urinary proteomics

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

• 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

• 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

• 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

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

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

• 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

• 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

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

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

• 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

• 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

• 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

• 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

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

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

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

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

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

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

SDS-PAGE separation of urinary proteins before and
after depletion
Results

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

Cellular and Functional Classification of DN-urinary proteins
Results

Comparison of diabetic groups urinary proteins
Results

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

Comparison of intra Diabetic groups
Results

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

Non diabetic glomerular disease
groups
Functional classification of the identified glomerular
diseases urinary proteins
Results

Comparison of glomerular diseases urinary proteins
Results

Comparison of glomerular disease groups with Control
group
Results

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

Renal Tubular Acidosis (RTA)group
Functional classification of RTA group urinary proteome and
comparison with control group
A B
C
Results

Comparison of RTA proteome with Control and glomerular
disease groups
1289
546
271
Results

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.

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

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

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

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

• 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

• 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

• 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

• 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)

• 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

• 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

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

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

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

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

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

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

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

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

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

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

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

• 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

• 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

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

Disease threshold
Time
Molecular
changes
Biochemical
changes
Clinical
symptoms
Diseaseprogression
Molecular based diagnosis always better for early diagnosis
Summery

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.

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.

Supporting information
MS-MS m/z spectra

Alpha-1-acid glycoprotein
YVGGQEHFAHLLILR
Enolase
GNPTVEVDLHTAK

peptide distribution sample in duplicate runs
Controls run 1_1 Controls run 1_2
Controls run 3_1 Controls run 3_2 Controls run 4_1 Controls run 4_2

Normoalbuminuria run 1_1 Normoalbuminuria run 1_2 Normoalbuminuria run 2_1 Normoalbuminuria run 2_2
Normoalbuminuria run 3_1 Normoalbuminuria run 3_2

Microalbuminuria run 1_1 Microalbuminuria run 1_2 Microalbuminuria run 2_1 Microalbuminuria run 2_2

Macroalbuminuria run 1_1 Macroalbuminuria run 1_2 Macroalbuminuria run 2_1 Macroalbuminuria run 2_2

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

Partial list of Unique proteins for MCD
Accession Description ΣCoverage
Σ#
Proteins
Σ# Unique
Peptides
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
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
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

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

Σ# Unique
Peptides
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
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
TGF-beta receptor type-2
2.77 3 1 1 2 3.18 542 61.4 5.40
Homo sapiens plasma glutamate
carboxypeptidase (PGCP), mRNA
7.16 4 2 2 3 7.96 391 43.0 6.29
Annexin A5
9.82 4 1 1 3 9.02 163 18.2 5.06
14-3-3 protein gamma
11.59 5 1 2 7 8.29 207 23.5 4.82
Homo sapiens sarcoglycan, epsilon
(SGCE), mRNA
5.30 7 1 1 1 2.53 396 45.0 6.13
Vinculin
6.42 3 1 1 1 4.52 327 36.2 6.44
Tubulin beta-7 chain
6.94 18 2 2 4 5.81 317 35.9 4.93
Splicing factor, arginine/serine-rich
1
11.19 1 1 1 1 2.68 143 16.2 11.09
Heat shock 70 kDa protein 1
3.75 12 2 2 3 5.16 586 63.9 5.55
Complement C2
14.38 13 2 8 16 18.24 723 80.1 7.46
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

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

Σ#
Unique
Peptides
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
IPI00909283.1 cDNA FLJ58514, highly similar to Cadherin-
11
16.05 4 7 7 34 95.71 779 85.5 4.83
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
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
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

• 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:-

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:-

• 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

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.

• 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.

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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