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urinary proteomics

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urinary proteomics

  1. 1. Evaluation of urinary proteome and its correlation with kidney function in patients with Diabetic Nephropathy and with renal biopsies in non diabetic renal diseases. Ravi kumar M Prof. K.V. Dakshinamurty
  2. 2. • Chronic kidney disease is a devastating disease with clinical economic and ethical dimensions and is emerging as a major public health problem globally. • The incidence of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD) are growing at an alarming rate. • The most common causes of CKD are diabetic nephropathy (DN), hypertension and glomerulonephritis. • Type 2 diabetes mellitus (T2DM) is an alarming healthcare concern worldwide. T2DM is most common cause of CKD in India. • India has the largest number of diabetic patients in the world, estimated to be ∼40.9 million in the year 2007 and expected to increase to ∼69.9 million by the year 2025. • This is due to an increase in the prevalence of DM, genetic factors, life style modifications and an increase in the life span of patients with diabetes • T2DM is responsible for 30-40% of all ESRD cases. • Currently it is impossible to predict which and when diabetic patients will develop nephropathy. • Early detection of kidney injury followed by adequate therapy might prevent the progression of disease. Introduction
  3. 3. • Glomerular diseases (GD) such as MCD, MGN and FSGS are associated with proteinuria that is caused by increased glomerular permeability. • Diagnosis of the type of GD requires a invasive renal biopsy. • Although each renal disease that leads to proteinuria has specific pathogenic mechanisms (eg. immunologic, metabolic, plasma factors). • The identification of specific biomarkers in urine would greatly improve the diagnostic potential and also direct the clinical management. • • Sometimes Urine has been described as a “fluid biopsy” of the kidney. Changes in kidney function can be detected in the urinary proteome non-invasively. • The great accessibility of urine makes this fluid an ideal target for the search of disease specific markers • The pathological changes in human organs, which could be found in serum, may also be reflected and detected in urine proteome. • As a component of body fluid proteome, urine proteome is composed of the proteins filtered from blood within the glomerulus, as well as the proteins secreted from the kidneys. • There are many parameters to evaluate the renal damage such as serum creatinine, urea, uric acid, but in many instances there is no pathological change until damage is already advanced. • Clinical analysis of proteins in urine generally is based on measurement of total protein concentration and albumin, where a lot of other proteins found in urine are neglected using these methods. • 24hr urine total protein estimation can not detect or estimate individual proteins. Introduction
  4. 4. • Urinary proteomics is increasingly being used to discover potential biomarkers noninvasively. • Urinary biomarkers detecting kidney injury might allow identification of patients who would benefit from further evaluation. • With the development of proteomic techniques, urinary proteome analysis provides a fast, non-invasive diagnostic tool for patients with renal diseases. • The advent of highly sensitive proteomic technologies can identify urinary proteins associated with development of kidney diseases well before any clinically identifiable alteration. • Orbitrap mass spectrometer has a high resolving power and identifies proteins with more accuracy • In this sense, we applied proteomic technologies for an un-biased examination of urine to detect novel biomarkers that could play a critical role in disease diagnostics, treatment monitoring and prognosis. • Such data would eventually even make renal biopsy superfluous. Introduction
  5. 5. Sample collection Proteins extraction Proteomic analysis Bioinformatics analysis Proteomic discovery Less number of sample size Discovery Detect multiple biomarker candidates Biomarker discovery Validation phase Discovery phase More number of sample size validation Functional study Diagnostics and therapeutics use Confirmation of biomarker panels in test patient populations Identification The Biomarker Discovery ProcessIntroduction
  6. 6. Aim and objectives Identification and characterization of proteins excreted in the urine of diabetic nephropathy, non-diabetic glomerular diseases and renal tubular acidosis by using SDS- PAGE, LC/MS/MS approach using LTQ-Orbitrap mass spectrometer. Comparing urinary protein patterns in patients with kidney disease (Type 2 Diabetes with nephropathy, Glomerular diseases and RTA) and with controls. To prove urine is a well suited body fluid for proteomic analysis to identify predictive biomarkers.  To identify newer urinary biomarkers for CKD. To identify urinary protein patterns in CKD as diagnostic and prognostic markers
  7. 7. • PATIENT SELECTION:- • The patients in this study were recruited from the OPD and wards of Dept. of Nephrology, NIMS. Prior to the study, subjects were given an information sheet containing details about the project. Informed written consent was taken from them. • Ethical approval was obtained from the Institutional Review Board, NIMS. • Inclusion Criteria:  History of proteinuria (150mg/24hr or albumin positive by dipstick)  Type II diabetes with and without nephropathy  Non-diabetic Glomerular disorders  Renal tubular acidosis  Willing to sign Informed consent form • Exclusion Criteria:  Type I diabetes  History of hematuria  Pregnancy  Breast feeding or lactating  Any malignancy  Liver disease  Viral hepatitis Methodology
  8. 8. • CONTROL SELECTION:- • The controls in this study were recruited from the outpatients of Dept. of Nephrology, NIMS. • The controls were healthy individuals in the same population sampled at the same time. Control subjects were matched for confounding factors (age, sex and socio economic status, etc.). • Written informed consent was taken from them. Methodology
  9. 9. Controls Diabetic CKD Non- Diabetic CKD Tubular Diseases Chronic Kidney Disease (CKD) Patient group information Type 2 Diabetes Mellitus 1. DM with Normoalbuminuria 2. DM with Microalbuminuria 3. DM with Macroalbuminuria 1. Healthy Individuals 1. Minimal change disease (MCD) 2. Membranous glomerulonephritis (MGN) 3. focal segmental glomerulosclerosis (FSGS) 4. Lupus nephritis (LN) 1. Renal tubular acidosis Methodology
  10. 10. Experimental Work flow Methodology Protein identification (Sequest & Mascot) Morning second voided urine sample Centrifuge for 10min at 10,000g and filtered Protein concentration by Spin column (3Kda) & Protein estimation by Bradford assay LC-MS/MS (Orbitrap) Samples from each group were pooled, depleted by Hu-6 (Agilent) affinity column and desalted In gel Tryptic digestion Bioinformatic Analysis •Chromatogram : 90min linear gradient •Maximum Missed Cleavage Sites: 2 •Precursor Mass Tolerance: 10 ppm •Fragment Mass Tolerance: 0.8 Da •Dynamic Modification: Mithionine Oxidation/ +15.995 Da (M) •Static Modification: Cysteine Carbamidomethyl/ +57.021 Da (C) Protein separation by 1D SDS-PAGE
  11. 11. • For all the proteomic experiments, 100ml of morning second voided urine samples were collected . • 1ml (0.5g/dl) sodium azide was added to the urine to prevent bacterial growth. • centrifuged at 1000g for 10min to remove cell debris and casts and filtered the supernatant through whatman filter paper No.1. • The supernatant was stored at -800 C until further processing to prevent protein degradation. Urine sample collection Methodology
  12. 12. • Urinary protein concentration and salt removal by cutoff columns:- • Urinary proteins concentration and salts removal was done by Millipore 3KDa cutoff column. • centrifuged at 4,000 rpm. at 40C. The final concentrate was washed twice with milli-Q water to remove excess salts present in urine. • Final protein concentration was estimated by the Bradford method. • Depletion of high abundant proteins:- • Pooled urine protein samples were depleted of six major abundant plasma proteins (albumin, IgG, IgA, antitrypsin, transferrin, and haptoglobin) using the Agilent multiple affinity removal (MARS) column. • Urinary proteins (4 mg) equilibrated in Agilent buffer A was processed using an Agilent immunoaffinity column (4.6 X 100-mm) attached to HPLC. • Appropriate flows through fractions were collected and Flow Through concentrated using 5,000 kDa cutoff filters . Protein concentration was determined using the Bradford protein assay. Urine sample preparation Methodology
  13. 13. • Urinary protein samples were loaded into a gel. Electrophoresis was performed at constant voltage of 100. • Gel was stained with Coomassie blue and digitized using Vesadoc gel scanner. • The urinary proteins were fractionated on 11 cm, 4-20% gradient gels. • Protein in-gel tryptic digestion:- • The bands in the gels were sliced manually, Bands de-stained with 50% ACN/50%25mM NH4HCO3. • The gel bands immersed in 20uLof trypsin solution. The digestion was done at 370C for 20 hr. Urinary proteins separation by SDS-PAGE Methodology
  14. 14. • Nanoflow electrospray ionization and tandem mass spectrometric analysis of peptide samples were carried out using LTQ-Orbitrap Velos. • Each peptide fraction was further separated on a Bio Basic C18 nanocapillary column using a 90 min linear gradient . • Low-resolution CID-MS/MS. • Resolution in the Orbitrap system was set to r= 60,000. • The resulting fragment ions were scanned out in the low-pressure ion trap at the “normal scan rate” (33,333 amu/s). • Ion selection threshold was 500 counts for MS/MS, and the maximum allowed ion accumulation times were 500 ms for full scans and 25 ms for CID-MS/MS measurements. • Each fraction was run in duplicate resulting in generation of raw files from all the nine groups and searched against protein database. • Protein identification:- • The raw files were analyzed using Proteome Discoverer. • SEQUEST search algorithm was employed and searched against the human IPI database. • Search parameters include: 2 missed cleavage allowed; oxidation of methionine was set as a dynamic modification while carbamidomethylation of cysteine as static modification. • Precursor and fragment mass tolerance were set to 10 ppm and 0.8 Da, respectively. • The peptide and protein identifications were obtained using high peptide confidence and top one peptide rank filters. • The FDR was calculated by enabling the peptide sequence analysis using a decoy database. High confidence peptide identifications were obtained by setting a target FDR threshold of 1% at the peptide level (95% confidence). LC-MS/MS analysis:- Methodology
  15. 15. Patient selection Sex (n) Age (mean±SD) DURATION OF DIABETES (years) (mean±SD) Control subjects (10) M(4)/F(6) 50.7±8.80 Study group 1 (NA) (DM-normoalbuminuria) (10) M(5)/F(5) 52.8±11.03 8.3±7.48 Study group 2 (MIA) (DM-microalbuminuria) (10) M(5)/F(5) 51.6±12.12 9±6.56 Study group 3 (MAA) (DN-macroalbuminuria) (15) M(11)/F(4) 51.73±7.50 13.6±5.90 Results Diabetic groups
  16. 16. Patient selection Sex (n) Age (mean±SD) MCD (9) M(6)/F(3) 25.11±10.70 MGN (6) M(3)/F(3) 28.83±12.05 FSGS (11) M(7)/F(4) 31.90±20.03 LN (9) F(9) 35±13.09 RTA(8) M(5)/F(3) 30.27±13.31 Non-diabetic glomerular disease groups Results
  17. 17. STUDY GROUPS BMI (mean±SD) WHR (mean±SD) SY S BP (mean±SD) DIA BP (mean±SD) Healthy Individuals 22.94±2.73 0.93±0.01 116±5 77.77±4.40 Study group 1 (NA) 26.34±3.94 0.96±0.02 130±16.99 81±5.67 Study group 2 (MIA) 28.04±4.96 0.95±0.04 134±10.74 85±5.27 Study group 3 (MAA) 22.49±3.18 0.94±0.04 144±14.04 87.33±7.03 Anthropometric measurements & Vital signs Results Diabetic groups
  18. 18. STUDY GROUPS BMI (mean±SD) WHR (mean±SD) SY S BP (mean±SD) DIA BP (mean±SD) MCD 21.27±4.01 0.94±0.01 118.8±6.0 80±5 MGN 22.90±7.90 0.94±0.03 118.33±7.52 78.33±4.08 FSGS 22.69±5.01 0.89±0.07 134.54±19.6 84.54±8.20 LN 26.56±5.86 0.98±0.02 127.7±18.55 81.11±12.69 RTA 24.6±6.80 0.94±0.06 120.24±12.2 80.61±6.25 Anthropometric measurements & Vital signs Results Non-diabetic glomerular disease groups
  19. 19. STUDY GROUPS FPG (mg/dl) HBA1 C S.CRT (mg/dl) P.TP (g/dl) P.ALB (g/dl) 24HR (g/day) U.ALB (mg/dl) T.CHL (mg/dl) TGL (mg/dl) HDL (mg/dl) LDL (mg/dl) VLDL (mg/dl) CONT ROL 90.88 ±5.96 5.5 9±0 .31 0.89± 0.20 7.86± 0.71 3.98± 0.48 0.10± 0.02 11.8± 7.64 146.1 ±23.4 100.7 ±20.5 45.2± 7.6 80.9± 24.8 20±4. 18 NA 146.3 ±24.9 7.8 9±1 .5 1.04± 0.2 7.48± 0.40 4.48± 1.03 0.11± 0.4 10.67 ±7.34 158.3 ±32 159.5 ±99 39.4± 4 87.6± 23.1 31.7± 19.6 MIA 114.2 ±23.4 10. 2±1 .55 1.23± 0.96 7.06± 0.6 3.7±0 .5 0.49± 0.21 83.4± 30.21 177.7 ±37.7 159.9 ±75.2 46.1± 11.9 99.7± 35.02 31.9± 15.02 MAA 191.1 3±82. 46 11. 00± 2.3 5.50± 2.53 5.49± 0.84 2.78± 0.64 3.37± 1.43 918.0 ±443. 4 195.7 ±79 134.9 ±68.7 40.7± 12 118.3 ±57.4 27±1 3.7 LABORATORY INVESTIGATIONS (mean±SD) Results Diabetic groups
  20. 20. STUDY GROUPS FPG (mg/dl) S.CRT (mg/dl) S.TP (g/dl) S.ALB (g/dl) 24Hr. U.TP (g/day) T.CHL (mg/dl) TGL (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) VLDL-C (mg/dl) MCD 95.66 ±10.1 1.01±0 .5 5.23±1 .1 2.7±0. 86 3.6±1. 77 337.5± 181.0 396.3± 246.05 54.3±1 0.44 162.7± 29.24 62±6.9 MGN 91.7± 9.8 0.95±0 .2 4.81±1 .2 2.4±0. 84 3.9±2. 6 260.3± 63.3 222.8± 91.04 46.3±1 0.15 169.3± 44.1 44.7±1 8.1 FSGS 96.54 ±17.1 3 1.66±0 .75 5.97±1 .3 2.58±0 .95 2.50±1 .04 306.5± 118.8 263.8± 99.0 47.3±8 .7 204.1± 125.2 67.77± 41.8 LN 101±2 8.4 1.5±1. 4 5.9±0. 95 3.02±0 .73 1.2±1. 1 233.9± 89.5 234.3± 101.8 53.1±1 2.59 133.9± 79.4 46.9±2 0.4 RTA 92.3± 12.8 1.2±0. 6 6.92±1 .6 3.56±0 .56 0.15±0 .09 280.5± 50.4 145.6± 25.4 50.23± 11.2 85.8±1 1.5 16.8±2 .3 LABORATORY INVESTIGATIONS (mean±SD) Results Non-diabetic glomerular disease groups
  21. 21. SDS-PAGE separation of urinary proteins before and after depletion Results
  22. 22. S.no Disease Total number of proteins Unique proteins 1 Control group 274 69 2 DM group 505 169 3 MIA group 468 129 4 MAA group 320 132 6 MCD group 307 43 8 MGN group 825 462 5 FSGS group 457 101 7 LN group 546 126 9 RTA group 546 141 Urinary Proteome identified by LC-MS/MS (Orbitrap) Results
  23. 23. Cellular and Functional Classification of DN-urinary proteins Results
  24. 24. Comparison of diabetic groups urinary proteins Results
  25. 25. 84 315Control (274) DM (505) 190 100 294 Control (274) MIA (468)174 172 218 MAA (320) 102Control (274) Comparison of Diabetic groups with Controls Results
  26. 26. Comparison of intra Diabetic groups Results
  27. 27. Comparison of DN unique proteins with GD Proteins 66 1223 Non-diabetic CKD (1289) DN unique proteins (132) 66 Eg; Neutrophil defensin 1 (apoptosis) Metallothionein-1X (copper ion binding) Coactosin-like protein (defense response & actin binding ) Apolipoprotein C-II (cholesterol efflux) Isoform 2 of Pericentrin (calmodulin binding) Results
  28. 28. Non diabetic glomerular disease groups Functional classification of the identified glomerular diseases urinary proteins Results
  29. 29. Comparison of glomerular diseases urinary proteins Results
  30. 30. Comparison of glomerular disease groups with Control group Results
  31. 31. FSGS (457) MCD (307) LN (546) MGN (825) 72 Comparison between intra-glomerular diseases Results Comparison of intra glomerular diseases unique proteins control proteome Control Group proteins Intra GD unique proteins GD unique proteins MCD 271 43 40 MGN 271 462 453 FSGS 271 101 99 LN 271 126 113
  32. 32. Renal Tubular Acidosis (RTA)group Functional classification of RTA group urinary proteome and comparison with control group A B C Results
  33. 33. Comparison of RTA proteome with Control and glomerular disease groups 1289 546 271 Results
  34. 34. Venn diagram comparing of urinary proteome studies on DN P V Rao et al. 2007 (2DIGE (LC- MS/MS)approach) Elisa et al. 2008 (2DE- ESI-Q-TOF MS/MS approach) 20 7 13 14 320 54 62 Our study (SDS-PAGE;LC-MS/MS (Orbitrap) Discussion Protein identifications from the current study were compared to two other studies (P V Rao et al. 2007 and Elisa et al. 2008) that were carried out using high-resolution mass spectrometers. A total of 273 proteins were unique to this study, whereas 21 proteins were unique to P V Rao et al. and 14 were unique to Elisa et al.
  35. 35. Identified proteins with known functions on DN Protein known functions on DN Hemopexin (HPX ) Higher levels were observed in type 2 diabetes (Campenhout et al). HPX activated after certain proinflammatory conditions leading to proteinuria and glomerular alterations by altering glomerular filtration barrier (Bakker et al). VDBP VDBP deficiency inhibits the insulin secretary response (Baier et al). suggesting its possible role in the immunopathogenesis and progression of the DN (Rao et al). In DN, urinary excretion of VDBP may be due to glomerular hyperfiltration. Zinc-α2- glycoprotein (ZAG) One of the most abundant urinary protein in DN (Rao, Elisa , Kumar & Sanju et al.). stimulates lipid degradation in adipocytes. urinary ZAG might be related to the pathogenesis in glomerular basement membrane of DN. α2-HS- glycoprotein Acts as potent circulating inhibitor of calcium phosphorus precipitation. Deficiencies contributes to vascular calcification in DN (RAJNISH et al). may offer potential for future therapeutic approaches . alpha 1-AT & alpha 2-MG glomerular walls deposition was observed in DN patients. Discussion
  36. 36. Protein known functions on DN Glutathione peroxidase Lower levels was observed in early stage nephropathy . Lower levels may be due to oxidative stress occurs at an early stage of nephropathy. Factor H Could inhibit the over-expression of extracellular matrix in mesangial cells induced by high glucose, which showed the renal protective functions . Apolipoprote in A-IV In T2DM increased levels were found mainly related to hypertriglyceridemia and a lesser extent to HDL cholesterol level (Verges et al). Increased concentrations of apoA-IV in CKD suggest a metabolic role of antiatherogenic property. Gelsolin Regulates insulin secretion and also has an role in β-cell survival and could be a major player in the development of type 2 diabetes (Yermen et al). Gelsolin maintains an organized actin cytoskeleton of podocyte. Reduces the formation of malondialdehyde adducts, maintain a normal distribution of nephrin in the glomeruli and provides protection at the onset of proteinuria and plays an important renoprotective role in nephritis (Liu et al). Discussion
  37. 37. Protein known functions on DN Kininogens (KKS) In the kidney, components of the KKS regulate glomerular hemodynamics and tubular function. The suppressed Kininogens within podocytes under diabetic condition was associated with podocyte apoptosis (Kwak at al). Inter-α-trypsin inhibitor heavy chain H1 (ITIH1) Inhibits calcium oxalate crystallization, also plays a role in stabilizing hyaluronan in the renal interstitial matrix (Evan et al). ITIH1 does not have a direct role on DN but it has an anti-proteolytic activities and play an anti-inflammatory role. Vitronectin In diabetic patients with NA and MIA, plasma concentrations of vitronectin were significantly higher than those of control subjects and in diabetic patients with CRF were significantly lower than those with normal renal function (Morioka at al). Decreased because of its accumulation in sclerotic glomeruli Extracellular matrix proteins (ECM) Hyperglycemia stimulate extracellular matrix protein synthesis both in mesangial and endothelial cells. DN is characterized by excessive accumulation of extracellular matrix proteins in the kidney (Ha et al). ROS play a central role in the extracellular matrix synthesis and degradation in the glomeruli and tubulointerstitium leading to renal diseases . Discussion
  38. 38. Protein known functions on DN Myoglobin Glycation-induced modification of myoglobin induces increased formation of free radicals. Free myoglobin in circulation, if becomes glycated, may pose a serious threat by eliciting oxidative stress (Roy at al). Junctional adhesion molecule-A (JAM-A) Hyperglycemia down regulates JAM-A and increases glomerular endothelial permeability. JAM-A may regulate albumin extravasation at the glomeruli and play a role in the initiation of DN. (Hara et al). It may be a useful marker of the development of the disease. Angiotensin The high-glucose in diabetes increases Ang II production by renal cells, which stimulates TGF- 1 secretion, leading to increased synthesis and decreased degradation of matrix proteins, thus producing matrix accumulation. C-reactive protein (CRP) CRP is associated with nephropathy and GBM thickening, suggesting a role for inflammation in the pathogenesis of diabetic glomerulopathy (Vestra et al). Discussion
  39. 39. • Ghiggeri et al (1987) confirmed that albumin was a major determinant of urinary excretion in MCD. • In 1999 Cutler et al. performed 2-DE analysis and identified two LMW range proteins; α2u-globulin and glial fibrillary acidic protein and higher molecular weight range proteins were albumin, transferrin, and vitamin D-binding protein. • Except glial fibrillary acidic protein, all the remaining proteins were identified in our study. • More than 40 unique proteins were reported for MCD. Minimal change disease (MCD) Comparison with Published data on urinary proteome of non-diabetic Glomerular diseases: Discussion
  40. 40. • Branten et al. in 2004 have done a validation study on β2-Microglobulin and IgG by using ELISA. β2-Microglobulin was identified and IgG was completely depleted in our study. • In 2007, Ngai et al. performed urinary proteome profile of a rat model using 2-DIGE and MALDI-TOF-MS. Albumin, α1AT, preprohaptoglobin, liver- regeneration-related protein, transthyretin, E-cadherin, MPP7, tropomyosin, kallikrein, and α-2 globulin were identified. Among these proteins serum albumin, α -1-AT, transthyretin, E-cadherin, kallikrein, and α-2 globulin were also identified in our study. • In MGN we could generate highest proteome data (825 proteins), of which more than 600 proteins were uniquely identified in our study. Membranous glomerulonephritis (MGN) Discussion
  41. 41. • Musante et al. in 2002 identified fibulin, apo J, vitronectin, albumin, fibrinogen, and mannan-binding lectin-associated serine protease. Except apo J all remaining proteins were identified in our study. • In 2004 Thongboonkerd et al. has reported growth hormone, immunoglobulin light and heavy chains were specific to FSGS. Except GH, remaining immunoglobulin light and heavy chains were identified in our study. • Shui et al. in 2006 observed that fibronectin (FN) in blood acts as an initiator of FSGS in mouse model. serum and urine FN could serve as useful biomarkers for monitoring the progression of FSGS. This protein was also identified in our study. • Same group in 2007 , has done functional studies on osteopontin (OPN) on Balb/c mice model. They concluded that, the detection of OPN either in glomeruli or in urine could be helpful in prognosis of FSGS. We identified this protein also in our study. Focal segmental glomerulosclerosis (FSGS) Discussion
  42. 42. • Worthmann et al. in 2010, studied the glomerular expression and urinary excretion of IGFBP-1, -2, and -3 in FSGS and MCD. Local expression of IGFBPs in podocytes and endothelial cells might contribute to the pathogenesis of glomerular disease and that IGFBP- 1 and -3 are potential non-invasive markers of FSGS, differentiating against MCD. Interestingly, we found IGF-binding protein 2 and IGF-binding protein 6 proteins in FSGS but not present in MCD. • Along with these proteins we identified more than 300 proteins specific to FSGS, which were not reported previously. Discussion Focal segmental glomerulosclerosis (FSGS)
  43. 43. • Oates et al. in 2005 performed 2-DE fallowed by MALDI. identified; α-1 acid glycoprotein, zinc α-2 glycoprotein, IgG κ light chain and α-1 microglobulin. In our study, except IgG κ light chain, all remaining proteins were present. • In 2007, Rovin et al. applied SELDI-TOF MS to urinary proteome of LN. They found adiponectin and adipocyte-derived cytokine, was present in the urine. We have identified adiponectin but not adipocyte-derived cytokine. • Zhang et al. in 2008 identified hepcidin, α1-AT and albumin fragments by using SELDI-TOF MS in urine samples LN patients. Except hepcidin remaining two proteins were identified in our study. • Suzuki et al. in 2009 identified lipocalin-type prostaglandin-D synthetase, α1-acid- glycoprotein, Tf, Cp, and albumin fragments by using SELDI approach. Three proteins (α1-acid-glycoprotein, ceruloplasmin and albumin) were identified in our study. • Even for LN, more than 300 proteins were uniquely identified to our study. Lupus Nephritis (LN) No proteomic studies on RTA Discussion
  44. 44. • Identifying biomarkers for various glomerular diseases is becoming one of the most valuable and productive subfields in proteomic studies of kidney diseases. • Mischak et al. (2004 ) have identified urinary proteome patterns with CE-MS for glomerular diseases. They compared urine samples from patients with MCD, FSGS, MGN and healthy controls to identify peptide expression patterns. The classification accuracy was 71.4% for MCD and FSGS and 92.9% for MGN. • Chalmers et al. (2005) used CE-MS to classify MCD, MGN, FSGS, IgA nephropathy and DN by using urinary peptidome patterns. Some of the candidate biomarkers were identified by FT-ICR-MS as albumin fragments. • Varghese et al. (2007) profiled the urinary proteome of FSGS, LN, MGN and DN by 2- DE coupled with MALDI-TOF-MS. Identified Orosomucoid, Tf, α-1 microglobulin, zinc α-2 glycoprotein, α-1 AT, complement factor B, Hp, transthyretin, RBP, albumin, and hemopexin proteins commonly. But no single spot that could differentiate all four diseases. • David et al. in 2010 identified 634 peptides by using CE-MS in urine of CKD patients. He also found most of the proteins commonly present in all the CKDs. • We could distinguish each GD by identifying novel (unique) proteins for each group. Distinguishing between different glomerular diseases Discussion
  45. 45. Protein known functions on MCD α1-Acid glycoprotein A minimal change nephrosis was induced in rats by intraperitoneal injection of puromycin aminonucleoside. When human α1-acid glycoprotein was injected at 600 mg/kg intravenously on experimental days 6, 7, 8, and 9 into rats, urine protein output decreased significantly, and the number of podocytic foot processes increased significantly (Muchitsch et al). Its therapeutic role on nephrosis is characterized by a loss of sialic acid and a loss of negative charge, thereby leading to a loss of permselectivity. Identified proteins known functions on glomerular diseases Discussion
  46. 46. Protein known functions on MGN Enolase Renal expression of α-enolase was increased in MGN glomeruli. Podocyte α-enolase may be considered the fourth auto-antigen of idiopathic MGN in humans (Bruschi et al). α-enolase may be implicated in the pathogenesis of human MGN. Vacuolar protein The appearance of epithelial vacuolation coincides with an increased filtration of protein in IMGN (Toth et al). It may be an important histological marker when diagnosing the level of severity of glomerular lesions. β-2- microglobulin Renal outcome of urinary β2m is more related to the presence and the extent of tubulointerstitial injury than to glomerular pathology (Amanda et al). Uβ2m reflects the severity of tubulointerstitial injury and helps early stage diagnosis. Fatty acid- binding protein (FABP) Urinary FABP’s are increased in patients with iMGN. It Found in proximal and distal tubuls. The urinary excretion of FABP may be relevant to tubular stress and injury (Julia et al). FABP could therefore be an early sign of tubular stress and its urine excretion may increase before other markers do. Discussion
  47. 47. Protein known functions on FSGS Angiopoietin Overexpression of Angiopoietin in rats; induced nephrotic range proteinuria, loss of GBM charge and foot process effacement (Lionel et al). Apolipoprotein E apoE regulates growth as well as survival of mesangial cells. (Guangping et al). apoE was dysregulated in FSGS characterized by high levels in serum and urine and absence in glomeruli (Bruschi et al). β2-glycoprotein I β2GPI complexes, generated by oxidative stress, might be a novel risk factor and a diagnostic marker for the development of CKD (Kasahara et al). β2GPI may also play an important role in the development of dyslipidemias associated with the progression of CKD. Fatty acid- binding protein 1 In FSGS urinary FABP1 increased significantly and gradually along with the progression of interstitial injury (Noiri et al). Urinary FABP1 correlated with tubulointerstitial injury of fibrotic are and macrophage infiltration. These observations support that the urinary FABP1 may be a good indicator to reflect tubulointerstitial injury in FSGS. Discussion
  48. 48. Protein known functions on FSGS Fibronectin (FN) FN in blood acts as an initiator of the development of FSGS in mouse model after leaking from the blood to the basement membrane and mesangial area. FN can bind to various ECM proteins and cell receptors (Shui et ak.). Urine FN proteins could serve as useful biomarkers for monitoring the progression of FSGS. α-actinin-4 Mutant α-actinin-4 binds filamentous actin more strongly than wild- type . Regulation of the actin cytoskeleton of glomerular podocytes may be altered in FSGS patients (Kaplan et al). α-actinin-4 also plays an important role in coupling actin nucleation to assembly at cadherin- based cell-cell adhesive contacts (Vivian et al). Osteopontin (OPN) The FSGS model showed an increased expression of OPN in early glomerular epithelial hyperplasia lesion and correlated with the increases of glomerular sclerosis and urine OPN protein levels (Shui et al). Urinary OPN may acts as an injury marker for FSGS. Type-IV collagen IHC revealed accumulation of type IV collagen in the sclerotic matrix of FSGS (Razzaque et al). Discussion
  49. 49. Protein known functions on LN Histones Histone H2A, H1 + H3, H4 positively stained only with LN, but not with Non-SLE renal biopsies (Akashi et al). These results suggest that histones may play an important role in diagnosis and induction of LN. Prostaglandin- H2 D-isomerase (PGDS) PGDS has been reported to have a strong correlation levels with the severity of LN in mice . A urinary proteomic study also revealed that, PGDS present in active LN compared to non-LN glomerular diseases and healthy controls (Somparn et al). superoxide dismutase (SOD) LN was associated with higher glomerular SOD (Wang et al). Urinary SOD correlates well with GN score and renal disease activity indices, performing better than 24-hour proteinuria and BUN (Wu et al). Adiponectin Adiponectin can modulate inflammation, induce MCP-1 production and plays an essential role in remodeling the tubulointerstitium. Adiponectin levels in urine found higher in LN, compared to healthy and renal disease controls and patients with active or inactive nonrenal SLE (Rovin et al). It can acts as a prognostic and diagnostic marker for LN. Discussion
  50. 50. Protein known functions on LN Growth Factor Overexpression of IGF-I and IGFBP-2 were observed in LN glomeruli of mouse kidney (Suzuki et al). Urinary LMW proteins of GF may be used as biomarkers for LN. Ceruloplasmin (CP) Cp plays a critical physiological role in controlling the rate of iron efflux from cells with mobilizable iron in LN kidney (Suzuki et al). Urinary Cp concentrations differ only with LN activity rather than extrarenal diseases. Annexins Elevated expression of Annexin-A1 and Annexin-A2 observed in LN patients. Annexin-A1 mediated the anti-inflammatory actions of glucocorticoids in many experimental models (Ayoub et al, ) and Ao et al. found that Annexin-A2 was more prevalent in SLE patients. Discussion
  51. 51. Protein known functions on CKDs α2- Macroglobulin (α2M) Deposition of α2M in glomerular diseases indicates that α2M may play an active role in the modulation of local inflammatory reaction and tissue repair (Yang et al). Urinary excretion of α2M, may be a potential prognostic and diagnostic marker in proteinuric glomerulopathies. Topoisomeras es Topoisomerase I & II were strongly expressed in RPGN and LN, whereas MCD and MGN showed low levels in both glomerular and tubular compartments. There was also a positive correlation with serum creatinine levels and an inverse association with proteinuria and NS (Lilija et al). Assessment of topoisomerases may help in diagnosis. Because of their low nephrotoxicity, topoisomerase inhibitors might prove to be useful therapeutic agents in the treatment of renal diseases. Uromodulin In CKD uromodulin enter the renal interstitium either by basolateral secretion or urinary back-leakage in damaged tubuli and stimulates cells of the immune system and thereby causes inflammation and progression of disease (Lhotta et al). Discussion
  52. 52. Protein known functions on CKDs Aminopeptidas e N (APN) APN is located in renal microvillar membrane. Damage of tubules in primary glomerulonephritis, LN, and DN is accompanied by a release of APN. Urinary APN was found significantly elevated in glomerulonephritis patients (Birgit et al). APN might be of diagnostic value for the detection of early stage of disease. Angiotensinoge n (AGT) Urinary AGT levels were enhanced in CKD patients compared with control subjects. Urinary AGT levels were positively correlated with UAlb/UCre, excretion of sodium, UPro/UCre and S.creatinine and correlated negatively with eGFR (Hiroyuki et al). Urinary AGT was associated with declined kidney function and increased albuminuria (Katherine et al.) Cubilin Cubulin is located within the epithelium of kidney, plays a role in normal proximal tubule endocytic reabsorption of filtered albumin. Megalin/ cubilin-mediated endocytosis by proximal tubule cells of increased quantities of filtered proteins in glomerular diseases appears to evoke cell stress responses resulting in increased inflammatory cytokines leading to tubulointerstitial inflammation and fibrosis (Nakhoul et al). Cubilin may acts as marker for tubular injury in CKDs. Discussion
  53. 53. Protein known functions on tubular diseases Uteroglobin The highest values were observed in proximal tubulopathies (Ascensión et al). It seems to be more sensitive than other tubular markers due to its very low concentration in the tubular fluid, as illustrated by its absence from the urine in many of the healthy individuals. Galectins When galactins introduced to a mice, resulted in significant preservation of tubules and reduced interstitial fibrosis, with decreased myofibroblast activation and collagen I expression (Dang et al). Endoglin Interstitial expression of endoglin is associated with increased renal damage. There was also a positive correlation between mesangial cell staining for endoglin and interstitial endoglin expression (Roy et al). Endoglins may contribute to renal scarring by increased binding of TGF- β which would be marker for the initial tubular damage. Discussion
  54. 54. Protein known functions on tubular diseases AnnexinA1 (ANXA1) ANXA1 has potent anti-inflammatory effects and protects against ischemia/reperfusion injury. ANXA1 treatment reduced Tacrolimus induced tubular dilatation and macrophage infiltration (Araujo et al). Since ANXA1 is having a protecting effect of tubular injury, its urinary estimation may help in treatment monitoring. cofilin 1 Cofilin is a ubiquitous actin-binding protein required for the reorganization of actin filaments. Cofilin 1 have been demonstrated to be involved in metabolic alkalosis, polyuria, and renal tubular injury (Thongboonkerd et al). lipocalins Lipocalin are involved in inflammation and detoxification processes. Increased Urinary neutrophil gelatinase associated lipocalin (uNGAL) was observed in Patients with acute tubular necrosis (ATN) (Claudia et al). uNGAL levels may be useful in the diagnosis of ATN. When using this kidney biomarker, UTI should be ruled out because, in UTI uNGAL levels may increase in urine. Discussion
  55. 55. Protein known functions on tubular diseases Osteopontin Tubular osteopontin expression has been shown to be elevated in a variety of animal models of tubulointerstitial renal disease (Eddy et al). Osteopontin levels were closely correlated with the degree of tubulointerstitial injury (Pichler et al). Urinary Osteopontin may acts as markers of early phase tubular damage. Vimentin In normal rats , it is expressed in glomeruli and renal vasculature, but not in tubular cells (Grone et al). Renal tubular injury was documented as more vimentin-positive tubules (Nangaku et al). Vimentin could perhaps be regarded as an indicator of the regenerating and proliferating activity of tubular lesions (Gröne et al). Vimentin may be marker of tubular epithelial cell regeneration after injury and marker of tubular injury. Discussion
  56. 56. • Proteomic analysis of urine with Orbitrap (LC-MS/MS) is a fast and sensitive approach for identification of proteins. • We identified ≥ 300 proteins in each group using SDS-PAGE; LC-MS/MS (Orbitrap) approach • More than 100 proteins were found to be unique to each group of disease. • The urinary proteome differentiates healthy individuals from diabetes and DN. • It also distinguishes patients with DN from patients with non diabetic glomerular diseases. • Also distinguishes GDs form tubular diseases. Summery
  57. 57. • Some of the proteins representing specific group may lead to better understanding of disease pathophysiology. • Urine is the best source for indentifying disease specific markers, because of its non- invasive way of collection. • Proteins, we have identified were not only having role in early diagnosis but also have a role in disease pathophysiology and therapeutics. • In our study, urinary proteomics enabled identification of new biomarkers for early detection of CKD with promising clinical value. • Further validation of this proteins, may provide useful and potential biomarkers for CKD that could be applied as powerful tool in clinical diagnostics, treatment monitoring and prognosis. • Urinary biomarkers can’t immediately replace renal biopsy, which remains the gold standard for the diagnosis of glomerular diseases. However, urinary biomarkers offer many opportunities for being used as the complementary diagnostic or prognostic tool, when renal biopsy is limited or contraindicated. Summery
  58. 58. Diagnostic Assay Implementation Design and implement biomarker based clinical assay Identification Purify and identify biomarkers Validation Select biomarker with highest predictive value Discovery Detect multiple candidates Study Design Define the clinical question, Sample and workflow The Biomarker Discovery Process Summery
  59. 59. Diagnostic Assay Implementation Design and implement biomarker based clinical assay Identification Purify and identify biomarkers Validation Select biomarker with highest predictive value Discovery Detect multiple candidates Study Design Define the clinical question, Sample and workflow The Biomarker Discovery Process Summery
  60. 60. Disease threshold Time Molecular changes Biochemical changes Clinical symptoms Diseaseprogression Molecular based diagnosis always better for early diagnosis Summery
  61. 61. Limitations of the study • Could not validate the identified markers. • At this stage we cannot say that those proteins can be used as diagnostic markers unless these proteins are further validated on large scale sample size to confirm the biomarkers • Quantitation was not done for the novel molecules.
  62. 62. Future directions • The proteins we identified can be checked in different stages of diseases (eg. Lupus nephritis class I, II, III etc.). • The presence and disappearance of a particular protein following treatment may be used for response rate and prognosis of the disease. • Our urinary proteomic studies has given the roadmap for several further investigations on our identified proteins; like validation, functional studies, in which some proteins may be useful early detection biomarkers, some proteins may have role in disease pathogenesis and therapeutic interventions. • Characterization and validation of biomarkers are needed to make use into clinical practice.
  63. 63. Supporting information MS-MS m/z spectra
  64. 64. Alpha-1-acid glycoprotein YVGGQEHFAHLLILR Enolase GNPTVEVDLHTAK
  65. 65. peptide distribution sample in duplicate runs Controls run 1_1 Controls run 1_2 Controls run 2_1 Controls run 2_2 Controls run 3_1 Controls run 3_2 Controls run 4_1 Controls run 4_2
  66. 66. Controls run 5_1 Controls run 5_2 Controls run 7_1 Controls run 7_2 Controls run 6_1 Controls run 6_2
  67. 67. Normoalbuminuria run 1_1 Normoalbuminuria run 1_2 Normoalbuminuria run 2_1 Normoalbuminuria run 2_2 Normoalbuminuria run 3_1 Normoalbuminuria run 3_2 Normoalbuminuria run 4_1 Normoalbuminuria run 4_2
  68. 68. Normoalbuminuria run 5_1 Normoalbuminuria run 5_2 Microalbuminuria run 1_1 Microalbuminuria run 1_2 Microalbuminuria run 2_1 Microalbuminuria run 2_2
  69. 69. Macroalbuminuria run 1_1 Macroalbuminuria run 1_2 Macroalbuminuria run 2_1 Macroalbuminuria run 2_2
  70. 70. Accession Description ΣCoverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs Max. Score # AAs MW [kDa] calc. pI IPI00001429 Protocadherin beta-4 1.51 1 1 1 1 2.47 795 87.2 5.20 IPI00002188 Brefeldin A-inhibited guanine nucleotide- exchange protein 1 0.76 1 1 1 1 2.30 1849 208.6 5.85 IPI00003919 Isoform 1 of Glutaminyl- peptide cyclotransferase 11.08 4 3 3 9 11.65 361 40.9 6.61 IPI00004500 NEDD4-binding protein 3 3.49 1 1 1 1 2.98 544 60.4 8.10 IPI00005721 Neutrophil defensin 1 32.98 2 1 1 5 12.60 94 10.2 6.99 IPI00007800 Angiopoietin-related protein 2 9.74 2 3 3 3 5.88 493 57.1 7.53 IPI00007983 Isoform 3 of PDZ and LIM domain protein 2 8.47 1 1 1 1 2.86 366 39.2 8.90 IPI00008753 Metallothionein-1X 19.67 6 1 1 1 3.20 61 6.1 7.96 IPI00009823 Carboxypeptidase A1 12.65 5 4 4 9 13.78 419 47.1 5.76 IPI00009901 Nuclear transport factor 2 33.86 1 2 2 4 12.67 127 14.5 5.38 IPI00010105 Eukaryotic translation initiation factor 6 13.06 3 2 2 3 6.10 245 26.6 4.68 IPI00010156 Mitogen-activated protein kinase 4 6.47 1 1 1 1 2.85 587 65.9 5.45 IPI00010182 Isoform 1 of Acyl-CoA- binding protein 18.39 7 1 1 1 2.55 87 10.0 6.57 IPI00010675 Trefoil factor 2 21.71 1 2 2 4 7.97 129 14.3 5.81 IPI00011685 Collagen alpha-1(X) chain 4.71 1 1 1 1 2.60 680 66.1 9.67 IPI00012587 Phosphatidylinositol-3,4,5- trisphosphate 3- phosphatase and dual- specificity protein phosphatase PTEN 9.43 1 1 1 2 3.36 403 47.1 6.37 IPI00012760 Leptin 7.78 1 1 1 1 3.80 167 18.6 6.37 IPI00013303 Limbic system-associated membrane protein 4.73 2 1 1 2 3.99 338 37.4 6.98 Partial list of DN unique proteins, compared with C, DM, MA, GD Supplementary data
  71. 71. Partial list of Unique proteins for MCD Accession Description ΣCoverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs Max. Score # AAs MW [kDa] calc. pI IPI00553177.1 Isoform 1 of Alpha-1-antitrypsin 56.22 4 20 20 657 295.59 418 46.7 5.59 IPI00643525.1 Uncharacterized protein 17.37 11 19 19 72 49.93 1744 192.6 7.08 IPI00964635.1 31 kDa protein 24.64 7 4 4 6 10.25 276 30.8 4.81 IPI00746033.1 47 kDa protein 16.95 3 4 4 7 15.64 419 47.1 7.46 IPI00917825.2 cDNA FLJ38812 fis, clone LIVER2006469, highly similar to PLASMA SERINE PROTEASE INHIBITOR 15.36 2 4 4 6 9.51 332 37.6 9.23 IPI00969537.1 ACTA2 protein (Fragment) 10.30 12 1 3 12 9.71 330 36.8 5.35 IPI00796636.1 Hemoglobin (Fragment) 45.71 9 3 3 9 12.48 105 11.5 6.37 IPI00966427.1 Uncharacterized protein 30.52 6 3 3 6 9.08 154 17.1 7.49 IPI00641244.1 11 kDa protein 21.65 3 1 2 3 5.85 97 10.7 8.72 IPI00030385.5 cDNA FLJ13813 fis, clone THYRO1000358, moderately similar to SELENIUM-BINDING LIVER PROTEIN 10.64 9 2 2 4 6.52 235 25.9 6.68 IPI00291560.4 Isoform 1 of Arginase-1 9.32 2 2 2 5 7.44 322 34.7 7.21 IPI00554556.1 Isoform C of Protein CutA 26.28 4 2 2 2 3.23 156 16.8 5.21 IPI00888712.3 Putative beta-actin-like protein 3 9.07 26 1 2 14 11.85 375 42.0 6.33 IPI00022542.1 Rho-associated protein kinase 1 1.62 3 1 2 2 2.65 1354 158.1 5.90 IPI00784332.4 Tubulin alpha-3C/D chain 10.74 20 2 2 3 6.47 326 35.9 6.11 IPI00789173.1 Uncharacterized protein 17.52 3 2 2 8 5.31 137 14.8 5.54 IPI00645646.1 17 kDa protein 7.59 6 1 1 6 3.55 158 17.3 9.74 IPI00879148.1 18 kDa protein 8.33 4 1 1 2 3.73 168 18.4 7.99 IPI00947319.1 28 kDa protein 6.37 6 1 1 3 6.10 251 27.6 7.90 IPI00793097.1 8 kDa protein 23.88 2 1 1 1 2.46 67 7.8 6.52 IPI00004901.5 cDNA FLJ20242 fis, clone COLF6369 8.02 5 1 1 2 4.02 162 17.8 7.43 IPI00384791.1 cDNA FLJ38599 fis, clone HEART2003432, weakly similar to ENDOGLUCANASE Z 2.73 1 1 1 2 2.71 660 73.9 5.08 IPI00514248.3 cDNA FLJ53673, highly similar to Palmitoyl- protein thioesterase 1 5.60 3 1 1 1 2.77 232 26.0 6.54 IPI00967010.1 cDNA FLJ58367, weakly similar to LIM domain only protein 7 1.68 1 1 1 1 2.75 833 94.1 6.87 IPI00893729.1 Ferritin (Fragment) 14.85 2 1 1 2 4.55 101 11.2 8.78 IPI00377087.4 Gelsolin 6.91 6 1 1 4 7.04 188 20.8 4.84 IPI00640129.2 Heat shock protein 90kDa alpha (Cytosolic), class B member 1 9.09 9 1 1 2 2.64 154 18.0 6.02 IPI00217330.2 Isoform 2 of Oligoribonuclease, mitochondrial (Fragment) 3.52 2 1 1 1 2.37 199 23.2 6.09 IPI00000769.2 Kinesin-like protein KIF22 1.50 1 1 1 1 2.87 665 73.2 9.45 IPI00003944.1 Lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex, mitochondrial 2.90 1 1 1 1 2.50 482 53.5 8.51 IPI00927864.2 Malate dehydrogenase 6.93 3 1 1 4 3.69 231 24.6 7.99 IPI00302329.1 Myosin-8 0.83 1 1 1 1 2.87 1937 222.6 5.74 IPI00556271.1 NHL repeat-containing protein 3 isoform b 4.29 2 1 1 3 3.52 280 31.0 6.79
  72. 72. Partial list of Unique proteins for MGN Accession Description ΣCoverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs Max. Score # AAs MW [kDa] calc. pI IPI00843765.1 Isoform 3 of Spectrin alpha chain, brain 15.91 5 29 29 69 72.61 2452 282.1 5.34 IPI00005614.6 Isoform Long of Spectrin beta chain, brain 1 11.80 3 19 21 59 54.12 2364 274.4 5.57 IPI00006482.1 Isoform Long of Sodium/potassium- transporting ATPase subunit alpha-1 21.90 9 9 17 66 59.85 1023 112.8 5.49 IPI00657691.2 Isoform 5 of Dynamin-1 20.68 18 15 15 67 58.51 851 95.8 6.76 IPI00302840.2 Sodium/potassium-transporting ATPase subunit alpha-3 21.72 10 6 15 78 72.01 1013 111.7 5.38 IPI00013683.2 Tubulin beta-3 chain 48.89 5 7 15 208 139.04 450 50.4 4.93 IPI00909140.7 cDNA FLJ56903, highly similar to Tubulin beta-7 chain 43.75 9 1 14 193 122.63 464 52.0 4.92 IPI00084828.1 Isoform 1 of Syntaxin-binding protein 1 26.77 3 14 14 48 42.77 594 67.5 6.96 IPI00007752.1 Tubulin beta-2C chain 43.60 13 1 14 251 179.97 445 49.8 4.89 IPI00640401.1 ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide 18.93 9 2 13 44 45.16 1009 110.8 5.59 IPI00939595.1 Isoform 2 of Heat shock cognate 71 kDa protein 37.32 26 11 13 48 49.84 493 53.5 5.86 IPI00936987.1 cDNA FLJ58682, highly similar to Vesicle-fusing ATPase 15.29 2 11 11 28 30.72 739 82.0 6.81 IPI00784295.2 Isoform 1 of Heat shock protein HSP 90- alpha 17.21 11 6 11 44 42.01 732 84.6 5.02 IPI00790702.1 Beta adaptin subunit 16.25 8 6 10 35 39.70 880 98.1 5.24 IPI00386271.4 Calcium-binding mitochondrial carrier protein Aralar1 19.17 6 10 10 21 24.85 678 74.7 8.38 IPI00647102.4 cDNA FLJ42590 fis, clone BRACE3009708, highly similar to Sodium/potassium-transporting ATPase alpha-2chain 16.63 6 1 10 37 36.87 920 101.2 5.63 IPI00382844.1 Aconitase (Fragment) 17.00 2 4 9 29 28.38 600 65.3 7.84 IPI00257508.4 Dihydropyrimidinase-related protein 2 21.85 4 7 9 52 44.78 572 62.3 6.38 IPI00218207.2 Isoform 2 of Spectrin beta chain, brain 2 4.95 3 7 9 16 19.24 2365 268.1 6.11 IPI00220667.3 Isoform 4 of Hexokinase-1 11.49 10 9 9 33 36.66 905 101.0 6.96 IPI00953417.1 cDNA FLJ53012, highly similar to Tubulin beta-7 chain 57.20 1 0 8 98 71.24 243 27.4 4.49 IPI00456969.1 Cytoplasmic dynein 1 heavy chain 1 2.13 1 8 8 14 16.17 4646 532.1 6.40 IPI00251507.2 Isoform IB of Synapsin-1 16.44 2 7 8 38 37.68 669 70.0 9.88 IPI00010154.3 Rab GDP dissociation inhibitor alpha 25.73 5 4 8 35 23.00 447 50.6 5.14 IPI00657774.1 Syntaxin 1B alternative isoform deltaTM 38.63 2 8 8 22 22.88 277 31.8 5.24 IPI00853283.3 54 kDa protein 20.34 3 6 7 20 22.42 472 53.6 5.05 IPI00011932.8 Heat shock 70 kDa protein 12A 13.33 4 7 7 21 16.83 675 74.9 6.77 IPI00179415.4 Isoform 1 of Serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform 16.51 13 5 7 8 13.24 521 58.7 5.86 IPI00719600.5 Isoform 2 of Cytoplasmic FMR1- interacting protein 2 6.62 9 7 7 19 15.54 1253 145.6 7.05 IPI00215715.5 Isoform A of Calcium/calmodulin- dependent protein kinase type II subunit alpha 19.87 12 4 7 38 37.04 478 54.1 7.08 IPI00220281.3 Isoform Alpha-1 of Guanine nucleotide- binding protein G(o) subunit alpha 25.14 21 5 7 46 30.94 354 40.0 5.53 IPI00909560.1 Pyruvate kinase 17.22 13 7 7 30 38.63 511 55.9 7.50 IPI00216319.3 14-3-3 protein eta 29.67 7 4 6 25 29.45 246 28.2 4.84
  73. 73. Accession Description ΣCoverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs Max. Score # AAs MW [kDa] calc. pI IPI00008529 60S acidic ribosomal protein P2 10.43 1 1 1 1 2.96 115 11.7 4.54 IPI00884926 alpha-1-acid glycoprotein 1 precursor 58.21 2 7 11 946 705.01 201 23.5 5.11 IPI00004957 Angiopoietin-related protein 3 2.39 1 1 1 1 3.10 460 53.6 6.70 IPI00922421 Aspartate aminotransferase 9.29 2 2 2 2 6.99 366 41.0 7.18 IPI00298828 Beta-2-glycoprotein 1 37.39 2 8 8 39 41.88 345 38.3 7.97 IPI00004101 Betaine--homocysteine S- methyltransferase 1 3.69 1 1 1 2 3.22 406 45.0 7.03 IPI00647823 Cartilage glycoprotein-39 7.24 2 1 1 2 2.56 152 16.8 6.38 IPI00925214 Caspase 12 7.58 1 1 1 2 2.48 277 31.0 5.74 IPI00643860 CC2D1A protein 3.57 3 1 1 1 2.72 532 55.6 5.87 IPI00910463 cDNA FLJ50286, highly similar to Retinal dehydrogenase 1 2.82 2 1 1 1 2.76 390 42.6 5.80 IPI00909914 cDNA FLJ50884, highly similar to Beta-hexosaminidase alpha chain 5.34 3 1 1 1 3.21 356 40.9 4.96 IPI00909841 cDNA FLJ51435, moderately similar to Cofilin-1 27.68 3 2 2 4 13.22 112 12.5 6.60 IPI00909103 cDNA FLJ51462, highly similar to TGF-beta receptor type-2 2.77 3 1 1 2 3.18 542 61.4 5.40 IPI00796411 cDNA FLJ51665, highly similar to Homo sapiens plasma glutamate carboxypeptidase (PGCP), mRNA 7.16 4 2 2 3 7.96 391 43.0 6.29 IPI00964365 cDNA FLJ52002, highly similar to Annexin A5 9.82 4 1 1 3 9.02 163 18.2 5.06 IPI00910779 cDNA FLJ52141, highly similar to 14-3-3 protein gamma 11.59 5 1 2 7 8.29 207 23.5 4.82 IPI00910519 cDNA FLJ52341, highly similar to Homo sapiens sarcoglycan, epsilon (SGCE), mRNA 5.30 7 1 1 1 2.53 396 45.0 6.13 IPI00909711 cDNA FLJ53006, highly similar to Vinculin 6.42 3 1 1 1 4.52 327 36.2 6.44 IPI00908770 cDNA FLJ53063, highly similar to Tubulin beta-7 chain 6.94 18 2 2 4 5.81 317 35.9 4.93 IPI00922361 cDNA FLJ53078, highly similar to Splicing factor, arginine/serine-rich 1 11.19 1 1 1 1 2.68 143 16.2 11.09 IPI00911039 cDNA FLJ54408, highly similar to Heat shock 70 kDa protein 1 3.75 12 2 2 3 5.16 586 63.9 5.55 IPI00645500 cDNA FLJ55526, highly similar to Complement C2 14.38 13 2 8 16 18.24 723 80.1 7.46 IPI00797452 cDNA FLJ55805, highly similar to Keratin, type II cytoskeletal 4 21.10 7 4 8 148 124.08 474 51.7 6.81 Partial list of Unique proteins for FSGS
  74. 74. Partial list of Unique proteins for LN Accession Description ΣCovera ge Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs Max. Score # AAs MW [kDa] calc. pI IPI00847179.1 apolipoprotein A-IV precursor 48.48 5 18 18 141 123.09 396 45.3 5.38 IPI00082827.1 Isoform 2 of Vascular cell adhesion protein 1 15.77 4 8 8 29 43.28 647 71.2 5.17 IPI00171196.2 Isoform 3 of Keratin, type I cytoskeletal 13 16.90 15 4 8 152 119.06 420 45.8 4.88 IPI00236554.1 Isoform H14 of Myeloperoxidase 14.46 3 8 8 28 21.90 650 73.8 9.11 IPI00418446.4 Isoform 2 of Acid ceramidase 21.90 4 7 7 32 29.13 411 46.5 7.85 IPI00024095.3 Annexin A3 22.91 6 6 6 11 23.24 323 36.4 5.92 IPI00921849.1 cDNA FLJ57046, highly similar to Lysosomal alpha-glucosidase 13.98 1 2 6 30 28.61 644 71.7 6.34 IPI00658130.1 IGL@ protein 36.02 13 0 6 60 36.81 236 25.0 7.97 IPI00290085.2 Cadherin-2 8.28 7 5 5 12 11.67 906 99.7 4.81 IPI00013569.4 Isoform 1 of Pappalysin-2 3.57 2 5 5 8 14.59 1791 198.4 5.47 IPI00002147.4 Chitinase-3-like protein 1 14.62 2 4 4 6 7.83 383 42.6 8.46 IPI00375676.11 Ferritin 18.85 2 4 4 11 20.02 191 21.3 6.06 IPI00909509.1 cDNA FLJ59138, highly similar to Annexin A2 15.98 8 3 3 10 8.18 194 21.7 6.35 IPI00337687.1 Isoform 3 of Interleukin-1 receptor accessory protein 8.09 7 3 3 4 6.30 346 39.7 6.28 IPI00942625.1 phosphoinositide-3-kinase-interacting protein 1 isoform 2 23.70 5 3 3 9 9.61 173 18.5 4.91 IPI00556287.1 Putative uncharacterized protein 26.46 1 1 3 39 19.12 189 20.7 5.27 IPI00796830.1 13 kDa protein 27.19 1 1 2 43 52.93 114 13.0 6.54 IPI00872814.1 68 kDa protein 3.13 5 1 2 4 2.89 576 67.6 6.40 IPI00010420.2 ADP/ATP translocase 4 6.35 6 2 2 8 8.18 315 35.0 9.89 IPI00879608.2 alpha-2-antiplasmin isoform b precursor 8.20 5 2 2 3 5.83 427 47.9 6.10 IPI00386839.1 Amyloid lambda 6 light chain variable region SAR (Fragment) 24.14 4 1 2 7 12.07 116 12.3 5.01 IPI00299435.3 apolipoprotein F precursor 6.44 1 2 2 2 5.54 326 35.4 5.64 IPI00909703.2 cDNA FLJ51518, highly similar to Annexin A11 5.83 3 2 2 3 4.91 412 45.7 8.46 IPI00032291.2 Complement C5 1.55 1 2 2 4 7.92 1676 188.2 6.52 IPI00783862.2 Flavin reductase 12.14 2 2 2 3 5.56 206 22.1 7.65 IPI00657911.2 Gamma-globin 14.60 10 1 2 4 2.53 137 15.3 9.47 IPI00387097.1 Ig kappa chain V-I region Lay 25.00 5 1 2 10 15.84 108 11.8 7.96 IPI00829834.2 Ig kappa chain V-III region VH (Fragment) 23.28 5 1 2 9 19.27 116 12.7 5.94 IPI00382420.1 Ig lambda chain V-I region HA 18.75 11 0 2 32 25.23 112 11.9 8.91 IPI00382421.1 Ig lambda chain V-I region NEW 14.41 7 2 2 2 2.61 111 11.4 8.00 IPI00029717.1 Isoform 2 of Fibrinogen alpha chain 3.73 4 2 2 6 5.31 644 69.7 8.06 IPI00221362.3 Isoform 3 of Apoptosis-associated speck- like protein containing a CARD 20.74 3 2 2 2 3.16 135 15.0 7.44 IPI00218169.2 Isoform 7 of Mucin-1 8.63 26 2 2 6 8.40 255 27.5 6.38
  75. 75. Accession Description ΣCoverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs Max. Score # AAs MW [kDa] calc. pI IPI00556459.1 Serine/cysteine proteinase inhibitor clade G member 1 splice variant 2 (Fragment) 29.73 3 8 8 94 234.41 333 37.3 8.00 IPI00290857.3 Keratin, type II cytoskeletal 3 9.24 5 1 8 88 52.67 628 64.4 6.48 IPI00798387.1 Uncharacterized protein 17.55 10 1 7 98 113.82 433 47.9 4.84 IPI00909283.1 cDNA FLJ58514, highly similar to Cadherin- 11 16.05 4 7 7 34 95.71 779 85.5 4.83 IPI00873598.2 Uncharacterized protein 10.55 13 1 6 216 241.89 455 49.1 4.79 IPI00302944.3 Isoform 4 of Collagen alpha-1(XII) chain 2.48 5 5 5 23 38.22 2987 324.4 5.50 IPI00017704.3 Coactosin-like protein 28.17 1 4 4 9 17.15 142 15.9 5.67 IPI00910870.1 cDNA FLJ59163, highly similar to Heat shock cognate 71 kDa protein 25.24 22 2 4 13 16.33 210 23.1 8.50 IPI00307466.2 Isoform 2 of Serpin B3 14.50 3 4 4 9 16.10 338 38.5 6.74 IPI00218201.2 Isoform 2 of Macrophage colony-stimulating factor 1 13.47 4 4 4 20 27.17 438 47.9 4.91 IPI00218528.1 Isoform 1 of Plakophilin-1 8.13 3 4 4 11 19.87 726 80.4 8.97 IPI00969451.1 Tenascin XB 3.30 19 4 4 7 14.75 2149 233.2 5.12 IPI00072918.2 322 kDa protein 1.98 9 4 4 21 16.83 2976 322.0 6.90 IPI00219025.3 Glutaredoxin-1 31.13 1 3 3 12 24.05 106 11.8 8.09 IPI00940393.1 Elongation factor 1-alpha 10.89 8 3 3 16 16.33 395 42.6 9.01 IPI00431749.2 Isoform 2 of Keratin, type II cytoskeletal 80 8.77 6 2 3 43 34.77 422 47.2 5.30 IPI00965611.1 cDNA FLJ52396, highly similar to SPARC-like protein 1 8.53 5 3 3 6 9.66 539 61.7 4.84 IPI00300052.2 Keratin, type II cuticular Hb4 6.17 3 1 3 5 4.70 600 64.8 7.56 IPI00296537.4 Isoform C of Fibulin-1 6.30 8 3 3 4 12.94 683 74.4 5.24 IPI00027509.5 Matrix metalloproteinase-9 5.94 2 3 3 4 12.64 707 78.4 6.06 IPI00247063.3 Neprilysin 5.07 1 3 3 9 25.72 750 85.5 5.73 IPI00010863.5 Copper transport protein ATOX1 13.24 1 2 2 3 5.47 68 7.4 7.24 IPI00827773.1 Cold agglutinin FS-1 L-chain (Fragment) 19.47 13 2 2 17 19.16 113 12.4 8.48 IPI00792875.1 Uncharacterized protein 21.71 3 2 2 6 14.43 129 14.3 5.57 IPI00969452.1 Anthrax toxin receptor 1, isoform CRA_a 17.52 8 2 2 3 5.63 137 15.4 6.16 IPI00791498.1 17 kDa protein 14.81 2 2 2 2 2.67 162 16.8 9.32 IPI00947198.1 Uncharacterized protein 12.66 2 2 2 3 5.80 158 17.9 5.57 IPI00947111.1 Uncharacterized protein 10.04 7 2 2 5 16.11 249 28.0 7.97 IPI00910055.1 cDNA FLJ54604, highly similar to Betaine-- homocysteine S-methyltransferase 7.40 2 2 2 2 3.21 365 40.5 6.93 IPI00910734.2 cDNA FLJ53641, highly similar to Intercellular adhesion molecule 1 7.43 4 2 2 2 5.17 444 48.1 8.16 IPI00099883.4 Isoform 1 of G-protein coupled receptor family C group 5 member C 6.35 5 2 2 2 5.15 441 48.2 8.43 IPI00296141.4 Dipeptidyl peptidase 2 4.47 2 2 2 4 6.90 492 54.3 6.32 IPI00293748.3 Isoform 1 of Multiple inositol polyphosphate phosphatase 1 6.16 3 1 2 2 2.51 487 55.0 7.81 Partial list of Unique proteins for RTA
  76. 76. • The sample size was calculated based on case-control study by using Odds ratio calculation. • Parameters that were used as inputs for this study were:(Design of case -control study):- • Identified a group of individuals with the disease (cases) • Selected a group of individuals without the disease (controls) • Determined the proportion of cases who were exposed and those that were not exposed • Then done the same for control (exposed versus non-exposed) sample size calculation:-
  77. 77. 2 21 2 /2 )(p )Z)(1)(( ) 1 ( p Zpp r r n     Sample size in the case group Represents the desired power (typically .84 for 80% power). Represents the desired level of statistical significance (typically 1.96). A measure of variability (similar to standard deviation) Effect Size (the difference in proportions) r=ratio of controls to cases Formula used to calculate the sample size for case control study:-
  78. 78. • For 80% power zB = 0.84 • For 0.05 significance leve zα = 1.96 • r = 1 (equal number of cases and controls) • The proportion exposed in the control group is 40% • To get proportion of cases exposed 1)1(exp exp exp   ORp ORp p controls controls case
  79. 79. Acknowledgements • I would like thank my supervisor Prof. K.V. Dakshinamurty for the untiring help and support in any situation right from difficulties in executing the project to availing facilities. He has been a perfect mentor in true sense of words. He encouraged and challenged me throughout my academic program that helped in developing my own scientific ideas. • I would like thank my co-supervisor Prof. P.V. Rao for his excellent guidance in clinical proteomics, diabetes research. I am grateful for his cooperation in sample collection and for discussions in designing the study. I am grateful for his suggestions especially while dealing with proteomics and clinical data, in spite of his busy schedules. • I would like thank my co-supervisor Prof. Aruna k. Prayaga, for her excellent guidance in renal biopsies and other pathological aspects of my project. • I would like thank my co-supervisor Prof. K.S.S. Sai Baba for his help in biochemical aspects of the project. I am grateful for his excellent guidance throughout my work.
  80. 80. • Prof.Dr. K.V.Dakshinamurty • Prof.Dr. P.V.Rao • Prof.Dr. Aruna k. Prayaga • Prof.Dr. Malati Tangirala • Prof.Dr. K.S.S. Saibaba • Asso. Prof.Dr. Vijay kumar kutala Acknowledgements Doctoral committee members Department of Nephrology • Dr. Taduri Gangadar • Dr.Rapur Ram • Dr. Guditi Swarnalatha • Dr. Uttara das • Dr. Madav Desai • Dr. Y. Rakesh • Dr. Vasa Ramesh • Dr. BH Santhosh pai • Dr. Shyam sundar • Dr.Gajjala Divakar naidu • Dr. Sriram • Mr. Kalyan • Mr. Sudhakar • Mr. Srinivas • Nursing staff of Department of Nephrology & • Academic and Administration staff of NIMS.
  81. 81. Thank you
  82. 82. Amino Acid Masses Amino acid Mass(avg) Amino acid Mass(avg) G 57.0520 D 115.0886 A 71.0788 Q 128.1308 S 87.0782 K 128.1742 P 97.1167 E 129.1155 V 99.1326 M 131.1986 T 101.1051 H 137.1412 C 103.1448 F 147.1766 I 113.1595 R 156.1876 L 113.1595 Y 163.1760 N 114.1039 W 186.2133

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