate (Waters, 186000128).

[0168] 2. Peptides are concentrated at HLB sorbent at manifold pressure (15 kPa for 3 minutes).

[0169] 3. The sorbent is washed twice with 0.1% TFA solution.

[0170] 4. Peptides are eluted in two steps: 200 ul of methanol, 200 ul of 80% acetonitrile/20% water.

[0171] 5. Peptide solution is evaporated to dryness with SpeedVac.

[0172] Mass Spectrometry

[0173] MS analysis was performed by LC-MS in the Laboratory of Mass Spectrometry (IBB PAS, Warsaw) using a nanoAcquity UPLC system (Waters) coupled to an Orbitrap QExactive mass spectrometer (Thermo Fisher Scientific). The resulting peptide mixtures were applied to RP-18 pre-column (Waters, Milford, Mass.) using water containing 0.1% TFA as a mobile phase and then transferred to a nano-HPLC RP-18 column (internal diameter 75 μM, Waters, Milford Mass.) using ACN gradient (0-35% ACN in 180 min) in the presence of 0.1% FA at a flow rate of 250 nl/min. The column outlet was coupled directly to the ion source of Orbitrap QExative mass spectrometer (Thermo Electron Corp., San Jose, Calif.) working in the regime of data-dependent MS to MS/MS switch and data were acquired in the m/z range of 300-2000 The mass spectrometer was operated in the data-dependent MS2 mode, and data were acquired in the m/z range of 100-2000. Peptides were separated by a 180 min linear gradient of 95% solution A (0.1% formic acid in water) to 45% solution B (acetonitrile and 0.1% formic acid). The measurement of each sample was preceded by three washing runs to avoid cross-contamination. Data were analyzed with the Max-Quant (Version 1.6.3.4) platform using mode match between runs (Cox and Mann, 2008)

[0174] Results

[0175] The goal of the data analysis was to assess the feasibility of a two-step model based on the MS data able to: (a) discriminate between patients and control group; (b) discriminate between disorders affecting patients. MS measurements covered 84 patients: 30 IgAN patients; 20 MN patients; 26 LN patients and 8 healthy controls. During the analysis, the focus was on IgAN, MN and LN patients, as the control group was separated from others. 2510 proteins were identified at 5% FDR (False Discovery Rate). In order to reduce the number of false positive identifications, a threshold of 0.1% was assumed with FDR resulting in 1659 proteins.

[0176] For each of the 1659 proteins considered in the MS analysis, W test statistic was computed (as per Wilcoxon test) for pairwise comparisons between patient groups (IgAN, MN, LN) and control group. Due to the preliminary character of the test, bootstrapped W values were not used.

[0177] The proteinograms showed distinct patterns, differentiating the control group from particular glomerulopathies (FIG. 3). FIGS. 3A-D show the proteinogram patterns obtained for the four groups (control—A, IgAN—B, MN—C, LS—D). Each dot colour corresponds to a different patient. The protein with the highest levels in all graphs is serum albumin, which is a more universal marker for proteinuria. FIG. 3E shows a comparison of patterns for all four groups, while FIG. 3F shows the comparison on a smaller scale without displaying the top values for serum albumin in order to better visualize the differences between the groups.

[0178] It is clearly visible that the proteinogram patterns can be used for reliable differentiation between groups and for identification of a particular glomerulopathy.

[0179] The results were further analysed in order to identify the most useful markers to be employed in screening procedures. The proteins found to be suitable for the discrimination between patients and healthy controls and differentiation between the particular diseases are listed in Table 1 below.

TABLE-US-00001 TABLE 1 Proteins suitable for detection and differentiation of CKDs T: Gene T: Majority names protein IDs T: Protein names ALB P02768-1 Serum albumin CP P00450 Ceruloplasmin TF P02787 Serotransferrin A1BG P04217 Alpha-1B-glycoprotein ORM1 P02763 Alpha-1-acid glycoprotein 1 IGHG2 A0A286YEY4 Ig gamma-2 chain C region F2 P00734 Prothrombin; Activation peptide fragment 1; Activation peptide fragment 2; Thrombin light chain; Thrombin heavy chain ORM2 P19652 Alpha-1-acid glycoprotein 2 SERPINA1 P01009 Alpha-1-antitrypsin; Short peptide from AAT AZGP1 P25311 Zinc-alpha-2-glycoprotein CNDP1 Q96KN2 Beta-Ala-His dipeptidase SERPINA6 P08185 Corticosteroid-binding globulin P01780 Ig heavy chain V-III region JON AFM P43652 Afamin IGHV3-21 A0A0B4J1V1 TTR P02766 Transthyretin ITIH2 Q5T985 Inter-alpha-trypsin inhibitor heavy chain H2 HPX P02790 Hemopexin HP P00738 Haptoglobin; Haptoglobin alpha chain; Haptoglobin beta chain CD59 E9PNW4 CD59 glycoprotein A2M P01023 Alpha-2-macroglobulin GC D6RF35 Vitamin D-binding protein LYNX1 P0DP57 GM2A P17900 Ganglioside GM2 activator; Ganglioside GM2 activator isoform short Q1RMN8 SERPINC1 P01008 Antithrombin-III SLURP1 P55000 Secreted Ly-6/uPAR-related protein 1 C3 P01024 Complement C3; Complement C3 beta chain; C3-beta- c; Complement C3 alpha chain; C3a anaphylatoxin; Acylation stimulating protein; Complement C3b alpha chain; Complement C3c alpha chain fragment 1; Complement C3dg fragment; Complement C3g fragment; Complement C3d fragment; Complement C3f fragment; Complement C3c alpha chain fragment 2 IGLL5 A0A0B4J231 Immunoglobulin lambda-like polypeptide 5; Ig lambda-1 chain C regions CPN1 P15169 Carboxypeptidase N catalytic chain CD55 H7BY55 Complement decay-accelerating factor IGHG3 P01860 Ig gamma-3 chain C region IGHV5-51 A0A0C4DH38 LEAP2 Q969E1 Liver-expressed antimicrobial peptide 2 GRN P28799 Granulins; Acrogranin; Paragranulin; Granulin-1; Granulin- 2; Granulin-3; Granulin-4; Granulin-5; Granulin-6; Granulin-7 PGM1 P36871 Phosphoglucomutase-1 PON1 P27169 Serum paraoxonase/arylesterase 1 C4B P0C0L5 Complement C4-B; Complement C4 beta chain; Complement C4-B alpha chain; C4a anaphylatoxin; C4b-B; C4d- B; Complement C4 gamma chain P01619 Ig kappa chain V-III region B6 VTA1 Q9NP79 Vacuolar protein sorting-associated protein VTA1 homolog VASN Q6EMK4 Vasorin TCP1 P17987 T-complex protein 1 subunit alpha IGHV3-66 A0A0C4DH42 IGKV2D- A0A075B6P5 Ig kappa chain V-II region FR 28 A0A0G2JMB2 GPLD1 P80108 Phosphatidylinositol-glycan-specific phospholipase D LRG1 P02750 Leucine-rich alpha-2-glycoprotein PSAP P07602 Prosaposin; Saposin-A; Saposin-B-Val; Saposin-B; Saposin- C; Saposin-D SERPINA3 P01011 Alpha-1-antichymotrypsin; Alpha-1-antichymotrypsin His-Pro- less IGKC P01834 Ig kappa chain C region ACO1 P21399 Cytoplasmic aconitate hydratase MB P02144 Myoglobin DCXR Q7Z4W1 L-xylulose reductase PGLYRP2 Q96PD5 N-acetylmuramoyl-L-alanine amidase WFDC2 Q14508 WAP four-disulfide core domain protein 2 GOT1 P17174 Aspartate aminotransferase, cytoplasmic P01624 Ig kappa chain V-III region POM NAP1L4 C9JZI7 Nucleosome assembly protein 1-like 4 HBA1 P69905 Hemoglobin subunit alpha FOLR1 P15328 Folate receptor alpha LAMC1 P11047 Laminin subunit gamma-1 SERPINA7 P05543 Thyroxine-binding globulin P04432 Ig kappa chain V-I region Daudi; Ig kappa chain V-I region DEE TFF2 Q03403 Trefoil factor 2 PDCD6IP Q8WUM4 Programmed cell death 6-interacting protein TFF1 P04155 Trefoil factor 1 IGKV1-5 P01602 Ig kappa chain V-I region HK102 IGHG1 A0A0A0MS08 Ig gamma-1 chain C region APOA1 P02647 Apolipoprotein A-I; Proapolipoprotein A-I; Truncated apolipoprotein A-I HINT1 P49773 Histidine triad nucleotide-binding protein 1 FZD4 Q9ULV1 Frizzled-4 IGLV3-10 A0A075B6K4 FAM3B A8MTF8 Protein FAM3B IL10RB H0Y3Z8 Interleukin-10 receptor subunit beta CLSTN1 Q5SR54 Calsyntenin-1; Soluble Alc-alpha; CTF1-alpha PPIB P23284 Peptidyl-prolyl cis-trans isomerase B TIMP2 P16035 Metalloproteinase inhibitor 2 RNASE1 P07998 Ribonuclease pancreatic FBN1 P35555 Fibrillin-1 PDCD6 O75340 Programmed cell death protein 6 NT5C Q8TCD5 5(3)-deoxyribonucleotidase, cytosolic type IGKV3D- A0A0A0MRZ8 Ig kappa chain V-III region VG 11 IGHM A0A1B0GUU9 Ig mu chain C region SHMT1 P34896 Serine hydroxymethyltransferase, cytosolic S100A7 P31151 Protein S100-A7 LGALS3 P17931 Galectin-3; Galectin IGHV4-61 A0A0C4DH41 Ig heavy chain V-II region NEWM UMOD X6RBG4 Uromodulin; Uromodulin, secreted form BCAM A0A087WXM8 Basal cell adhesion molecule FAT4 Q6V0I7 Protocadherin Fat 4 HBB P68871 Hemoglobin subunit beta; LVV-hemorphin-7; Spinorphin CMBL Q96DG6 Carboxymethylenebutenolidase homolog CUTA O60888 Protein CutA PCDHGC3 Q9UN70 Protocadherin gamma-C3 ENPP2 Q13822 Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 CD300A Q9UGN4 CMRF35-like molecule 8 GLO1 Q04760 Lactoylglutathione lyase GPC4 O75487 Glypican-4; Secreted glypican-4 RNF13 O43567 E3 ubiquitin-protein ligase RNF13 NHLC3 Q5JS37 NHL repeat-containing protein 3

[0180] For each comparison, ten of the most extreme (most discriminating) proteins were taken obtaining 18 unique proteins (FIG. 4).

[0181] Using selected proteins a multinomial log-linear models were built via neural networks (Venables W N, Ripley B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer) The full model was optimized, covering all 18 proteins, considering the Akaike Information Criterion. The resulting model employed seven proteins. Higher order effects of proteins were computed in the model to assess how intensity produced by the MS experiments affects the probability of specific disorders (FIG. 5) (Fox J, Hong J. Effect displays in R for multinomial and proportional-odds logit models: Extensions to the effects package. Journal of Statistical Software 2009. 32:1; 1-24). For example, for P00450 (gene name CP), for lower intensities, a patient has the highest probability of being in group 4 (MN). On the other hand, for the highest possible values of intensities, the most probable group is 3 (IgAN).

[0182] Probabilities were additionally converted to discrete predictions (Table 2).

TABLE-US-00002 TABLE 2 Predictions, based on measured intensity for particular proteins (group 3 = IgAN, group 4 = MN, group 5 = LN) Protein (gene) Intensity Patient group A0A286YEY4; P01859 (IGHG2) <1e+06 5 A0A286YEY4; P01859 (IGHG2) 1e+06-3e+10 3 A0A286YEY4; P01859 (IGHG2) 3e+10-5e+10 3 A0A286YEY4; P01859 (IGHG2) 5e+10-8e+10 3 A0A286YEY4; P01859 (IGHG2) 8e+10-1e+11 3 P00450 (CP) <1e+07 4 P00450 (CP) 1e+07-3e+10 3 P00450 (CP) 3e+10-6e+10 3 P00450 (CP) 6e+10-9e+10 3 P00450 (CP) 9e+10-1e+11 3 P00734; E9PIT3; C9JV37 (F2) <0 3 P00734; E9PIT3; C9JV37 (F2)   0-3e+09 4 P00734; E9PIT3; C9JV37 (F2) 3e+09-6e+09 4 P00734; E9PIT3; C9JV37 (F2) 6e+09-9e+09 4 P00734; E9PIT3; C9JV37 (F2) 9e+09-1e+10 4 P02763 (ORM1) <4e+06 3 P02763 (ORM1) 4e+06-3e+10 3 P02763 (ORM1) 3e+10-6e+10 5 P02763 (ORM1) 6e+10-9e+10 5 P02763 (ORM1) 9e+10-1e+11 5 P04217; M0R009; CON_Q2KJF1 <3e+05 3 (A1BG) P04217; M0R009; CON_Q2KJF1 3e+05-5e+10 4 (A1BG) P04217; M0R009; CON_Q2KJF1 5e+10-9e+10 4 (A1BG) P04217; M0R009; CON_Q2KJF1 9e+10-1e+11 4 (A1BG) P04217; M0R009; CON_Q2KJF1 1e+11-2e+11 4 (A1BG) P04432; P01597 (none) <0 4 P04432; P01597 (none)   0-2e+09 5 P04432; P01597 (none) 2e+09-3e+09 5 P04432; P01597 (none) 3e+09-5e+09 5 P04432; P01597 (none) 5e+09-7e+09 5 Q5JS37; C9J973 (NHLRC3) <0 3 Q5JS37; C9J973 (NHLRC3)   0-2e+09 5 Q5JS37; C9J973 (NHLRC3) 2e+09-4e+09 5 Q5JS37; C9J973 (NHLRC3) 4e+09-6e+09 5 Q5JS37; C9J973 (NHLRC3) 6e+09-8e+09 5

[0183] The current model in 97% of cases differentiates between the control group and afflicted patients. Moreover, in 65.79% of cases it is able to accurately distinguish between diseases (IgAN, MN and LN). This data shows that the label-free proteomics approach enables to perform semi quantitative analysis on the basis of which proteins can be selected for further verification by means of targeted proteomics. There was very high repeatability and consistency of the data for the samples (highest to lowest: control, IgAN, MN, LN).

[0184] Among the protein markers found to be the strongest diagnostic or differentiating factors, there were no significant sequence similarities or homology. However, a large number of the selected markers share some analogous structural features, such as Ig-like domains. This is a type of protein domain that consists of a 2-layer sandwich of 7-9 antiparallel 3-strands arranged in two 3-sheets with a Greek key topology. This type of domains are found in hundreds of proteins of different functions. However, the protein markers found to be the most useful diagnostic factors in the study described above, were strikingly similar in the localization of their Ig-like domains and disulphide bridges, showing structural similarity despite varied amino acid sequences. Many of the selected markers have a function related to neutrophil degranulation and/or blood platelets functions.

Example 4. Development of a Diagnostic Model

[0185] The model obtained in Example 3 was built for the second, hardest step of the analysis and differentiates between three disorders affecting patients. The rational panel design suggests limiting the amount of involved proteins. Therefore the present inventors endeavored to develop a simpler model, discriminating between the control group and patients.

[0186] To model the relationship between the status of a patient (control/afflicted) and intensities of measured proteins, a generalized linear model (GLM) was used with a binomial error distribution and the logit link function (McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall). The final linear model was constructed using the backward AIC-based selection of variables (Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer).

[0187] Further analysis of protein profile obtained in the aforementioned screening studies, permitted the isolation of at least eight potential candidate proteins, which in the most perfect way can be used to investigate the pathophysiology IgAN. Research on the role of these 8 proteins, previously unpublished, can be a basis for a novel diagnostic method. The final model, capable of reliably distinguishing between the control group and patients involves the following 5 proteins: [0188] P02768 (ALB, Serum albumin), measured intensity indicated herein as x.sub.1; [0189] P01009 (serpina1, Alpha-1-antitrypsin), measured intensity indicated herein as x.sub.2; [0190] P02763 (ORM1, Alpha-1-acid glycoprotein 1), measured intensity indicated herein as x.sub.3; [0191] P02787 (TF, Serotransferrin), measured intensity indicated herein as x.sub.4; [0192] P04155 (TFF1, Trefoil factor 1), measured intensity indicated herein as x.sub.5.

[0193] Deviance Residuals: [0194] Min 1Q Median 3Q Max

[0195] 1.165e-03 2.000e-08 2.000e-08 2.000e-08 6.337e-04

[0196] Coefficients:

TABLE-US-00003 Estimate Std. Error Z value Pr(>|z|) (Intercept) 1.72E+01 1.57E+03 0.011 0.991 x1 −5.76E−10  2.83E−08 −0.02 0.984 x2 −9.38E−09  6.30E−07 −0.015 0.988 x3 1.33E−08 1.07E−06 0.012 0.99 x4 2.56E−08 1.15E−06 0.022 0.982 x5 4.03E−07 5.01E−05 0.008 0.994

[0197] (Dispersion parameter for binomial family taken to be 1)

[0198] Null deviance: 5.2835e+01 on 83 degrees of freedom

[0199] Residual deviance: 2.4935e-06 on 78 degrees of freedom

[0200] AIC: 12

[0201] Number of Fisher Scoring iterations: 25

[0202] The best model (with the best value of AIC criterion) involved the level of five proteins:

[00006]$p\left(\mathrm{afflicted}\right)=\frac{\exp \left(E\right)}{1+\exp \left(E\right)}$$E=17.205-5.758⨯{10}^{-10}⨯x_1-9.3798⨯{10}^{-9}⨯x_2+1.3297⨯{10}^{-8}⨯x_3+2.5638⨯{10}^{-8}⨯x_4+4.0311⨯{10}^{-7}⨯x_5$

[0203] Where:

[0204] x.sub.1: ALB

[0205] x.sub.2: SERPINA1

[0206] x.sub.3: ORM1

[0207] X.sub.4: TF

[0208] x.sub.5: TFF1

[0209] The model was validated using the jackknife (leave-one-out) test yielding following performance measures:

TABLE-US-00004 Area under True False True False the curve Positive Positive Negative Negative 0.9786 73 0 8 3

[0210] The probability of the disease can therefore be calculated using the following formula:

[00007]$p\left(\mathrm{disease}\right)=\frac{\exp \left(E\right)}{\left(1+\exp \left(E\right)\right)}$

[0211] wherein:

E=17.204550857965−5.75799550569336*10.sup.−10*x.sub.1−9.37976121221068*10.sup.−9*x.sub.2+1.32966288022553*10.sup.−8*x.sub.3+2.5638225555611*10.sup.−8*x.sub.4+4.03113433888467*10.sup.−7*x.sub.5.

Example 5. Diagnostic Approach

[0212] Coded urine samples derived from patients are analysed using MS and levels of five protein markers are evaluated. The analysed markers were serum albumin (ALB; P02768); alpha-1-antitrypsin (serpina1; P01009); alpha-1-acid glycoprotein 1 (ORM1; P02763); serotransferrin (TF; P0278) and Trefoil factor 1 (TFF1; P04155).

[0213] The formula

[00008]$p\left(\mathrm{disease}\right)=\frac{\exp \left(E\right)}{\left(1+\exp \left(E\right)\right)}$

[0214] wherein:

E=17.204550857965−5.75799550569336*10.sup.−10*x.sub.1−9.37976121221068*10.sup.−9*x.sub.2+1.32966288022553*10.sup.−8*x.sub.3+2.5638225555611*10.sup.−8*x.sub.4+4.03113433888467*10.sup.−7*x.sub.5;

[0215] wherein x.sub.1 is the determined level for serum albumin (ALB; P02768); x.sub.2 is the determined level for alpha-1-antitrypsin (serpina1; P01009); x.sub.3 is the determined level for alpha-1-acid glycoprotein 1 (ORM1; P02763); x.sub.4 is the determined level for serotransferrin (TF; P0278); x.sub.5 is the determined level for trefoil factor 1 (TFF1; P04155),

[0216] was used to calculate the probability for each sample of being derived from the subject having or being at a risk of chronic kidney disease or glomerulopathy.

[0217] For the samples classified as derived from subjects having or being at a risk of chronic kidney disease or glomerulopathy, a further classification was performed, in order to divide them in groups corresponding to a specific condition.

[0218] This classification step was performed utilizing a decision tree shown on FIG. 7. The conditions for classification to groups are also listed below (Group 3: IgAN, Group 4: MN, Group 5: LN):

[0219] group is Group: 3 [0.67 0.33 0.00] when [0220] P02787 is 8.4e+10 to 2.5e+11 [0221] P02763>=4.0e+10 [0222] P02768 is 8.7e+11 to 1.7e+12

[0223] group is Group: 3 [0.82 0.12 0.06] when [0224] P02787>=8.4e+10 [0225] P02763<4.0e+10

[0226] group is Group: 3 [0.83 0.17 0.00] when [0227] P02787>=8.4e+10 [0228] P02763>=4.0e+10 [0229] P02768>=1.7e+12

[0230] group is Group: 4 [0.40 0.60 0.00] when [0231] P02787<1.9e+10 [0232] P02763<1.7e+10 [0233] P01009<4.2e+09

[0234] group is Group: 4 [0.12 0.62 0.25] when [0235] P02787>=2.5e+11 [0236] P02763>=4.0e+10 [0237] P02768 is 8.7e+11 to 1.7e+12

[0238] group is Group: 4 [0.20 0.80 0.00] when [0239] P02787 is 1.9e+10 to 8.4e+10 [0240] P02763<1.7e+10

[0241] group is Group: 5 [0.17 0.17 0.67] when [0242] P02787<1.9e+10 [0243] P02763<1.7e+10 [0244] P01009>=4.2e+09

[0245] group is Group: 5 [0.00 0.00 1.00] when [0246] P02787>=8.4e+10 [0247] P02763>=4.0e+10 [0248] P02768<8.7e+11

[0249] group is Group: 5 [0.00 0.00 1.00] when [0250] P02787<8.4e+10 [0251] P02763>=1.7e+10

[0252] An exemplary sample provided the following results:

TABLE-US-00005 Relative intensities in MS Gene for the corresponding Protein IDs names proteins P02768-1 ALB 6.01E+11 P01009 SERPINA1 5.62E+10 P02787 TF 9.25E+10 P02763 ORM1 1.89E+10

[0253] The decision tree (FIG. 7) classifies this sample in group 3 (IgAN). After decoding the sample it is ascertained that the sample is derived from a subject diagnosed with IgAN by other means (biopsy) and showing symptoms consistent with this condition. Further treatment confirms the diagnosis based on protein markers.

[0254] An exemplary sample provided the following results:

TABLE-US-00006 Relative intensities in MS Gene for the corresponding Protein IDs names proteins P02768-1 ALB 9.63E+11 P01009 SERPINA1  2.7E+11 P02787 TF 7.48E+10 P02763 ORM1 9.12E+10

[0255] The decision tree (FIG. 7) classifies this sample in group 5 (LN). After decoding the sample it is ascertained that the sample is derived from a subject diagnosed with LN by other means (biopsy) and showing symptoms consistent with this condition. Further treatment confirms the diagnosis based on protein markers.