Concanavalin A-captured Glycoproteins in Healthy Human Urine

Abstract

Both the urinary proteome and its glycoproteome can reflect human health status, and more directly, functions of kidney and urinary tracts. Because the high abundance protein albumin is not N-glycosylated, the urine N-glycoprotein enrichment procedure could deplete it, and urine proteome could thus provide a more detailed protein profile in addition to glycosylation information especially when albuminuria occurs in some kidney diseases. In terms of describing the details of urinary proteins, the urine glycoproteome is even a better choice than the proteome itself. Pooled urine samples from healthy volunteers were collected and acetone-precipitated for proteins. N-Linked glycoproteins enriched with concanavalin A affinity purification were separated and analyzed by SDS-PAGE-reverse phase LC/MS/MS or two-dimensional LC/MS/MS. A total of 225 urinary proteins were identified based on two-hit criteria with reliability over 97% for each peptide. Among these proteins, 94 were identified in previous urine proteome works, 150 were annotated as glycoproteins in Swiss-Prot, and 43 were predicted as glycoproteins by NetNGlyc 1.0. A number of known biomarkers and disease-related glycoproteins were identified. Because changes in protein quantity or the glycosylation status can lead to changes in the concanavalin A-captured glycoprotein profile, specific urine glycoproteome patterns might be observed for specific pathological conditions as multiplex urinary biomarkers. Knowledge of the urine glycoproteome is important in understanding kidney and body function.

Full text

Concanavalin A-captured Glycoproteins in
Healthy Human Urine*□
S
Linjie Wang‡, Fuxin Li§, Wei Sun‡, Shuzhen Wu‡, Xiaorong Wang‡, Ling Zhang‡,
Dexian Zheng‡, Jue Wang§, and Youhe Gao‡¶
Both the urinary proteome and its glycoproteome can
reflect human health status, and more directly, functions
of kidney and urinary tracts. Because the high abundance
protein albumin is not N-glycosylated, the urine N-glyco-
protein enrichment procedure could deplete it, and urine
proteome could thus provide a more detailed protein pro-
file in addition to glycosylation information especially
when albuminuria occurs in some kidney diseases. In
terms of describing the details of urinary proteins, the
urine glycoproteome is even a better choice than the
proteome itself. Pooled urine samples from healthy vol-
unteers were collected and acetone-precipitated for pro-
teins. N-Linked glycoproteins enriched with concanavalin
A affinity purification were separated and analyzed by
SDS-PAGE-reverse phase LC/MS/MS or two-dimensional
LC/MS/MS. A total of 225 urinary proteins were identified
based on two-hit criteria with reliability over 97% for each
peptide. Among these proteins, 94 were identified in previ-
ous urine proteome works, 150 were annotated as glyco-
proteins in Swiss-Prot, and 43 were predicted as glycopro-
teins by NetNGlyc 1.0. A number of known biomarkers and
disease-related glycoproteins were identified. Because
changes in protein quantity or the glycosylation status can
lead to changes in the concanavalin A-captured glycopro-
tein profile, specific urine glycoproteome patterns might
be observed for specific pathological conditions as mul-
tiplex urinary biomarkers. Knowledge of the urine glyco-
proteome is important in understanding kidney and body
function. Molecular & Cellular Proteomics 5:560 –562,
2006.
The urinary proteome has received more and more atten-
tion in the proteomic field for its simplicity compared with
serum as well as its potential in biomarker discovery. Several
research teams have worked on profiling the healthy human
urine proteome using electrophoresis and/or liquid chroma-
tography followed by mass spectrometry identification (1–5).
The fact that some proteins were observed at higher molec-
ular weight than their theoretical ones on SDS-PAGE (5) con-
firmed that the post-translational modifications including gly-
cosylation exist extensively in urinary proteins. Glycosylation,
a common and important protein post-translational modifica-
tion, is involved in many biological processes such as cell
adhesion, signal transduction, immune response, and inflam-
matory reaction (6). More than half of all proteins are thought
to be glycoproteins, and they will undergo changes in quality
and quantity along with the changes in different physiological
and pathological states. Both the urine proteome and its
glycoproteome can reflect human health status, especially
functions of kidney and urinary tracts. In some kidney dis-
eases, albuminuria usually occurs. Because high abundance
albumin is not N-glycosylated, urine N-glycoprotein enrich-
ment procedure could deplete it, and the urine glycoproteome
could provide a more detailed protein profile in addition to the
glycosylation information. In terms of describing details of
urinary proteins, the urine glycoproteome is even a better
choice than the proteome itself. In this study our goal was to
profile the N-linked glycoproteome in normal human urine.
In the present study, pooled urine samples from healthy
males and females were collected, and urine proteins were
acetone-precipitated as described previously (5). Lectin con-
canavalin A (Con A)
1
was chosen to enrich N-linked glycopro-
teins for its broader specificity and higher affinity. Con A
affinity chromatography was performed according to previous
protocols (7). The eluted proteins (100
␮
g loaded each time)
from Con A-agarose were separated and analyzed by two
approaches: 1) SDS-PAGE-RPLC/MS/MS (SDS-PAGE, in-gel
digestion, and peptide extraction followed by RPLC/MS/MS)
and 2) two-dimensional LC/MS/MS (protein mixture digestion
followed by strong cation exchange-RPLC/MS/MS). Proteins
were reduced with dithiothreitol and alkylated with iodoacet-
amide before tryptic digestion. Glycosidase PNGase F was
also added to remove N-glycans from glycoproteins during
the digestion. All peptides were analyzed by an LCQ-DECA
XP
plus
electrospray ion trap mass spectrometer (ThermoFinni-
gan, San Jose, CA). Ions were detected in a survey scan from
400 to 1500 amu (three microscans) followed by five data-de-
pendent MS/MS scans (five microscans each; isolation width,
3 amu; 35% normalized collision energy; dynamic exclusion
From the ‡Proteomics Research Center, National Key Laboratory of
Medical Molecular Biology, Institute of Basic Medical Sciences, Chi-
nese Academy of Medical Sciences/Peking Union Medical College,
Beijing 100005 and §Laboratory of Complex Systems and Intelligence
Science, Institute of Automation, Chinese Academy of Sciences,
Beijing 100080, China
Received, November 16, 2005
Published, MCP Papers in Press, November 29, 2005, DOI
10.1074/mcp.D500013-MCP200
1
The abbreviations used are: Con A, concanavalin A; AMASS,
advanced mass spectrum scanner; RP, reverse phase.
Dataset
© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.560 Molecular & Cellular Proteomics 5.3
This paper is available on line at http://www.mcponline.org

for 3 min) in a completely automated fashion. Both ap-
proaches were run twice in parallel. All MS/MS spectra were
searched using Bioworks 3.1 against the database ipi.hu-
man.v3.05 (8) with enzyme constraints and with a static mod-
ification of ⫹57 Da on cysteine residue and a differential
modification of ⫹1 Da on asparagine residue. The precursor
ion mass tolerance was 1.40 Da, and the fragment ion mass
tolerance was 1.50 Da. We used SEQUEST criteria as follows:
⌬Cn ⱖ0.1; Rsp ⫽1; Xcorr ⱖ1.9 for ⫹1 charged peptides;
Xcorr ⱖ2.2 for ⫹2 with fully or partially tryptic end; Xcorr ⱖ
3.0 for ⫹2 without regard to the end residues; Xcorr ⱖ3.75 for
⫹3. Then AMASS version 1.13 (available at www.proteomics-
cams.com) was used to filter the SEQUEST results with three
parameters: MatchPct ⱖ60, Cont ⱖ40, and Rscore ⬍2.6 (9,
10). Proteins with two or more spectra approved by AMASS
were accepted as positive identifications. Reverse database
searching was used to estimate the false positive rate. The
false positive rate ⫽peptide number in reverse database/
peptide number in forward database ⫻100%, and the final
average false positive rate was 2.76% for SEQUEST/AMASS-
filtered positive peptides.
In total, 225 proteins were identified (excluding keratins)
based on two or more positive peptides with a reliability of
more than 97% for each (Supplemental Table 1). For 142
proteins recognized with at least two independent peptides,
the reliability of protein identification can reach more than
99.9%. Even for the other 83 proteins identified by single
peptide with multiple hits, the reliability was still more than
97%. 94 proteins were identified in previous urine proteome
studies (1–5). 43 proteins were also identified in serum N-
glycoproteome (11–13). 150 were annotated as glycoproteins
or subunits of glycoproteins in Swiss-Prot, and 43 were an-
notated as potential glycoproteins predicted by NetNGlyc
1.0.
2
22 proteins had potential N-linked glycan binding sites
but no signal peptides, which means they are unlikely to be
N-glycosylated. 10 proteins had no N-glycosylation sites.
Those 32 identified non-N-glycosylated proteins might either
be associated with the captured N-glycoproteins or nonspe-
cifically bind to Con A. For example, albumin can be associ-
ated with many proteins in the list as one of its significant
functions is protein transportation. The proteins were catego-
rized based on their subcellular localizations. For the known
proteins, their subcellular localizations were determined by
the Swiss-Prot or Gene Ontology annotations. For all the
others, localizations were predicted based on those of similar
known proteins by BLAST or PSORT II for proteins that had no
similar known proteins in BLAST search. There were 101
extracellular, 67 membrane, 32 lysosomal, 22 cytoplasmic,
one cytoskeletal, and two nuclear proteins. They are mainly
composed of enzymes, enzyme inhibitors, receptors, immu-
noglobulin/complements, and apolipoproteins that participate
in many biological processes such as immune response and
inflammation, blood coagulation, cell adhesion, signal trans-
duction, and cleansing of the aged or abnormal proteins in
lysosome. A number of known biomarkers and disease-re-
lated glycoproteins were identified, such as prostate-specific
antigen, cadherins, and cathepsins. To enrich the details of
the urinary Con A-captured glycoprotein profile, 119 proteins
identified by one hit were listed in Supplemental Table 2. In
total there were 334 proteins identified with more than 97%
reliability. The peptide hit numbers of all the proteins were
also included in Supplemental Tables 1 and 2 to serve as a
rough estimate of the protein quantity in the sample.
Both the changes in protein quantity and the glycosylation
status of glycoproteins can be reflected in the profiled
changes of Con A-captured glycoproteins. Specific patterns
might be observed in the Con A-captured glycoproteins for
specific pathological conditions as “multiplex urinary biomar-
kers.” Hence we believe profiling Con A-captured glycopro-
teins in healthy human urine will be a very useful reference for
future applications of the urine glycoproteome. We expect
that with the development of more efficient enrichment tech-
niques and sophisticated mass spectrometers more glyco-
proteins will be in the detectable range.
Acknowledgments—We thank Dr. Mark A. Knepper (Laboratory of
Kidney and Electrolyte Metabolism, NHLBI, National Institutes of
Health, Bethesda, MD) for helpful discussion. We also thank Dr.
Weizhi Chen and Peter Marshall for critical reading of the manuscript.
* This work was supported in part by National Basic Research
Program Grant 2004CB520804 and by National Natural Science
Foundation Grants 30270657, 30230150, and 3037030. The costs of
publication of this article were defrayed in part by the payment of
page charges. This article must therefore be hereby marked “adver-
tisement” in accordance with 18 U.S.C. Section 1734 solely to indi-
cate this fact.
□SThe on-line version of this article (available at http://www.
mcponline.org) contains supplemental material.
¶ To whom correspondence should be addressed: Inst. of Basic
Medical Sciences, Chinese Academy of Medical Sciences/Peking
Union Medical College, 5 Dong Dan San Tiao, Beijing 100005, China.
Tel.: 86-010-6521-2284; Fax: 86-010-6521-2284; E-mail: gaoyouhe@
pumc.edu.cn.
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