Nucleic Acids Research Advance Access published online on October 31, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn849
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sys-BodyFluid: a systematical database for human body fluid proteome research
Su-Jun Li,
Mao Peng,
Hong Li,
Bo-Shu Liu,
Chuan Wang,
Jia-Rui Wu,
Yi-Xue Li* and
Rong Zeng
Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
* To whom correspondence should be addressed. Tel: +86 21 54920089; Fax: +86 21 54920143; Email: yxli{at}sibs.ac.cn Correspondence may also be addressed to Rong Zeng. Tel: +86 21 54920170; Fax: +86 21 54920171; Email: zr{at}sibs.ac.cn
Received August 13, 2008. Revised September 29, 2008. Accepted October 16, 2008.
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ABSTRACT
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Recently, body fluids have widely become an important target
for proteomic research and proteomic study has produced more
and more body fluid related protein data. A database is needed
to collect and analyze these proteome data. Thus, we developed
this web-based body fluid proteome database Sys-BodyFluid. It
contains eleven kinds of body fluid proteomes, including plasma/serum,
urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid,
synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid,
human milk and amniotic fluid. Over 10 000 proteins are presented
in the Sys-BodyFluid. Sys-BodyFluid provides the detailed protein
annotations, including protein description, Gene Ontology, domain
information, protein sequence and involved pathways. These proteome
data can be retrieved by using protein name, protein accession
number and sequence similarity. In addition, users can query
between these different body fluids to get the different proteins
identification information. Sys-BodyFluid database can facilitate
the body fluid proteomics and disease proteomics research as
a reference database. It is available at
http://www.biosino.org/bodyfluid/.
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INTRODUCTION
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In the post-genome era, proteomic technology has rapidly developed
to be a powerful platform for the research of human physiology.
It can be applied for identifying potential novel biomarkers
for prognosis, diagnosis and therapeusis (
1,
2). And in recent
years it is shown that body fluids have become one of the important
targets for proteomics research (
3). The body fluids include
a wide variety of compositions like plasma/serum, urine, cerebrospinal
fluid, saliva, bronchoalveolar lavage fluid, synovial fluid,
nipple aspirate fluid, tear fluid, amniotic fluid and so on.
Analysis of the protein composition in body fluids can help
to understand human disease proteomics better. Hu
et al.,(
3)
reviewed the body fluids research advances in proteome analysis
and focused on its applications to human disease biomarker discovery.
The importance of body fluids has also been appreciated by recent
proteomics work (
4). The database ‘MAPU: Max-Planck Unified
database of organellar, cellular, tissue and body fluid’
(
5) published in 2007 exhibit the close attention of the proteome
researchers to the body fluids. The MAPU database stores the
data from their own lab and contains several kinds of body fluids,
such as urine and tear fluid. To collect more curated proteomics
data in the related literatures of the body fluids and provide
comprehensive protein annotation, as well as explore the relationships
between the different body fluids, we constructed this database
Sys-BodyFluid. Abundant proteomics data and in-depth protein
annotation make Sys-BodyFluid to be a reference database for
body fluid and clinical proteomics research.
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DATABASE CONSTRUCTION
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Sys-BodyFluid database was implemented through MySQL relational
database (
http://www.mysql.com). The web graphical user interface
was constructed using JavaServer Pages technology (
http://java.sun.com/products/jsp/).
The manually curated body fluid protein data in the Sys-BodyFluid
were imported to MySQL database by JAVA program. The protein
annotation data were downloaded from International Protein Index
(IPI) database, Gene Ontology (
6), GOA database (
7) and KEGG
(
8) pathway database. Open source JAVA library named as JFreeChart
(
http://www.jfree.org/jfreechart/) distributed under LGPL was
adopted to plot the image of the statistics data in the web.
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DATA SOURCE AND DATABASE CONTENTS
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We searched PubMed and manually curated 50 related peer-review
publications published online before May 2008. The primary sequences
of the proteins were retrieved by the original ID from their
corresponding databases in these publications. Due to the database
updates, the protein sequences reported in the literatures may
have changed or depleted in the current databases. Therefore,
these protein sequences were manually validated before importing
into the database. Each protein was mapping to the IPI database
to uniform the protein ID in Sys-BodyFluid by blasting these
protein sequences against the database (Human IPI Version 3.44)
(the
E-value cutoff was set to 10
–8, the BLAST-HSP coverage
was >0.9). Thus, each of the protein has a corresponding
IPI ID in the Sys-BodyFluid database. The total unique proteins
and paper numbers of the 11 kinds of body fluids in our database
are summarized in
Table 1. For example, there are 13 papers
and 7748 proteins about the plasma/serum research in our database.
Users can obtain this statistical information about the Sys-BodyFluid
database in the ‘DATABASE’ web link in the website
http://www.biosino.org/bodyfluid.
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DATA AVAILABILITY
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The Sys-BodyFluid is accessed from graphical web interface (
http://www.biosino.org/bodyfluid/)
and the data are available for download through the ‘DOWNLOAD’
link in the website as a text file. Users could specify their
interested body fluid data to download.
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DATABASE UTILITY
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Sys-BodyFluid provides users the current database data statistics
of different body fluids through the DATABASE link for the paper
number and the unique protein number (DATABASE Link). As shown
in
Figure 1, Sys-BodyFluid offers users an optimal search function,
including searching by protein ID, name and sequence similarity
(SEARCH link,
Figure 1A). The comprehensive browse option allows
users to explore comparison analysis between two or more different
body fluids data (Browse link,
Figure 1B). For each protein
in Sys-BodyFluid, we provide detailed annotation information,
including protein description, involved body fluids, paper information,
domain, Gene Ontology, pathway, sequence and so on (
Figure 1C).
Users can choose their interested body fluid to browse or download.
Web page describing the body fluid provides users particular
information. Furthermore, the availability of pathway analysis
will assist users to investigate the difference between body
fluids through involved metabolism and signal transduction pathway
(Pathway link,
Figure 1D). Proteins in our database are labeled
with ‘red’ color. The body fluid number and paper
number the proteins involved in are also showed in the web page.
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Figure 1. The web graphical user interface of Sys-BodyFluid database. (A) Search part and option. Users could search protein by protein ID, protein name and sequence similarity. (B) Browse part. Database allows user browse protein by their interested body fluid and interested paper. Protein existed in two body fluids could also be viewed and multi body fluids can be investigated. (C) Protein annotation part. There is detailed information in the database for each protein, including description, domain, Gene Ontology term, sequence and so on. (D) Pathway part. The proteins (colored by red) in different body fluids and their involved pathway are shown in pathway link. Proteins in our database are labeled with ‘red’ color. The body fluid number and paper number are also showed in the web page.
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RESULTS AND DISCUSSION
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To get more comprehensive understanding of the relationship
between body fluids, we compared the proteins composition in
different body fluids. The result is shown in
Figure 2A. There
are 2928 proteins presented in at least two body fluids and
1359 proteins exist in at least three body fluids. Only 15 proteins
exist in total 11 body fluids. For these 2928 proteins, GO annotation
information were obtained and enrichment analysis was performed
using BiNGO (
9) and Cytoscape (
10). Each node in
Figure 2B represents
a GO term. The node's size is scaled by protein number and node's
color shows
P-value of the enrichment analysis. The edge denotes
the parent–children relationship between nodes. From this
analysis, it is shown that some molecular functions like ‘protein
binding’ and ‘enzyme regulator activity’ are
over-presented in this dataset, as well as the biological process
like ‘transport’ and ‘secretion’. Cellular
component like ‘extracellular region’ is significantly
enriched.
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[Download PowerPoint slide]
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Figure 2. (A) The data comparison in different body fluids. There are 2928 proteins presented in at least two body fluids and 1359 proteins existed in at least three body fluids. Only 15 proteins exist in total 11 body fluids. (B) Gene Ontology annotation statistical analysis for the 2928 proteins existing in at least two body fluids.
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Human body fluids proteome analysis is still a challenge because
dynamic range and the complexity of the body fluids protein
composition. It is important to construct a body fluid reference
database dedicated to biomarker discovery research. Previous
work like MAPU is a great effort to integrate the data from
their own lab and aim to provide a ‘gold standard’
reference proteome database. It is still necessary to refer
to other proteomic literature data. For this reason, our database
Sys-BodyFluid was build as a complementary database to the MAPU
and aimed to provide users more information about the body fluids
accompanied by protein abundant annotations. The relationship
between different body fluids was also focused in our database.
Users can access this database by
http://www.biosino.org/bodyfluid.
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PERSPECTIVES
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As more and more body fluid proteome data have been produced
recently, it is planned to update Sys-BodyFluid database every
6 months. New body fluid proteome data produced during the time
will be added to our database. Furthermore, more annotation
information like protein interaction data will also be included.
In the future, we will collect more body fluid proteome data
in the disease proteomics research, for example, cancer and
diabetes proteome data. If possible, tissue proteomics data
will be also included to look into the crosstalk between the
tissue protein and the body fluid protein.
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FUNDING
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Basic Research Foundation (2006CB910700); CAS Project (KSCX2-YW-R-106,
KSCX2-YW-R-112, KGCX1-YW-13); High-technology Project (2007AA02Z334).
Funding for open access charge: CAS project KSCX2-YW-R-106.
Conflict of interest statement. None declared.
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Footnotes
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The authors wish it to be known that, in their opinion, the
first three authors should be regarded as joint First Authors
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REFERENCES
|
|---|
- Aebersold R., Mann M. Mass spectrometry-based proteomics. Nature (2003) 422:198–207.[CrossRef][Web of Science][Medline]
- Binz P.A., Hochstrasser D.F., Appel R.D. Mass spectrometry-based proteomics: current status and potential use in clinical chemistry. Clin. Chem. Lab. Med. (2003) 41:1540–1551.[CrossRef][Web of Science][Medline]
- Hu S., Loo J.A., Wong D.T. Human body fluid proteome analysis. Proteomics (2006) 6:6326–6353.[CrossRef][Web of Science][Medline]
- Fusaro V.A., Stone J.H. Mass spectrometry-based proteomics and analyses of serum: a primer for the clinical investigator. Clin. Exp. Rheumatol. (2003) 21:S3–S14.[Web of Science][Medline]
- Zhang Y., Zhang Y., Adachi J., Olsen J.V., Shi R., de Souza G., Pasini E., Foster L.J., Macek B., Zougman A., et al. MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes. Nucleic Acids Res. (2007) 35:D771–D779.[Abstract/Free Full Text]
- Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. (2000) 25:25–29.[CrossRef][Web of Science][Medline]
- Camon E., Magrane M., Barrell D., Lee V., Dimmer E., Maslen J., Binns D., Harte N., Lopez R., Apweiler R. The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res. (2004) 32:D262–D266.[Abstract/Free Full Text]
- Kanehisa M., Araki M., Goto S., Hattori M., Hirakawa M., Itoh M., Katayama T., Kawashima S., Okuda S., Tokimatsu T., et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. (2008) 36:D480–D484.[Abstract/Free Full Text]
- Maere S., Heymans K., Kuiper M. BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics (2005) 21:3448–3449.[Abstract/Free Full Text]
- Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. (2003) 13:2498–2504.[Abstract/Free Full Text]
- Jin W.H., Dai J., Li S.J., Xia Q.C., Zou H.F., Zeng R. Human plasma proteome analysis by multidimensional chromatography prefractionation and linear ion trap mass spectrometry identification. J. Proteome Res. (2005) 4:613–619.[CrossRef][Web of Science][Medline]
- Anderson N.L., Polanski M., Pieper R., Gatlin T., Tirumalai R.S., Conrads T.P., Veenstra T.D., Adkins J.N., Pounds J.G., Fagan R., et al. The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol. Cell. Proteomics (2004) 3:311–326.[Abstract/Free Full Text]
- Barnea E., Sorkin R., Ziv T., Beer I., Admon A. Evaluation of prefractionation methods as a preparatory step for multidimensional based chromatography of serum proteins. Proteomics (2005) 5:3367–3375.[CrossRef][Web of Science][Medline]
- Gong Y., Li X., Yang B., Ying W., Li D., Zhang Y., Dai S., Cai Y., Wang J., He F., et al. Different immunoaffinity fractionation strategies to characterize the human plasma proteome. J. Proteome Res. (2006) 5:1379–1387.[CrossRef][Web of Science][Medline]
- He P., He H.Z., Dai J., Wang Y., Sheng Q.H., Zhou L.P., Zhang Z.S., Sun Y.L., Liu F., Wang K., et al. The human plasma proteome: analysis of Chinese serum using shotgun strategy. Proteomics (2005) 5:3442–3453.[CrossRef][Web of Science][Medline]
- Liu X., Valentine S.J., Plasencia M.D., Trimpin S., Naylor S., Clemmer D.E. Mapping the human plasma proteome by SCX-LC-IMS-MS. J. Am. Soc. Mass Spectrom. (2007) 18:1249–1264.[CrossRef][Web of Science][Medline]
- Omenn G.S., States D.J., Adamski M., Blackwell T.W., Menon R., Hermjakob H., Apweiler R., Haab B.B., Simpson R.J., Eddes J.S., et al. Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics (2005) 5:3226–3245.[CrossRef][Web of Science][Medline]
- Sennels L., Salek M., Lomas L., Boschetti E., Righetti P.G., Rappsilber J. Proteomic analysis of human blood serum using peptide library beads. J. Proteome Res. (2007) 6:4055–4062.[Web of Science][Medline]
- Tanaka Y., Akiyama H., Kuroda T., Jung G., Tanahashi K., Sugaya H., Utsumi J., Kawasaki H., Hirano H. A novel approach and protocol for discovering extremely low-abundance proteins in serum. Proteomics (2006) 6:4845–4855.[CrossRef][Web of Science][Medline]
- Tirumalai R.S., Chan K.C., Prieto D.A., Issaq H.J., Conrads T.P., Veenstra T.D. Characterization of the low molecular weight human serum proteome. Mol. Cell. Proteomics (2003) 2:1096–1103.[Abstract/Free Full Text]
- Tu C.J., Dai J., Li S.J., Sheng Q.H., Deng W.J., Xia Q.C., Zeng R. High-sensitivity analysis of human plasma proteome by immobilized isoelectric focusing fractionation coupled to mass spectrometry identification. J. Proteome Res. (2005) 4:1265–1273.[CrossRef][Web of Science][Medline]
- Valentine S.J., Plasencia M.D., Liu X., Krishnan M., Naylor S., Udseth H.R., Smith R.D., Clemmer D.E. Toward plasma proteome profiling with ion mobility-mass spectrometry. J. Proteome Res. (2006) 5:2977–2984.[CrossRef][Web of Science][Medline]
- Zhou M., Prieto D.A., Lucas D.A., Chan K.C., Issaq H.J., Veenstra T.D., Conrads T.P. Identification of the SELDI ProteinChip human serum retentate by microcapillary liquid chromatography-tandem mass spectrometry. J. Proteome Res. (2006) 5:2207–2216.[CrossRef][Web of Science][Medline]
- Denny P., Hagen F.K., Hardt M., Liao L., Yan W., Arellanno M., Bassilian S., Bedi G.S., Boontheung P., Cociorva D., et al. The proteomes of human parotid and submandibular/sublingual gland salivas collected as the ductal secretions. J. Proteome Res. (2008) 7:1994–2006.[CrossRef][Web of Science][Medline]
- Fang X., Yang L., Wang W., Song T., Lee C.S., DeVoe D.L., Balgley B.M. Comparison of electrokinetics-based multidimensional separations coupled with electrospray ionization-tandem mass spectrometry for characterization of human salivary proteins. Anal. Chem. (2007) 79:5785–5792.[Medline]
- Guo T., Rudnick P.A., Wang W., Lee C.S., Devoe D.L., Balgley B.M. Characterization of the human salivary proteome by capillary isoelectric focusing/nanoreversed-phase liquid chromatography coupled with ESI-tandem MS. J. Proteome Res. (2006) 5:1469–1478.[CrossRef][Web of Science][Medline]
- Ramachandran P., Boontheung P., Xie Y., Sondej M., Wong D.T., Loo J.A. Identification of N-linked glycoproteins in human saliva by glycoprotein capture and mass spectrometry. J. Proteome Res. (2006) 5:1493–1503.[CrossRef][Web of Science][Medline]
- Vitorino R., Lobo M.J., Ferrer-Correira A.J., Dubin J.R., Tomer K.B., Domingues P.M., Amado F.M. Identification of human whole saliva protein components using proteomics. Proteomics (2004) 4:1109–1115.[CrossRef][Web of Science][Medline]
- Walz A., Stuhler K., Wattenberg A., Hawranke E., Meyer H.E., Schmalz G., Bluggel M., Ruhl S. Proteome analysis of glandular parotid and submandibular-sublingual saliva in comparison to whole human saliva by two-dimensional gel electrophoresis. Proteomics (2006) 6:1631–1639.[CrossRef][Web of Science][Medline]
- Wilmarth P.A., Riviere M.A., Rustvold D.L., Lauten J.D., Madden T.E., David L.L. Two-dimensional liquid chromatography study of the human whole saliva proteome. J. Proteome Res. (2004) 3:1017–1023.[CrossRef][Web of Science][Medline]
- Xie H., Rhodus N.L., Griffin R.J., Carlis J.V., Griffin T.J. A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry. Mol. Cell. Proteomics (2005) 4:1826–1830.[Abstract/Free Full Text]
- Adachi J., Kumar C., Zhang Y., Olsen J.V., Mann M. The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol. (2006) 7:R80.[CrossRef][Medline]
- Castagna A., Cecconi D., Sennels L., Rappsilber J., Guerrier L., Fortis F., Boschetti E., Lomas L., Righetti P.G. Exploring the hidden human urinary proteome via ligand library beads. J. Proteome Res. (2005) 4:1917–1930.[CrossRef][Web of Science][Medline]
- Khan A., Packer N.H. Simple urinary sample preparation for proteomic analysis. J. Proteome Res. (2006) 5:2824–2838.[CrossRef][Web of Science][Medline]
- Oh J., Pyo J.H., Jo E.H., Hwang S.I., Kang S.C., Jung J.H., Park E.K., Kim S.Y., Choi J.Y., Lim J. Establishment of a near-standard two-dimensional human urine proteomic map. Proteomics (2004) 4:3485–3497.[CrossRef][Web of Science][Medline]
- Pieper R., Gatlin C.L., McGrath A.M., Makusky A.J., Mondal M., Seonarain M., Field E., Schatz C.R., Estock M.A., Ahmed N., et al. Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots. Proteomics (2004) 4:1159–1174.[CrossRef][Web of Science][Medline]
- Ru Q.C., Katenhusen R.A., Zhu L.A., Silberman J., Yang S., Orchard T.J., Brzeski H., Liebman M., Ellsworth D.L. Proteomic profiling of human urine using multi-dimensional protein identification technology. J. Chromatogr. A (2006) 1111:166–174.[CrossRef][Web of Science][Medline]
- Spahr C.S., Davis M.T., McGinley M.D., Robinson J.H., Bures E.J., Beierle J., Mort J., Courchesne P.L., Chen K., Wahl R.C., et al. Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry. I. Profiling an unfractionated tryptic digest. Proteomics (2001) 1:93–107.[CrossRef][Web of Science][Medline]
- Sun W., Li F., Wu S., Wang X., Zheng D., Wang J., Gao Y. Human urine proteome analysis by three separation approaches. Proteomics (2005) 5:4994–5001.[CrossRef][Web of Science][Medline]
- Zerefos P.G., Vougas K., Dimitraki P., Kossida S., Petrolekas A., Stravodimos K., Giannopoulos A., Fountoulakis M., Vlahou A. Characterization of the human urine proteome by preparative electrophoresis in combination with 2-DE. Proteomics (2006) 6:4346–4355.[CrossRef][Web of Science][Medline]
- Ogata Y., Charlesworth M.C., Higgins L., Keegan B.M., Vernino S., Muddiman D.C. Differential protein expression in male and female human lumbar cerebrospinal fluid using iTRAQ reagents after abundant protein depletion. Proteomics (2007) 7:3726–3734.[CrossRef][Web of Science][Medline]
- Ogata Y., Charlesworth M.C., Muddiman D.C. Evaluation of protein depletion methods for the analysis of total-, phospho- and glycoproteins in lumbar cerebrospinal fluid. J. Proteome Res. (2005) 4:837–845.[CrossRef][Web of Science][Medline]
- Pan S., Wang Y., Quinn J.F., Peskind E.R., Waichunas D., Wimberger J.T., Jin J., Li J.G., Zhu D., Pan C., et al. Identification of glycoproteins in human cerebrospinal fluid with a complementary proteomic approach. J. Proteome Res. (2006) 5:2769–2779.[CrossRef][Web of Science][Medline]
- Wenner B.R., Lovell M.A., Lynn B.C. Proteomic analysis of human ventricular cerebrospinal fluid from neurologically normal, elderly subjects using two-dimensional LC-MS/MS. J. Proteome Res. (2004) 3:97–103.[CrossRef][Web of Science][Medline]
- Zhang J., Goodlett D.R., Peskind E.R., Quinn J.F., Zhou Y., Wang Q., Pan C., Yi E., Eng J., Aebersold R.H., et al. Quantitative proteomic analysis of age-related changes in human cerebrospinal fluid. Neurobiol. Aging (2005) 26:207–227.[CrossRef][Web of Science][Medline]
- Zougman A., Pilch B., Podtelejnikov A., Kiehntopf M., Schnabel C., Kumar C., Mann M. Integrated analysis of the cerebrospinal fluid peptidome and proteome. J. Proteome Res. (2008) 7:386–399.[CrossRef][Web of Science][Medline]
- Fung K.Y., Glode L.M., Green S., Duncan M.W. A comprehensive characterization of the peptide and protein constituents of human seminal fluid. Prostate (2004) 61:171–181.[CrossRef][Web of Science][Medline]
- Pilch B., Mann M. Large-scale and high-confidence proteomic analysis of human seminal plasma. Genome Biol. (2006) 7:R40.[CrossRef][Medline]
- Cho C.K., Shan S.J., Winsor E.J., Diamandis E.P. Proteomics analysis of human amniotic fluid. Mol. Cell. Proteomics (2007) 6:1406–1415.[Abstract/Free Full Text]
- Michaels J.E., Dasari S., Pereira L., Reddy A.P., Lapidus J.A., Lu X., Jacob T., Thomas A., Rodland M., Roberts C.T. Jr., et al. Comprehensive proteomic analysis of the human amniotic fluid proteome: gestational age-dependent changes. J. Proteome Res. (2007) 6:1277–1285.[CrossRef][Web of Science][Medline]
- Nilsson S., Ramstrom M., Palmblad M., Axelsson O., Bergquist J. Explorative study of the protein composition of amniotic fluid by liquid chromatography electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. J. Proteome Res. (2004) 3:884–889.[CrossRef][Web of Science][Medline]
- de Souza G.A., Godoy L.M., Mann M. Identification of 491 proteins in the tear fluid proteome reveals a large number of proteases and protease inhibitors. Genome Biol. (2006) 7:R72.[CrossRef][Medline]
- Li N., Wang N., Zheng J., Liu X.M., Lever O.W., Erickson P.M., Li L. Characterization of human tear proteome using multiple proteomic analysis techniques. J. Proteome Res. (2005) 4:2052–2061.[CrossRef][Web of Science][Medline]
- Plymoth A., Yang Z., Lofdahl C.G., Ekberg-Jansson A., Dahlback M., Fehniger T.E., Marko-Varga G., Hancock W.S. Rapid proteome analysis of bronchoalveolar lavage samples of lifelong smokers and never-smokers by micro-scale liquid chromatography and mass spectrometry. Clin. Chem. (2006) 52:671–679.[Abstract/Free Full Text]
- Sabounchi-Schutt F., Astrom J., Eklund A., Grunewald J., Bjellqvist B. Detection and identification of human bronchoalveolar lavage proteins using narrow-range immobilized pH gradient DryStrip and the paper bridge sample application method. Electrophoresis (2001) 22:1851–1860.[CrossRef][Web of Science][Medline]
- Fortunato D., Giuffrida M.G., Cavaletto M., Garoffo L.P., Dellavalle G., Napolitano L., Giunta C., Fabris C., Bertino E., Coscia A., et al. Structural proteome of human colostral fat globule membrane proteins. Proteomics (2003) 3:897–905.[CrossRef][Web of Science][Medline]
- Palmer D.J., Kelly V.C., Smit A.M., Kuy S., Knight C.G., Cooper G.J. Human colostrum: identification of minor proteins in the aqueous phase by proteomics. Proteomics (2006) 6:2208–2216.[CrossRef][Web of Science][Medline]
- Gobezie R., Kho A., Krastins B., Sarracino D.A., Thornhill T.S., Chase M., Millett P.J., Lee D.M. High abundance synovial fluid proteome: distinct profiles in health and osteoarthritis. Arthritis Res. Ther. (2007) 9:R36.[CrossRef][Medline]
- Alexander H., Stegner A.L., Wagner-Mann C., Du Bois G.C., Alexander S., Sauter E.R. Proteomic analysis to identify breast cancer biomarkers in nipple aspirate fluid. Clin. Cancer Res. (2004) 10:7500–7510.[Abstract/Free Full Text]
- Varnum S.M., Covington C.C., Woodbury R.L., Petritis K., Kangas L.J., Abdullah M.S., Pounds J.G., Smith R.D., Zangar R.C. Proteomic characterization of nipple aspirate fluid: identification of potential biomarkers of breast cancer. Breast Cancer Res. Treat. (2003) 80:87–97.[CrossRef][Web of Science][Medline]
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ABSTRACT
INTRODUCTION