Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles

CCA-12856; No of Pages 8 Clinica Chimica Acta xxx (2012) xxx–xxx

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Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles Yu-Chang Tyan a,b,1,⁎, Ming-Hui Yang c,1, Tze-Wen Chung c,d, Chi-Yu Lu e, Wan-Chi Tsai f,g, Shiang-Bin Jong a,h a

Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan National Sun Yat-Sen University-Kaohsiung Medical University Joint Research Center, Kaohsiung, Taiwan Department of Chemical and Materials Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan d College of Pharmacy, Taipei Medical University, Taipei, Taiwan e Department of Biochemistry, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan f Department of Medical Laboratory Science and Biotechnology, Kaohsiung Medical University, Kaohsiung, Taiwan g Department of Laboratory Medicine, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, Taiwan h Department of Nuclear Medicine, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, Taiwan b c

a r t i c l e

i n f o

Article history: Received 23 September 2012 Accepted 10 October 2012 Available online xxxx Keywords: Magnetic nanoparticle Protein identification Mass spectrometry Biomarker Urinary proteome

a b s t r a c t Background: Samples originating from body fluids often contain a complex mixture of inorganic salts, buffers, chaotropic agents, surfactant/detergents, preservatives, and other solubilizing agents. The presence of those contaminants often precludes direct analysis by mass spectrometry. Urine, a blood filtrate produced by the urinary system, is an ideal bio-sample and a rich source of biomarkers for diagnostic information. Methods: To enhance our understanding of urinary proteome, the urine proteins were prepared by magnetic nanoparticles (MNPs) combined with MACS separation column system and then identified by reverse phase nano-high performance liquid chromatography electrospray ionization tandem mass spectrometry (RP-nano-HPLC-ESI-MS/MS) followed by peptide fragmentation pattern. Results: Experimental results have revealed that the better protein identification for the demonstration of bovine serum albumin (BSA) in artificial urine. Using this cleanup approach, a total of 542 peptides, corresponding to 282 unique proteins, were identified from human urine samples, in which 54 proteins have higher confidence levels. Indeed, this study has revealed that some biological factors might be increased along with aging, such as up-regulation of immunoproteins. Conclusions: The present study was designed to establish optimal techniques to develop a proteomic map of urinary proteins, and a cleanup method that greatly simplifies this sample preparation process was proposed. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Human body fluids play a significant part in proteome research. Body fluids such as serum, plasma, tear, saliva, cerebrospinal fluid (CSF), urine and serous fluids/effusions (pleural, pericardial, and peritoneal) have been proven to be a rich source of biomarkers for the detection of disease. Amongst them, urine samples are the most easily obtained and are one of the most common samples in clinical analysis [1]. Urine has long been known as a rich source of diagnostic information, because of its physical proteins and chemical composition. It is a suitable specimen for proteomic studies, due to the fact that many low-molecular weight proteins and peptides can pass through the glomerulus membrane, are catabolized within the proximal tubules and are finally ⁎ Corresponding author at: Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, 100, Shi-Chuan 1st Road, Kaohsiung 807, Taiwan. Tel.: + 886 7 3121101x2357. E-mail address: [email protected] (Y.-C. Tyan). 1 These authors contributed equally to this work as first authors.

secreted in the urine. Therefore, the urinary proteome is central to clinical and pharmaceutical research. Numerous efforts are being made to discover, identify and validate biological markers for the diagnosis of renal function or kidney and urinary tract diseases [2–9]. Recently, a more thorough investigation of the total protein composition of human urine is therefore required. Such a global analysis is important and can enhance our understanding of urogenital tract diseases and pathogenesis [10–12]. Although human urine samples are ideal bio-samples and have tremendous potential as sources of biomarkers [13], they are also considered one of the most difficult proteomic samples with which to work. Samples originating from biological sources often contain a complex mixture of inorganic salts, buffers, chemotropic agents, surfactants/detergents, preservatives, and other solubilizing agents. High concentrations of salts are often present in urine samples to solubilize or stabilize analytes such as proteins. Due to inorganic salts or contaminants in urine, the utility of the proteomics approach in identification of urinary proteins has been poorly defined. Also, buffer concentrations employed by biochemists in urine samples often are in unacceptable ranges for mass spectrometric analysis [14].

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Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021

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Y.-C. Tyan et al. / Clinica Chimica Acta xxx (2012) xxx–xxx

We describe a strategy for utilizing magnetic nanoparticles (MNPs) and MACS separation column system as a biological sample cleanup method for reverse phase nano-high performance liquid chromatography electrospray ionization tandem mass spectrometry (RP-nano-HPLC-ESI-MS/MS) analysis. Several sample preparation techniques have been designed specifically for mass spectrometry analysis of contaminated biological samples. A procedure has been developed for protein sample cleanup and preconcentration in a micro column system contained with hydrophobic resin. In this method, protein sample loading is accomplished by flowing the protein solution though the micro column, where the protein adsorbs to the hydrophobic surface. The proteins are eluted for detection by MS [15,16]. Zip tip, one kind of mini-reversed phase column chromatography, is useful for cleanup and concentration of samples. As peptides and proteins have differing affinities for the C18, the Zip tip can be used for fractionation of mixtures [17]. Another procedure for sample cleanup and preconcentration was using a size exclusion chromatography, such as multiscreen filter plate with Ultracel-10 membrane [18]. In this study, the strategy created MNPs and MACS separation column system that was designed to cleanup urine specimens. In our experiment, MNPs and MACS separation column system were used to complete a preliminary study for a urine sample cleanup strategy for mass spectrometry analysis. The carboxylic acid group modified MNP is re-activated and coupled with proteins. This type of carboxyl MNP can also be successfully used to develop a protein sample cleanup, concentrate and buffer exchange system. This strategy affords a simple, feasible and general method of sample preparation for analyses by mass spectrometry. 2. Materials and methods 2.1. Formula of artificial urine The following reagents were necessary for the preparation of normal artificial urine: bovine serum albumin powder (BSA, A3311, Sigma, St. Louis, MO), creatinine (C4255, Sigma), distilled water, potassium chloride (P3911, Sigma), sodium chloride (31434, Riedel-de Haën), sodium phosphate, monobasic (S0751, Sigma), and urea (113563, Usb Corporation). To 1.5 l of distilled water, 36.4 g of urea, 15.0 g of sodium chloride, 9.0 g of potassium chloride and 9.6 g of sodium phosphate were mixed until all the crystals were dissolved. The pH value was then checked to ensure the pH was within the 5–7 pH range. The pH of the solution was adjusted with 1 mol/l hydrochloric acid or 1 N sodium hydroxide. Next, a urine hydrometer was used to regulate the specific gravity into the range of 1.015–1.025 with distilled water. Then, to ensure a similarity to human urine, 4.0 g of creatinine and 100 mg of BSA were slowly mixed into the so-called normal artificial urine solution. The final volume of artificial urine solution was about 2 l. 2.2. Urine collection and preparation Human mid-stream urine specimens (first urine in the morning) were collected from forty normal individuals with no evidence of disease (twenty males and twenty females, aged 25–40 and 55–75 y, who did not consume aspirin or other non-steroidal anti-inflammatory drugs for at least two weeks previous and have no history or evidence of urological diseases). The urine samples were placed on ice prior to centrifugation at 2000×g for 10 min at 4 °C for the removal of cellular material and were frozen at −80 °C to prevent bacterial growth. Protein concentrations were measured by fluorescence-based protein quantification detection kit (Quant-iT™ Fluorometer, Qubit® Protein Assay Kit, Q33212, Invitrogen, San Diego, CA). Urine samples were cleanup by MNPs (LodeStars™ 2.7 Carboxyl) and MACS® separation column system with Milli-Q grade water (Millipore Co., Inc., Billerica, MA). The surface of carboxyl (\COOH

group) can be activated by converting the carboxylic acid groups to reactive esters using N-ethyl-N′-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC, E-6383, Sigma) and N-hydroxysuccinimide (NHS, H-7377, Sigma). Proteins can be coupled to this reactive surface via their primary amines to form stable amide bonds. MNPs were immersed in the coupling agents: 75 mmol/l EDC and 15 mmol/l NHS at 4 °C for 30 min [19,20]. Water-soluble EDC and NHS were used for activating O_C\OH [21,22] and then the EDC-NHS buffer was removed and replaced by urine samples. The scheme of the protein binding reaction was showed in Fig. 1. The protein-binding MNPs were loaded into the MACS® separation column and washed twice with D.I. Water. Then, protein-binding MNPs were eluted by digestion buffer and reduced, alkylated then digested with trypsin (V5111, Promega, Madison, WI) to generate the constituent peptides. Peptides and MNPs were separated using the MACS® separation column with magnetic field, and the peptides were eluted with 100 μl of 50% acetonitrile/H2O for subsequent RP-nano-HPLC-ESI-MS/MS analysis. 2.3. Proteome analysis by HPLC-MS/MS The complex peptide mixtures were separated by RP-nano-HPLCESI-MS/MS. The protein tryptic digests were fractionated using a flow rate of 400 nl/min with a nano-HPLC system (nanoACQUITY UPLC, Waters, Milford, MA) coupled to an ion trap mass spectrometer (LTQ Orbitrap Discovery Hybrid FTMS, Thermo, San Jose, CA) equipped with an electrospray ionization source. For RP-nano-HPLC-ESI-MS/MS, a sample (2 μl) of the desired peptide digest was loaded into the reverse phase column (Symmetry C18, 5 μm, 180 μm ×20 mm) by autosampler. The RP separation was performed using a linear acetonitrile gradient from 99% buffer A (100% D.I. water/0.1% formic acid) to 85% buffer B (100% acetonitrile/0.1% formic acid) in 60 min using the micropump at a flow rate of approximately 400 nl/min. The separation is performed on a C18 microcapillary column (BEH C18, 1.7 μm, 75 μm ×100 mm) using the nano separation system. As peptides eluted from the microcapillary column, they were electrosprayed into the ESI-MS/MS with the application of a distal 2.1 kV spraying voltage with heated capillary temperature of 200 °C. Each cycle of one full scan mass spectrum (m/z 400–2000) was followed by three data dependent tandem mass spectra with collision energy set at 35%. 2.4. Database search For protein identification, Mascot software (ver 2.2.1, Matrix Science, London, UK) was used to search the Swiss-Prot human protein sequence database. For proteolytic cleavages, only tryptic cleavage was allowed, and the number of maximal internal (missed) cleavage sites was set to 2. Variable modifications of cysteine with carboxyamidomethylation, methionine with oxidation, and asparagine/glutamine with deamidation were allowed. Mass tolerances of the precursor peptide ion and fragment ion were set to 10 ppm and 0.5 Da, respectively. Positive protein identifications were defined when Mowse scores greater than 100 were considered significant (p b 0.05). Proteins were initially annotated by similarity searches using NCBI PubMed (http://www. ncbi.nlm.nih.gov/), Swiss-Prot/TrEMBL (http://www.expasy.org/), and Bioinformatic Harvester EMBL (http://harvester.embl.de/) databases [23]. 3. Results and discussions Urinary proteome studies have been previously performed with a number of problems that prevented the usage of this technique as a diagnostic or physiologic tool. The aim of this study is to utilize proteomic approaches for large-scale identification of human urine proteins. MNPs have been reported in drug delivery and immunoassay as well as in biosensing by attaching molecules including antibodies, oligonucleotides, proteins, etc. [24–28]. For the cleanup efficiency of the

Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021

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Fig. 1. Theory of proteins binding with magnetic nano-beads in urine.

MNPs, albumin in the artificial urine is taken as a model protein for protein identification in this study. In this experiment, 20 μl of the artificial urine sample (contain 1 μg albumin) was cleaned-up by MNPs combined with MACS separation column system by de-ionized water to remove salt or non-protein contaminants. Albumin can be coupled to this reactive surface via their primary amines to form stable amide bonds by coupling agents. Then, the albumin was digested and eluted with 100 μl of 50% acetonitrile/H2O for subsequent nano-HPLC-ESIMS/MS analysis. The Mascot MS/MS ion search of the data yielded BSA as the top hit with MOWSE scores of 924 and 486, and the sequence coverage of ~ 34 and ~ 18%, corresponding to 19 and 11 peptide matches, respectively (Table 1). Table 2 showed the protein sequence view and matched peptides of the BSA in artificial urine (italic and bold) and in resuspension buffer (V542A, Promega; underline). Compared with the BSA in resuspension buffer, the artificial urine after the MNPs combined with MACS separation column system cleanup resulted better mass accuracy (Table 3). In Table 3, most peptides were observed as doubly- or trebly-charge peptides. This result demonstrated that BSA in artificial urine was sufficient for protein identification. In this study, urine samples were collected from young (aged 25–40) and elderly (aged 55–75) individuals. Total protein and albumin concentrations in urine samples were measured. Experimental results showed that the urinary protein concentrations in samples from both groups were in the normal range without significantly difference (Table 4). Urine samples obtained from elderly individuals had higher concentrations of urinary albumin compared with the young group; however, the concentrations of urinary albumin in both groups were Table 1 The result of BSA identification by MASCOT search. Data shown in this table were the results of 1 of 12 measurements. Protein identification of BSA

In artificial urine In resuspension buffer

MOWSE score

Sequence coverage

Peptide matches

924 486

34% 18%

19 11

lower than 20 mg/l and also in the normal range. Experimental results showed that the protein and albumin concentrations in the urine samples of all individuals were not displayed as the phenomenon of proteinuria or albuminuria. Urine samples were pooled with equal amounts of proteins and adjusted to 10 mg/ml using 25 mmol/l ammonium bicarbonate, respectively. Then, urine samples were cleanup and digested by the MNPs combined with MACS separation column system. The complex peptide mixtures obtained from the tryptic digestion of urine were analyzed by RP-nano-HPLC-ESI-MS/MS. Each RP-nano-HPLC-ESI-MS/MS analysis typically generated about 3750 MS/MS spectra. A total around 22,500 (3750 scans × 2 sample preparations× 3 repeat) MS/MS spectra were analyzed by the database search software Mascot. Only a small fraction (18.6%, 4192) of searches produced significant matches according to the inclusion criteria that we used for this study. The inclusion criteria were based on XCorr values, indices calculated by the search algorithms to reflect the similarity between product ion data and computer-simulated peptide fragmentation patterns. The peptide identification for the resulting MS/MS spectra was carried out using the Mascot database search software. These 4192 significant matches represented 1747 peptides. The total number of unique peptides identified in the urine sample was 542. The tryptic peptides produced were often mapped to protein sequence entries in the Swiss-Prot database. The 542 unique matched peptide sequences belonged to 282 proteins. Most of these were identified at minimal confidence level, which was only one unique peptide sequence matched. Experimental results reported a total of 54 protein identifications with higher confidence levels (at least three unique peptide sequences matched). The RP-nano-HPLC-ESI-MS/MS approach is perhaps the most representative method in proteome research. The fragmentation spectra obtained by the RP-nano-HPLC-ESI-MS/MS analysis in gradient detection mode were compared with a non-redundant protein database using Mascot software. In this study, for the overall quality, all Mascot results were visually confirmed. In addition, the criterion requires a readily observable series of at least four y ions for an identified peptide [29]. When a protein was identified by three or more unique peptides, no visual assessment of spectra was conducted and the protein was

Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021

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Table 2 Protein view and matched peptides shown in the BSA of artificial human urine and resuspension buffer. 1 51 101 151 201 251 301 351 401 451 501 551 601

MKWVTFISLL FSQYLQQCPF VASLRETYGD KADEKKFWGK LLPKIETMRE FVEVTKLVTD CCDKPLLEKS GSFLYEYSRR KHLVDEPQNL RSLGKVGTRC TESLVNRRPC ALVELLKHKP STQTALA

LLFSSAYSRG DEHVKLVNEL MADCCEKQEP YLYEIARRHP KVLASSARQR LTKVHKECCH HCIAEVEKDA HPEYAVSVLL IKQNCDQFEK CTKPESERMP FSALTPDETY KATEEQLKTV

VFRRDTHKSE TEFAKTCVAD ERNECFLSHK YFYAPELLYY LRCASIQKFG GDLLECADDR IPENLPPLTA RLAKEYEATL LGEYGFQNAL CTEDYLSLIL VPKAFDEKLF MENFVAFVDK

IAHRFKDLGE ESHAGCEKSL DDSPDLPKLK ANKYNGVFQE ERALKAWSVA ADLAKYICDN DFAEDKDVCK EECCAKDDPH IVRYTRKVPQ NRLCVLHEKT TFHADICTLP CCAADDKEAC

EHFKGLVLIA HTLFGDELCK PDPNTLCDEF CCQAEDKGAC RLSQKFPKAE QDTISSKLKE NYQEAKDAFL ACYSTVFDKL VSTPTLVEVS PVSEKVTKCC DTEKQIKKQT FAVEGPKLVV

Italic and bold: BSA matched peptides in the artificial human urine (cleanup by MNPs and MACS separation column); underline: matched peptides in the resuspension buffer.

considered to be present in the sample. Fig. 2 shows typical MS/MS spectrum of the identified peptide. The MS/MS spectrum represents the amino acid sequence of tryptic peptide, which is doubly charged peptide with m/z of 807.33. The monoisotopic mass of the neutral peptide molecular weight is 1612.66. The amino acid sequence of the tryptic peptides is LLDNWDSVTSTFSK. This peptide originated from Apolipoprotein A-I precursor, and the interpretation of the complete y-ion and b-ion series provides the peptide sequence as shown. Fig. 3 showed the distribution of cellular locations of protein identification by using RP-nano-HPLC-ESI-MS/MS approach in this study. Among 282 proteins identified, 23.6% were known to be nuclear/nucleus proteins; 30.3% were known to be secreted proteins; 16.9% were known to be cytoplasma proteins; 14.6% were known to be membrane proteins. A few golgi apparatus, cytoskeleton, mitochondrial and microsome proteins were also identified (19.1%). A considerable portion of the identified proteins (23.6%) has not been reported for their cellular locations. Identified proteins in this study were classified into different groups of molecular functions and biological processes, based on their functional categories in the SWISS-Port/TrEMBL and Bioinformatic Harvester EMBL protein database. We used the ExPASy Molecular Biology Server (Expert Protein Analysis System) of the Swiss Institute of Bioinformatics (SIB) to explore what known functions of

the identified proteins had been reported in the literature. The Swiss-Prot identifiers could be employed for linkage of proteins to define vocabulary of terms describing the biological processes, cellular components and molecular functions of known Gene Ontology (GO). The Gene Ontology Consortium provides annotation of each protein and structure allowing us to organize selected proteins into biologically relevant groups. These groupings can serve as the basis for identifying those areas of biology showing correlated protein changes [30,31]. The proteins identified in this study were presented into function categories based on their annotations in the GO database. Fig. 4 shows the number and percentage of proteins with certain reportedly known biological processes and molecular functions. Proteins identified in the category also include immumoglobulins, defenses, and urinary enzymes such as lysozyme and peroxidase, which form the urinary defense system to protect the urinary tract. Other groups included some typical urinary proteins exhibiting binding, transport, metabolism and catalytic activity. Among the 282 proteins, 127 proteins were binding proteins. Forty-two proteins were about catalytic activity. Seventy-four proteins have been known to be associated with defense/immune response, and 66 proteins have been known to be associated with metabolism. Protein functions related to structural molecule activity, transporter activity, and cell communication were also surveyed, and these functions were linked

Table 3 Matched BSA peptides sorted by residue number. Start–end

Peptide

Mr(calc)

Observed m/za Artificial human urine

Resuspension buffer

66–75 89–100 139–151 161–167 168–183 169–183 319–336 319–340 347–359 360–371 402–412 421–433 437–451 437–451 469–482 508–523 529–544 548–557 549–557 549–557 569–580 598–607

K.LVNELTEFAK.T K.SLHTLFGDELCK.V + Carbamidomethyl (C) K.LKPDPNTLCDEFK.A +Carbamidomethyl (C) K.YLYEIAR.R R.RHPYFYAPELLYYANK.Y R.HPYFYAPELLYYANK.Y K.DAIPENLPPLTADFAEDK.D K.DAIPENLPPLTADFAEDKDVCK.N+ Carbamidomethyl (C) K.DAFLGSFLYEYSR.R R.RHPEYAVSVLLR.L K.HLVDEPQNLIK.Q +Deamidated (NQ) K.LGEYGFQNALIVR.Y R.KVPQVSTPTLVEVSR.S K.VPQVSTPTLVEVSR.S + Deamidated (NQ) R.MPCTEDYLSLILNR.L + Carbamidomethyl (C) R.RPCFSALTPDETYVPK.A +Carbamidomethyl (C) K.LFTFHADICTLPDTEK.Q + Carbamidomethyl (C) K.KQTALVELLK.H K.QTALVELLK.H K.QTALVELLK.H +Pyro-glu (N-term Q) K.TVMENFVAFVDK.C K.LVVSTQTALA.-

1162.62 1418.69 1575.76 926.49 2044.02 1887.92 1954.95 2457.17 1566.74 1438.8 1305.69 1478.79 1638.93 1511.89 1723.83 1879.91 1906.91 1141.71 1013.61 996.59 1398.69 1001.58

582.35 473.87 526.52 464.06 – 945.03 978.69 820.03 – 480.72 653.88 740.22 820.63 756.95 862.98 941.29 954.63 572.13 507.99 – 700.38 501.76

582.55 – – – 1023.09 – 978.61 – 784.67 480.63 653.64 – 820.78 756.84 – – – 572.04 – 997.47 – 1002.34

a

(−) Not observed. Most peptides were observed as doubly- or trebly-charge peptides.

Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021

Y.-C. Tyan et al. / Clinica Chimica Acta xxx (2012) xxx–xxx Table 4 The concentrations of total protein and BSA in urine.

5

Nucleus 23.6%

Unknown 23.6%

Age range

Total protein (mg/l)

BSA (mg/l)

25–40 55–75

105.0 ± 8.28 139.5 ± 12.1

15.57 ± 0.75 16.54 ± 0.84

Mean ± SE; n = 40.

Others 19.1%

to considerable portions of the identified proteins in this study. Some proteins still had no prior functional information reported. It is not surprising that the largest group of the identified proteins were functionally unknown proteins (including the hypothetical proteins). In this study, a total of 54 proteins were identified with high levels of confidence in which three of them were only found in the urine samples from elderly individuals. The aging-related proteins were transforming growth factor (TGF), immunoglobulin G (IgG) and Complement 3a (C3a) that may play a role in the regulation of defense/ immune response. A summary of the protein identifications achieved is shown in Table 5. TGF with biological processes of aging and cell death was controlled proliferation, differentiation and other functions in many cell types. It plays an important role in bone remodeling as it is a potent stimulator of osteoblastic bone formation, causing chemotaxis, proliferation and differentiation in committed osteoblasts [32].

Membrane 14.6%

Secreted 30.3% Cytoplasma 16.9%

Some proteins were described of the different subcellular location, which explains the total sum being substantially larger than 100%. Fig. 3. Distribution of the identified human urine proteins according to their location. Assignments were made on the basis of information provided on the Swiss-Prot database at the ExPASy Molecular Biology Server. Some proteins were described of the different subcellular locations, which explain the total sum being substantially larger than 100%.

Antibodies are major components of the immune system and inflammatory response. IgG is the main antibody isotype found in blood and extracellular fluid allowing it to control infection of body tissues. Representing approximately 75% of serum immunoglobulins

Fig. 2. MS/MS spectral data of peptide from the urine sample: Apolipoprotein A-I precursor, peptide LLDNWDSVTSTFSK (m/z = 807.33, +2). Interpretation of the complete y-ion and b-ion series provides the peptide sequences as shown.

Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021

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Y.-C. Tyan et al. / Clinica Chimica Acta xxx (2012) xxx–xxx

A

300

282

Protein number

250 200

127, 45%

150 100

42, 15%

50

31, 11%

28, 10%

64, 23%

23, 8%

15, 5% sifie

d

ity Rece

Not

ptor

clas

activ

ctivi ty itor a inhib ease

Stru

B

Prot

ctura

Tran

l mo

spor

ter a

lecu le ac

ctivi ty

tivity

ity activ Cata

lytic

Bind

Tota

ing

l

0

300 282

Protein number

250 200 150

74, 66, 26% 23% 54, 39, 19% 32, 27, 29, 11% 9% 12, 12, 11, 11, 9, 9, 8, 8, 10% 14% 4% 4% 4% 4% 3% 3% 3% 3%

100 50

d sifie

rs Othe

clas Not

s nesi hoge

noen

esis

Morp

Orga

stion Dige

esis ynth Bios

lity moti Cell

ion ferat

Cell

scrip Tran

proli

tion

sis estra Hom

ent lopm

icati mun

com

Deve

on

t Cell

m

spor Tran

bolis Meta

onse

Defe

nse/

Imm

une

resp

Tota

l

0

Fig. 4. The number and percentage of proteins with certain reportedly known biological processes (A) and molecular functions (B). Proteins involved in biological process and molecular functions were selectively presented and illustrated, the number of protein represents the percentage out of total GO term selected, some proteins were described of the different biological processes or molecular functions, which explain the total sum being substantially larger than 100%.

in humans, IgG is the most abundant antibody isotype found in the circulation [33]. C3 plays a central role in the activation of the complement system. Its processing by C3 convertase is the central reaction in both classical and alternative complement pathways. After activation C3b can bind covalently, via its reactive thioester, to cell surface carbohydrates or immune aggregates. Derived from proteolytic degradation of complement C3, C3a anaphylatoxin is a mediator of local inflammatory process. It induces the contraction of smooth muscle, increases vascular permeability and causes histamine release from mast cells and basophilic leukocytes [34]. Our finding of an increased secretion of defense/immune response proteins in urine samples with aging may be explained through the view of immunosenescence, i.e., remodeling of immunity and inflammaging. The innate immunity is largely preserved with aging. Indeed, research has revealed that some biological factors might decrease or increase with age, while others like innate immunity undergo no

changes in expression [35,36]. The non-deterioration of innate immunity with age may be applied to urinary gland functions. It is different from the traditional inflammation signs of redness, swelling, heat, and pain, instead, inflammation covers the weakened adaptive immune response and increased activity of mononuclear cell innate immunity [37,38]. Urine, a blood filtrate produced by the urinary system, is readily collected and is an important source of information for diseases. Using proteomic approaches, we have profiled protein expression from urine and identified differentially expressed proteins. A protein expression database constructed and referred by our study may be valuable for new cancer biomarker discovery and as new diagnostic tools. 4. Conclusions We applied MNPs combined with MACS separation column system to catalog total 282 proteins from human urine proteome, 54 of

Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021

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Table 5 The 3 urinary proteins identified with higher confidence levels (at least three unique peptide sequences matched) in elderly individuals. Accession no.a

Protein name

P01137

Transforming growth factor

P01857

Immunoglobulin G

MW (Da)

Mascot score

44,341 417

36,106 268

Match queriesb

Sequence coverage (%)

Peptide

Subcellular location

Biological process

Molecular function

7

24.1

K.TIDMELVK.R

Extracellular matrix Secreted

Aging

Growth factor

Immune response Cell division/ proliferation Response to shear stress

Mitogen

21.2

R.EAVPEPVLLSRAELR.L K.VEQHVELYQKYSNNSWR.Y + Deamidated (NQ) R.QWLSRGGEIEGFR.L + Deamidated (NQ) K.GYHANFCLGPCPYIWSLDTQYSK.V R.ALDTNYCFSSTEK.N + Deamidated (NQ) R.ALDTNYCFSSTEKNCCVR.Q K.FNWYVDGVEVHNAK.T Secreted

Complement activation, classical pathway Innate immune response

Antigen binding

Complement alternate pathway

Endopeptidase inhibitor activity

6

K.STSGGTAALGCLVK.D

P01024

Complement C3a

187,148 738

7

5.8

K.PKDTLMISR.T K.NQVSLTCLVK.G + Deamidated (NQ) K.TTPPVLDSDGSFFLYSK.L K.SLSLSPGK.K.FVTVQATFGTQVVEK.V + Deamidated (NQ) R.TVMVNIENPEGIPVK.Q R.IPIEDGSGEVVLSR.K K.LSINTHPSQK.P K.ADIGCTPGSGK.D K.DSITTWEILAVSMSDK.K K.AAVYHHFISDGVR.K

a b

Secreted

Complement pathway Immunity Inflammatory response Innate immunity

Swiss-Prot/TrEMBL accession number was given from http://us.expasy.org/. Number of unique peptide sequences matched from Mascot database search result for this protein.

which were identified for the higher confidence level. Although the result showed few differences in the protein list between young and aged individuals, many urinary proteins in whole urine gave more useful information for further clinical diagnostic and epidemiological research applications. For urine proteome analysis, useful cleanup methods combined with LC-base separation were reported, and a urine proteome database has been produced. Our data demonstrate that development of strategy to isolate urine proteins can expand the urinary proteomic map. With the bioinformatics analysis, the urinary proteins may help discern the origin of proteins in the urinary tract, which may relate to aging. This approach is a potentially powerful tool to discover new biomarkers and/or causative factors of disease-related proteins in urinary clinical studies; in addition, its sensitivity may also make it applicable to direct ultra-microanalysis of other biofluids. However, the results of these studies must be verified by larger clinical studies. Acknowledgement The authors thank the Center of Excellence for Environmental Medicine, Kaohsiung Medical University for the assistance in protein identification. This work was supported by research grants NSC100-2320-B-037-007-MY3 from the National Science Council, and NSYSUKMU 101–015 from NSYSU-KMU Joint Research Project, Taiwan, Republic of China. S. Sheldon MT (ASCP) of Oklahoma University Medical Center Edmond (USA) is appreciated for the editorial assistance. Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version. The protein–protein interaction pathways were

analyzed by String 9.0 Web software. Proteins identified in this study were marked by red arrows. http://dx.doi.org/10.1016/j.cca. 2012.10.021.

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Please cite this article as: Tyan Y.-C., et al, Assessing human urinary proteome using a mass spectrometry-based profiling system combined with magnetic nanoparticles, Clin Chim Acta (2012), http://dx.doi.org/10.1016/j.cca.2012.10.021