Integrated Ionization Approach for RRLC-MS/MS-based Metabonomics: Finding Potential Biomarkers for Lung Cancer Zhuoling An,† Yanhua Chen,† Ruiping Zhang,† Yongmei Song,‡ Jianghao Sun,† Jiuming He,† Jinfa Bai,† Lijia Dong,‡ Qimin Zhan,‡ and Zeper Abliz*,† Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100050, P. R. China, and State Key Laboratory of Molecular Oncology, Cancer Institute and Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, P. R. China Received March 23, 2010
An integrated ionization approach of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combining with rapid resolution liquid chromatography mass spectrometry (RRLC-MS) has been developed for performing global metabonomic analysis on complex biological samples. This approach was designed to overcome the low ionization efficiencies of endogenous metabolites due to diverse physicochemical properties as well as ion suppression, and obtain comprehensive metabolite profiles in LC-MS analysis. Ionization capability and applicability were manifested by improved ionization efficiency and enlarged metabolite coverage in analysis on typical urinary metabolite standards and urine samples from healthy volunteers. The method was validated by the limit of detection and precision. When applied to the global metabonomic studies of lung cancer, more comprehensive biomarker candidates were obtained to reflect metabolic traits between healthy volunteers and lung cancer patients, including 74 potential biomarkers in positive ion mode and 59 in negative ion mode. Taking identical potential biomarkers of any two or three ionization methods into account, analysis using ESI-MS in positive (+) and negative (-) ion mode contributed to 70 and 64% of the total potential biomarkers, respectively. The biomarker discovery capability of (() APCI-MS accounted for 45 and 42% of the overall; meanwhile (() APPI-MS amounted for 39 and 54%. These results indicated that potential biomarkers with vital biological information could be missed if only a single ionization method was used. Furthermore, 11 potential biomarkers were identified including amino acids, nucleosides, and a metabolite of indole. They revealed elevated amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This proposed approach provided a more comprehensive picture of the metabolic changes and further verified identical biomarkers that were obtained simultaneously using different ionization methods. Keywords: metabonomics • integrated ionization approach • ESI, APCI • APPI • RRLC-MS, potential biomarkers • lung cancer
Introduction Metabonomics provides a way to measure the global, dynamic metabolic responses of living systems to biological stimuli or genetic manipulation.1-3 The application of comprehensive metabonomic methods to biological fluids can provide novel insights into biological processes. One of the primary goals of metabonomic studies is to find and identify disease biomarkers.4 Recently, metabonomics approaches that combine the metabolic profiles of biofluids with multivariate data analysis techniques have been widely applied to unravel new mechanistic explanations in relation to diseases.5-7 * To whom correspondence should be addressed. E-mail: zeper@ imm.ac.cn. Telephone: (+86) 01063165218. Fax: (+86) 01063165218. † Institute of Materia Medica. ‡ State Key Laboratory of Molecular Oncology, Cancer Institute and Cancer Hospital. 10.1021/pr100265g
2010 American Chemical Society
A significant challenge in metabonomic research is the global analysis of unknown low-abundance metabolites with diverse physical and chemical properties in biological samples. Currently, no analytical method is suitable for the comprehensive and unbiased analysis of numerous endogenous metabolites required for metabonomics. The high sensitivity of mass spectrometry (MS) and its potential for metabolite identification have made it the dominant approach for metabonomic studies.8,9 Liquid chromatography mass spectrometry (LC-MS) is ideal for metabolite profiling, and its main advantages include a wide dynamic range, ease of automation for large sample series, and the possiblity of small sample volumes.10-12 Ultraperformance liquid chromatography (UPLC) and rapid resolution liquid chromatography (RRLC) can provide high throughput and chromatography resolution. This has made LC-MS-based metabonomics more and more popular.13-16 Electrospray ionization (ESI) is the most commonly used ionization technique for LC-MS analysis. The main advantages Journal of Proteome Research 2010, 9, 4071–4081 4071 Published on Web 06/18/2010
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Figure 1. Flow diagram of integrated ionization approach for RRLC-MS-based metabonomics.
of ESI are soft ionization, no need for derivatization, and the ability to ionize compounds over a large mass range. However, ESI is inefficient at ionizing less polar compounds and is more susceptible to ion suppression in the ionization process. To overcome these issues, atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) have been introduced to LC-MS analysis. In contrast to ESI, APCI and APPI can produce molecular ions from less polar and nonpolar compounds by different ionization mechanisms. They also appear to be less susceptible to ion suppression and chemical noise induced by the matrix and buffer than ESI.17-20 Different applicability of these ionization methods makes it impossible to ionize all metabolites with various physicochemical properties by a single ionization method. Recently, LC inline atmospheric pressure ionization (ESI and APCI), offline matrix assisted laser desorption ionization (MALDI), and desorption ionization on silicon (DIOS) MS have been suggested for metabolomic analysis.21 The present study mainly focuses on the integration of ESI, APCI, and APPI, which are commonly used in LC-MS analysis, dealing with the biased and incomplete ionization induced by metabolite diversity and ion suppression, and allowing implementation of global metabonomic studies to obtain comprehensive potential biomarkers. The integrated ionization approach for RRLC-MS/MS-based metabonomics has been established based on the analysis of 4072
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typical urinary metabolite standards and urine samples of healthy volunteers. The ionization ability and applicability of this approach were evaluated through comparative analysis of ionization efficiency and metabolite coverage. The limit of detection and precision were assessed and validated. Furthermore, this approach has been first applied to the global metabonomic studies of lung cancer to explore more comprehensive potential biomarkers in urine. A summary of the integrated ionization strategy employed is shown in Figure 1.
Experimental Section Chemicals. Typical metabolite standards were purchased from Sigma-Aldrich or from previous studies in our laboratories and included 1-methyladenosine (1-MA), uridine (U), inosine (I), cytidine (C), L-isoleucine (IIe), L-cysteine (Cys), L-histidine (His), phenylalanine (Phe), L-tyrosine (Tyr), threonine (Thr), L-tryptophan (Trp), L-valine (Val), 5-hydroxy-L-tryptophan (5HTP), kynurenic acid (KA), hippuric acid (HA), vanillic acid (VA), caffeic acid (CA), galic acid (GA), estriol (E3), and estrone (E1). All other used standards for text mixture were of analytical or higher grade. Acetonitrile (HPLC grade) and formic acid (HPLC grade) were purchased from Merck. Samples. Five different types of samples were used in the study. The first type of samples (sample set #1) was representative of metabolite standards in a mixed solution. All metabolite
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Table 1. Concentrations of 20 Compounds in Different Standard Mixture for Ionization Ability and Method Validation normal urine concentratione (µg/mL)
compounds
normal urine valuec (µmol/mmol creatinine)
normal urine valued (µg/mL)
10% folde
20% folde,g
1- folde,f,g
5-foldg
1-Methyl-adenosine Uridine Inosine Cytidine L-Isoleucine L-Cysteine a L-Histidine Phenylalanine L-Tyrosine Threonine L-Tryptophan L-Valine 5-Hydroxy-L-Tryptophanb Kynurenic acid Hippuric acida Vanillic acid Caffeic acid Galic acidb Estriolb Estroneb
5.96 (0.00-12.0) 0.994 (0.637-1.351) 1.04 (0.48-1.60) 0.33 (0.16-0.50) 1.579 (0.789-2.368) 3.322 (1.447-5.197) 87.2 (23.0-151.0) 4.605 (1.645-7.566) 10.9 (2.566-19.1) 12.7 (4.934-20.4) 5.263 (1.316-9.211) 3.355(1.118-5.592) 1.34 (1.04-1.64) 837 1.0 (0.0-2.5) 2.6 (1.2-4.1) -
8.40 (0-17.00) 1.20 (0.78-1.60) 1.40 (0.64-2.10) 0.40 (0.19-0.61) 1.04 (0.52-1.60) 2.01 (0.88-3.10) 67.65(17.80-117.10) 3.80 (1.40-6.20) 9.88 (2.30-17.30) 7.56 (2.90-12.20) 5.37 (1.30-9.40) 1.96 (0.65-3.30) 1.27 (0.98-1.60) 74.98 0.84 (0-2.10) 2.34 (1.10-3.70) -
0.84 0.12 0.14 0.04 0.10 0.20 0.34 0.38 0.99 0.76 0.54 0.20 0.55 0.13 0.50 0.08 0.23 0.26 0.15 0.19
1.68 0.24 0.28 0.08 0.21 0.40 0.68 0.76 1.98 1.51 1.07 0.39 1.10 0.25 1.00 0.17 0.47 0.52 0.29 0.37
8.4 1.2 1.4 0.4 1.04 2.01 3.38 3.8 9.88 7.56 5.37 1.96 5.5 1.27 37.49 0.84 2.34 2.6 1.45 1.85
41.91 6.06 6.97 2.00 5.17 10.07 16.91 19.01 49.37 37.82 26.87 9.83 27.50 6.33 187.46 4.20 11.71 13.00 7.25 9.25
a Normal urine concentration in the mixed solution were decreased to 0.05% of the normal urine concentration due to high concentration. Concentration of these compounds in urine have not been reported. c Acquired from the Web site of HMDB. d Calculated in accordance with creatine concentration of 5000 µmol/L in every urine samples. e Sample set #1 which was for method validation. f Sample set #1 which was for ionization ability test. g Sample set #4 QC sample spiked with various concentrations of standard compounds. b
standards were at normal human urine concentrations as described in the Human Metabolome Database (HMDB). This standard solution was used for evaluating the ionization capacities of the multiple ionization approaches. The standard mixture was then gradually diluted to 20 and 10% of normal concentrations and used to determine the limit of detection. The standard mixture of 20 compounds and their concentrations are listed in Table 1. The second type (sample set #2) was urine samples collected from 19 lung cancer patients (61 ( 8.4 years old, BMI: 22.7 ( 2.9) and 22 age- and BMI- matched healthy Chinese volunteers from the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences (Beijing, China). Among these patients, 10 had squamous cell carcinoma, 5 had adenocarcinoma, 2 had small cell carcinoma, and 2 had undifferentiated carcinoma. All of these individuals were newly diagnosed and did not take any form of medical treatment during the sampling period. All participants gave written, informed consent according to the Guides of Hospital Ethics Committee and approved by corresponding regulatory agencies. Each subject was also informed about appropriate collection methods and fasting, diet, and medication restrictions. Fasting urine samples were collected before breakfast and were immediately frozen at -80 °C. The third type of sample (sample set #3) was a urine QC sample, which was pooled by equal volumes of urine (10 µL aliquots) from each of 22 control group volunteers. The fourth type (sample set #4) involved spiking of QC samples with representative metabolites at 0.2, 1, and 5-fold normal human urine concentrations to produce spiked urine samples. These samples were used to evaluate the precision of the method. The fifth type (sample set #5) was a mixture of standard compounds for evaluating the chromatographic reproducibility. Urine Sample Preparation. The frozen urine samples were thawed at 4 °C before analysis. Creatinine analysis was carried out by the Inspection Department of the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences using
an enzymatic procedure. The supernatant was collected after urine the samples were centrifuged at 10 000× g at 4 °C for 20 min. An 80 µL aliquot of urine supernatant was diluted to 320 µL with purified water. All samples were filtered through syringe filters (0.22 µm, Jinteng) before LC-MS analysis. RRLC-MS Analysis. Chromatographic separation was performed on a Zorbax Aq-C18 column (1.8 µm, 10 cm ×2.1 mm; Agilent, USA) using an Agilent 1200 Series rapid resolution liquid chromatography system (1200 RRLC system; Agilent technologies, Waldbronn, Germany). The column was maintained at 35 °C. The injected sample volume was 10 µL for each run. Gradient conditions were as follows: 0-6 min linear gradient 0-10% B, 6-11 min linear gradient 10-20% B, 11-21 min 20-40% B and 21-28 min 40-100% B. Solvent A was 0.1% formic acid-water and B was ACN, at a flow rate of 200 µL/ min. Mass spectrometry experiments were performed on a Q-TOF (QSTAR Elite, Applied Biosystem/MDS Sciex) equipped with ESI, APCI and APPI sources. Data were acquired in both positive and negative ion modes for each ionization technique which generated six separate LC-MS analysis. The measurement conditions were as follows: ESI, source voltage 5.5 kV or -4.0 kV, vaporizer temperature 450 °C, turbo gas 75 psi, nebulizer gas 75 psi, curtain gas 45 psi, declustering potential 50 V; APCI, needle current 2.5 µA, nebulizer temperature 420 °C, turbo gas 50 psi, nebulizer gas 50 psi, curtain gas 30 psi, declustering potential 50 V; and APPI, ion spray voltage 1.3 kV, heater temperature 400 °C, lamp gas 1 L/min, turbo gas 25 psi, nebulizer gas 75 psi, curtain gas 30 psi, declustering potential 50 V, source exhaust controller 1/2 clockwise turn after alert, dopant toluene solution 20 µL/min. The scan range was from m/z 100-850. Data acquisition and processing were performed with Analyst QS 2.0. RRLC-MS/MS Analysis. In the MS/MS experiments, information dependent acquisition (IDA) mode was applied, and the signals were detected in a TOF survey scan followed by Journal of Proteome Research • Vol. 9, No. 8, 2010 4073
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product ion scans on the most intense parent ions as a datadependent MS/MS experiment. Two MS/MS experiments were triggered by each survey scan. For 60 s, compounds with identical mass-to-charge ratio were automatically excluded. Then the metabolites of interest were included in the MS/MS table list. Collision energy (CE) was set to 30 or -30 eV. All other parameters were the same as above. Data acquired in positive ion mode were auto calibrated with background ions (phthalates: 149.0233, 391.2843). In negative ion mode, the signals were also acquired with auto calibration using standard solutions and postcolumn mixing: In ESI, a standard solution of 2-hydroxybenzoic acid and scutellarin was introduced at a concentration of 2 ng/µL and a flow rate of 10 µL/min, generating [M - H]- ions 137.0244 and 462.0798. While in APCI and APPI, a standard solution of 2-hydroxybenzoic acid and rhein was introduced at a concentration of 5.4 ng/µL and a flow rate of 3 µL/min, generating [M - H]- ions 137.0244 and 283.0248. Data Quality Assessment. The quality control sample (QC) (sample set #3) which pooled by equal volume of each healthy volunteer urine was processed as real samples and randomly placed in the sample queue to monitor the stability of the system.22 Samples from healthy volunteers and lung cancer patients were alternated in random order in the analysis batch. A test mixture of standard compounds (sample set #5) was also analyzed at the start, middle, and end of the batch to visually evaluate the chromatographic reproducibility. Data Preprocessing. RRLC-MS raw data files generated from urine analysis were converted to mzData format using wiff to mzData translator software (version 1.0.0.4, Applied Biosystems/MDS Sciex) with the threshold set at 1%. Peak finding, filtering, alignment, and scaling were subsequently carried out using open-source software MZmine 2 beta (version 1.95) (http://mzmine.sourceforge.net/). The parameters were optimized step-by-step until aligned peak lists agreed with the visualization of data across multiple samples.23 The parameters for detailed data preprocessing are available in the Supporting Information Table S1. Evaluation of Identical and Unique Metabolites. Following peak-picking and normalization, all the variables related to the RRLC-MS raw data were output in a peak list. A variable was kept if it had a nonzero value for at least 80% of samples in either group.24 The detailed data handling workflow is displayed in Figure 2. For the variables remaining, the resulting data generated from urine analysis of the 22 healthy volunteers were further processed using partial correlation analysis with high confidence level (a ) 0.001). By selecting a high confidence level, most indirectly caused correlations below the significance threshold would be reduced.25 The molecular, fragment, isotope and adduct ions would be assumed whether come from the same metabolites by summarizing the perfectly correlated signals. The partial correlation coefficients were calculated using ParCorA software (http://mendes.vbi.vt.edu/tikiindex.php?page)Software). Software and the output network could be visualized with the program Cytoscape (http:// www.cytoscape.org/).25 After a tight relationship of variables was judged from a series of correlation analysis and corresponding extracted ion chromatograms (EICs), as the fragment, isotope and adduct ions were manually removed. The numbers of unique and identical metabolites detected using ESI, APCI and APPI-MS in positive or negative ion mode were evaluated using a self-compiled program derived from Microsoft Visual basic (described in the Supporting Informa4074
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Figure 2. Workflow of evaluating identical and unique metabolites in RRLC-MS spectra using ESI, APCI, and APPI ionization methods.
tion). The tolerances allowed were m/z 0.05 amu and retention time 0.2 min. Every variable generated from each of the ionization methods was then compared reciprocally in sequence. Unique metabolites were defined as those detected at specific retention times where no corresponding [M + H]+ or [M - H]- ions were detected in the other ionization methods. Identical metabolites that were obtained by two ionization methods in positive and negative ion modes were relatively independent of those detected in all three ionization methods. Multivariate Data Analysis. The RRLC-MS raw data from urine analysis of the 22 healthy volunteers and 19 lung cancer patients were preprocessed by MZmine and normalized to creatinine. The resulting data were exported into SIMCA-P software +12.0 (Umetrics AB, Umeå, Sweden) for multivariate analysis, including orthogonal projections to latent structures discriminate analysis (OPLS-DA)26,27 and permutation tests. Centered and pareto scaling were applied to all data in order to reduce disturbances from noise and artifacts in the models. The quality of the models was evaluated by the relevant R2, Q2, and intercepts of R2 and Q2. OPLS-DA models are regarded as good and valid only if Q2(Y) > 0.5, 0 < R2(Y) - Q2(Y) < 0.3, intercepts of R2 < 0.4 and intercepts of Q2 < 0.05.28 Furthermore, an independent t-test (p < 0.05) (Microsoft Office Excel 2007) was used to determine if different biomarker candidates obtained from OPLS-DA modeling were statistically significant between groups at the univariate analysis level. Fragment, isotope and adduct ions originating from the same compound were judged by partial correlation coefficients. Metabolites were identified in a similar manner to Chen et al.29 and by exact molecular weight searches of free databases: HMDB (http:// www.hmdb.ca/), PubChem compound database (http://www.ncbi.nlm.nih.gov), METLIN (http://metlin.scripps.edu/), and KEGG (http://www.genome.jp/kegg/ligand.html). High-resolution MS/MS spectra were used for further confirmation of metabolite identities. Commercial standards were used to support identification of some metabolites.
Results and Discussion Ionization Ability and Applicability. Ionization efficiencies of representative metabolites in a standard mixture solution
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Table 2. Identical and Unique Metabolites Obtained by RRLC-MS Analysis on 22 Healthy Control Urine Samples in Positive and Negative Ion Modes of ESI, APCI, and APPI positive ion mode
negative ion mode
(+) ESI (+) APCI (+) APPI (-) ESI (-) APCI (-) APPI Totala ESI/APCI/APPIb ESI/APCIc APCI/APPId ESI/APPIe Unique Detection ability (%)f
118 67 40 1052 78
1635 118 67 103 168 28
118 103 40 87 21
72 70 43 576 66
1157 72 70 120 113 32
72 120 43 163 34
a Total metabolites detected in positive or negative ion mode. Overlapping metabolites detected in all three ionization MS spectra. c Identical metabolites detected in ESI-MS and APCI-MS spectra. d Identical metabolites in APCI-MS and APPI-MS spectra. e Identical metabolites in ESI-MS and APPI-MS spectra. f Percentage of total metabolites detected by a certain ionization method. b
Figure 3. Average peak areas (n ) 6) of representative metabolite standards for interpreting ionization ability of ESI, APCI, and APPI in (A) positive ion mode and (B) negative ion mode.
were evaluated using both positive (+) and negative (-) ion modes of ESI, APCI and APPI. The standard mixture solution (sample set #1) was prepared with concentrations normally found in human urine. It contained 4 nucleosides, 10 amino acids, 4 organic acids, and 2 estrogens from different chemical classes commonly found in biofluids. Figure 3 shows the peak areas that were calculated from the extracted ion chromatograms. In general, ESI could ionize a large range of compounds. ESIMS was suitable for detection of most nucleosides, amino acids, and organic acids. Uridine, inosine, cytidine, and aromatic amino acids had the best peak intensities in (() ESI-MS spectra. However, L-cysteine only produced peak responses in (+) APCI or (+) APPI-MS spectra. APCI source has been routinely used and regarded as an effective alternative to ESI, in which ionization occurs through gas-phase ion-molecule reactions in MS analysis. It ionized all the metabolite standards except for nucleosides. Organic acids were more effectively ionized by (+) APCI-MS than any of the other methods. APPI was
superior to APCI and ESI for ionization of estriol and estrone, probably due to their low polarities. Overall, ESI provided better ionization of polar and moderately polar compounds. APCI could ionize less polar and even nonpolar compounds. Whereas, APPI was more effective for nonpolar metabolites than the other ionization methods. These results indicate that an integrated ionization approach is valuable for the analysis of a wide range of metabolites. Metabolite completeness was evaluated based on the numbers of identical and unique metabolites from RRLC-MS analysis on 22 healthy control urine samples using ESI, APCI and APPI ionization in positive and negative ion modes. For more precise distinction and classification, the estimation of characteristic metabolites was based on the high-resolution MS data. All the raw data were processed by MZmine software for peak detection and alignment. Six data matrices were exported, and represented all the ions detected using this approach. Ions judged to be redundant from partial correlation coefficients and corresponding extracted ion chromatograms (EICs) were removed manually from the data list. For the same polarity mode, [M + H]+ and [M - H]- ions produced by the different ionization methods were compared. After setting tolerances for m/z and retention time, the numbers of identical and unique metabolites were listed in the Table 2. Using this integrated ionization approach, 1635 metabolites were detected in positive ion mode and 1157 in negative ion mode, respectively. When every single ionization method was applied, 78 and 66% of the overall detected metabolites were obtained by using ESI-MS in positive and negative ion modes considering the identical metabolites of any two or three ionization methods in the calculation. (() APCI-MS accounted for 28 and 32% of the integrated detection capability, and (() APPI-MS contributed to 21 and 34% respectively. These results indicate that integrated ionization approach provides more comprehensive metabolites than individual ionization method. Except for identical metabolites that were detected by any two ionization methods, only 118 and 72 metabolites were detected by all three ionization methods in positive and negative ion modes. The existence of ions unique to each ionization method suggests that metabolite information would be incomplete if only a single ionization method is used. ESI-MS provided more unique metabolites than APCI- or APPI-MS, which was consistent with the larger ionization range of ESI. Method Validation. A validation study is designed to demonstrate that the approach is reliable for metabonomic analysis. Journal of Proteome Research • Vol. 9, No. 8, 2010 4075
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Figure 4. Score plots of OPLS-DA models constructed on the data of RRLC-MS using integrated ionization approach: (a-1) (+) ESI ionization mode, (a-2) (-) ESI ionization mode; (b-1) (+) APCI ionization mode, (b-2) (-) APCI ionization mode; (c-1) (+) APPI ionization mode and (c-2) (-) APPI ionization mode. Symbols indicate 9 ) NC healthy volunteers and b ) LC lung cancer patients.
The quality of this metabonomic approach has been evaluated using 20 representative metabolite standards (sample set #1) and spiked urine samples (sample set #4). On account of fourfolds diluted urine samples prepared in sampling procedures, all of the metabolites were prepared at 10% and 20% of normal urine concentrations to assess the limit detection of the method. Except for vanillic acid and galic acid, RRLC-MS 4076
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analysis using integrated ionization in positive and negative modes was able to detect all of the metabolites at 10% normal urine concentrations. Vanillic acid and galic acid could be detected at 20% normal urine concentrations. When this method applied on spiked urine samples (sample set #4), the method exhibited excellent precision, and the relative standard deviations (RSDs) were 1.0 were highlighted as interesting variables, which had a high covariance combined with a high correlation in the scatter plot. The jack-knifed-based confidence interval and the raw data plots were then used to eliminate variables with low reliability. A test for independence (p < 0.05) was used to validate the concentration change of the variables that were significantly different between healthy individuals and lung cancer patients. The detailed results can be obtained in Table S3 as Supporting Information. To reduce redundant variables originating from the same compound, partial correlation coefficients were used to determine the fragment, isotope and adduct ions. These ions also had similar extracted ion chromatograms (EICs), which con-
Table 3. Identical and Unique Potential Biomarkers for Metabonomic Investigation Obtained by RRLC-MS Analysis of Lung Cancer and Healthy Control Urine Samples in Positive and Negative Ion Modes of ESI, APCI and APPI positive ion mode
negative ion mode
(+) ESI (+) APCI (+) APPI (-) ESI (-) APCI (-) APPI Totala ESI/APCI/APPIb ESI/APCIc APCI/APPId ESI/APPIe Unique Detection ability (%)f
9(4)g 7(2)g 4(1)g 32(3) 70
74 9(4)g 7(2)g 11(1)g 6(1)g 45
9(4)g 11(1)g 4(1)g 5 39
9(3)g 0 6(1)g 23(3)g 64
59 9(3)g 0 12(5)g 4 42
9(3)g 12(5)g 6(1)g 5 54
a Total potential biomarkers detected in positive or negative ion mode. Overlapping potential biomarkers detected in all three ionization MS spectra. c Identical potential biomarkers detected in ESI-MS and APCI-MS spectra. d Identical potential biomarkers detected in APCI-MS and APPI-MS spectra. e Identical potential biomarkers detected in ESI-MS and APPI-MS spectra. f Percentage of potential biomarkers detected by a certain ionization method. g Potential biomarkers detected in both positive and negative ion modes. b
firmed that they all came from one metabolite. After removal of redundant variables, 52 potential biomarkers were selected in (+) ESI-MS and 38 in (-) ESI-MS. The data generated from APCI and APPI-MS were also processed by S-plot, jack-knifed confidence interval, raw data plots, and t-test as above. This highlighted 33 and 25 metabolites as potential biomarkers using APCI-MS in positive and negative ion modes, respectively. Twenty-nine metabolites in positive ion mode and 32 in negative ion mode were identified as potential biomarkers in APPI-MS. To further determine which biomarkers were to be identical or unique among the different ionization methods, the selected potential biomarkers were further processed by a self-complied Microsoft Visual basic program. By using this integrated ionization approach, 74 potential biomarkers were detected in positive ionization mode and 59 in negative ionization mode, 12 of which were detected in both positive and negative ion modes, as illustrated in Table 3. Interestingly, Table 3 offered the similar distribution and trend of the identical and unique potential biomarkers as those metabolites described in Table 2. Including identical potential biomarkers of any two or three ionization methods in the calculation, analysis using ESI-MS in positive and negative ion modes contributed to 70% and 64% of total detected potential biomarkers. The biomarker discovery capability of (() APCI-MS accounted for 45% and 42%, meanwhile (() APPI-MS amounted for 39% and 54%. This approach appears to be effective on the prospect of detecting more comprehensive potential biomarkers. Identification of Potential Biomarkers. The elemental compositions of the biomarkers were determined by exact mass weights considering mass defect, mass assignments and relative intensities of the isotope peaks through the high-resolution MS spectra. Searches of various metabonomic databases with these molecular formulas were used to identify possible compounds for the biomarkers. The structural information for the biomarkers was obtained from their MS/MS fragmentation patterns. Retention times and MS/MS spectra of standard samples were then used to further confirm the identities of the biologically significant potential biomarkers. These steps enabled characterization of 11 potential biomarkers in human urine: five aromatic amino acids, five nucleosides, and a metabolite of indole. Detailed information on the determination of biomarker Journal of Proteome Research • Vol. 9, No. 8, 2010 4077
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Table 4. Potential Biomarkers and their Identification Results ionization method (+) ESI
(+) APCI (+) APPI (+) ESI/APCI
RT (min)
m/z
postulated elemental composition
P valuea
metabolite identification
1.43 1.64 8.99 9.1 11.28 2.21 7.34 1.88 2.62
189.1570 203.1467 384.1150 312.1301 413.1414 247.0922 134.0596 298.0984 182.0803
C9H20N2O2 C8H18N4O2 C14H17N5O8 C12H17N5O5 C15H20N6O8 C9H14N2O6 C8H7NO C11H15N5O3S C9H11NO3
N6,N6,N6-trimethyl-L-lysined Dimethylarginined Succinyladenosine Dimethylguanosined Threonylcarbonyl adenosined 5,6-Dihydrouridine Indoxylc 5′-Methylthioadenosinec Tyrosined
3.35
166.0853
C9H11NO2
6.58
205.0939
C11H12N2O2
7.6 × 10-4 2.8 × 10-3 1.6 × 10-2 4.9 × 10-3 5.1 × 10-3 8.5 × 10-4 7.1 × 10-4 5.1 × 10-3 1.5 × 10-3 7.3 × 10-4b 4.8 × 10-3 1.9 × 10-2b 6.5 × 10-4 1.4 × 10-2b
Phenylalanined Tryptophand
a P value of independent t-test. b Ions evaluated in positive ion mode of APCI. c Metabolites confirmed by literature or databases searches. confirmed using standard samples. Others, proposals based on MS fragmentation and exact mass.
d
Metabolites
Figure 5. Characteristic intensities of unique potential biomarkers from integrated ionization methods based RRLC-MS analysis: (A) m/z 384.1150, RT 8.99 min, unique potential biomarker from (+) ESI-MS, not detected in (+) APCI-MS or (+) APPI-MS; (B) m/z, 247.0922, RT 2.21 min, unique potential biomarker with high peak intensity in (+) APCI-MS (b-2), medium peak intensity in (+) APPI-MS (b-3), and low peak intensity in (+) ESI-MS (b-1); (C) m/z 298.0984, RT 1.88 min, unique potential biomarker with high peak intensity in (+) APPI-MS (c-3), medium peak intensity in (+) APCI-MS (c-2), and low peak intensity in (+) ESI-MS (c-1).
candidates is provided in Table 4. The variation for a given metabolite is not independent but may correlate with the variation of other metabolites in the same metabolic pathway, so the discriminated metabolites which are highly correlated in correlation networks should be more reliable biomarkers. To further confirm the identities of the potential biomarkers, they were in-depth analyzed with metabolic correlation network using partial correlation analysis. Figure S2 illustrates correlation of each of the pairs by conditioning on all other metabolites individually, which is caused by the pathway through some intermediates or direct correlation. (Details are provided in Supporting Information.) The results demonstrate that tyrosine (182.0803, 2.62) is directly correlated to phenylalanine (166.0853, 3.35) and tryptophan (205.0939, 6.58), which 4078
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are closely related in biosynthesis. Three significantly correlated nodes were identified as nucleosides, dimethylguanosine (312.1301, 9.1), succinyladenosine (384.1150, 8.99) and threonylcarbonyl adenosine (413.1414, 11.28). Therefore, credible identities had been also confirmed by metabolic correlation networks. These amino acids and nucleosides mentioned above had the same increase tendency and perturbations relations for breast cancer based on in-depth analysis of metabolic correlation network in our previous study.29 The unique potential biomarkers were detected due to the different ionization efficiencies, their structural properties, and adaptability under the experimental conditions. The prerequisites for identifying potential biomarkers included peak intensities above the threshold in the peak finding process, at
research articles
Finding Potential Biomarkers for Lung Cancer
Table 5. Summary of Potential Biomarkers Found by RRLC-MS Analysis on Lung Cancer and Healthy Control Urine Samples in Positive and Negative Ion Modes of ESI, APCI, and APPI positive ion mode
negative ion mode
m/z
RT (min)
fold changea
data origin
m/z
RT (min)
fold changea
data origin
124.0860 144.1007 151.0678 169.0361 175.0824 185.1266 203.1379 229.1191 265.1183 153.0437 166.0853 181.0583 182.0803 205.0939 227.1250 245.0832 126.0823 130.0619 130.0643 135.0447 136.0629 137.0465 141.0649 153.0639 156.0755 188.1728 274.0947 130.0510 130.0567 150.0637 217.1069 120.0673 128.0205 137.0467 146.0914 160.0816 168.0699 170.0945 173.0871 175.0837 176.0704 182.9497 189.1570 196.8726 203.1467 218.0466 221.0974 243.1341 262.1292 267.1359 290.1596 301.1408 312.1301 340.2614 348.0693 360.0688 367.1500 381.1006 384.1147 396.8035 407.7931 413.1414 565.2612 107.0462 132.0440 134.0608 138.0542 179.0442 247.0922 130.0611 136.0609 140.1139 189.1191 298.0984
1.45 2.15 7.79 3.99 1.47 9.46 2.18 1.80 10.80 5.88 3.35 2.04 2.62 6.58 1.57 2.37 2.18 12.85 6.35 12.90 1.88 4.93 1.98 7.05 1.40 1.42 1.79 2.54 1.48 2.82 2.38 1.89 1.21 5.26 2.08 1.60 4.89 2.38 1.67 8.27 12.89 1.25 1.43 1.38 1.64 8.12 2.65 10.94 2.67 11.90 7.01 1.35 9.10 18.85 8.79 12.15 3.79 9.78 8.99 19.64 19.65 11.28 19.39 9.60 7.35 7.34 2.34 2.30 2.21 13.69 6.39 1.62 2.05 1.88
1.8/1.6/1.7 3.5/2.6/3.0 1.9/2.4/2.6 1.6/2.0/1.7 2.1/2.3/2.3 1.9/1.9/2.2 2.2/2.2/2.6 2.8/4.3/3.4 3.7/3.2/3.3 2.1/2.8 1.8/1.6 2.4/2.4 1.8/1.7 2.3/1.8 2.2/1.8 2.2/2.3 2.3/1.9 6.7/6.1 2.1/2.6 5.8/6.5 2.3/2.2 3.0/3.4 3.1/1.9 2.6/2/2 2.0/2.4 3.0/3.2 1.8/3.8 1.9/2.1 1.7/2.1 2.1/2.4 2/2.5 2.6 2.9 2.1 2.9 4.6 2.3 4.4 3.1 3.2 5.5 3.0 1.9 3.5 1.7 2.3 2.7 3.8 2.3 2.3 2.0 1.7 2.3 1.9 2.2 2.4 2.3 2.2 1.9 1.8 1.8 2.2 2.8 3.9 2.8 3.8 4.5 2.4 2.3 6.3 2.5 2.7 1.9 2.6
ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI ESI/APCI ESI/APCI ESI/APCI ESI/APCI ESI/APCI ESI/APCI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI ESI/APPI ESI/APPI ESI/APPI ESI/APPI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI APCI APCI APCI APCI APCI APCI APPI APPI APPI APPI APPI
135.0345 149.0492 165.0436 167.0268 179.0599 194.0511 195.0548 243.0669 263.1091 128.0368 134.0613 137.0271 144.0653 145.0612 151.0256 153.0287 154.0589 227.1015 245.0752 290.0851 331.0901 111.0113 173.0143 178.0578 189.0442 191.0261 723.4980 142.9779 161.0491 194.9380 194.9458 197.0854 204.0689 241.1236 243.1390 255.1377 260.0279 264.0898 269.1495 279.0993 283.0853 302.1171 324.0746 336.0747 350.0905 433.2080 439.1622 539.2492 541.2638 836.5799 187.0376 215.0343 217.0451 377.0860 124.0177 135.0622 137.0255 183.0408 405.0183
1.52 1.63 1.46 4.01 1.55 8.11 1.50 2.39 10.80 2.58 9.82 1.44 4.74 10.86 5.65 2.38 1.46 1.85 2.25 1.78 10.86 2.11 1.81 9.77 2.92 2.10 18.86 1.33 3.10 1.69 1.53 15.56 13.81 10.94 11.90 13.43 2.99 12.75 17.14 8.46 11.19 12.72 8.78 12.17 12.92 19.09 17.66 19.33 19.40 19.65 1.50 1.48 1.49 1.59 3.83 9.77 7.14 2.37 2.26
2.5/2.6/3.0 2.4/2.7/2.7 1.9/3.6/3.3 1.9/2.0/3.1 2.5/2.4/3.1 2.9/4.3/3.7 4.4/4.9/3.9 2.5/2.4/2.8 2.6/3.9/4.3 2.4/1.9 2.4/2.0 2.1/2.1 3.1/2.9 5.2/4.2 3.4/3.7 2.7/2.8 1.8/2.3 5.8/4.5 2.5/3.2 4.2/4.6 3.7/4.7 2.6/2.2 2.2/2.8 2.0/2.8 3.3/3.4 1.7/2.2 2.6/2.1 3.1 4.2 3.5 5.2 4.1 14.4 4.0 3.6 6.2 2.0 5.4 4.4 9.7 4.4 2.9 2.8 2.5 5.3 -3.9 1.7 2.0 3.0 2.8 2.6 3.1 2.9 3.2 1.9 3.1 2.3 2.5 3.2
ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI ESI/APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI APCI/APPI ESI/APPI ESI/APPI ESI/APPI ESI/APPI ESI/APPI ESI/APPI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI ESI APCI APCI APCI APCI APPI APPI APPI APPI APPI
a Fold change was calculated from the mean values of each group. Fold change with a positive value indicates a relatively higher concentration present in lung cancer patients while a negative value means a relatively lower concentration as compared to the healthy controls.
least 80% of the peaks being present in all samples of either group. As shown in Figure 5A, the unique potential biomarker
identified in (+) ESI-MS which was the [M + H]+ ion of succinyladenosine at m/z 384.1150 had highest peak intensity Journal of Proteome Research • Vol. 9, No. 8, 2010 4079
research articles with this ionization approach. High ionization efficiencies for 5,6-dihydrouridine at m/z 247.0922 and 5′-methylthioadenosine at m/z 298.0984 were achieved only in specific ionization methods (see Figure 5B and C). This is a further indication that an integrated ionization approach for RRLC-MS/MS-based metabonomic studies can gain more comprehensive metabolites for diagnosis. Metabolic Changes in Response to Lung Cancer. There were 74 plus 59 potential biomarkers altered contributing to a significantly different metabolic profile of lung cancer group. Table 5 summarized a list of significant potential biomarkers whose concentrations changed between the lung cancer group and healthy volunteer, and their change had been estimated. A PLS-DA model was constructed by these putative biomarkers which showed 97.6% sensitivity and specificity based on a 99% confidence limit for classifications, when integrated ionization method applied. The Y-predicted scatter plot definitely assigned samples to either lung cancer or control samples using a priori cutoff of 0.5. The result demonstrates the putative biomarkers having excellent prediction abilities. The unusual increase of three essential aromatic amino acids such as tyrosine, phenylalanine and tryptophan in urinary excretion might be caused by derangement of protein metabolism in cancer patients.31 Dimethylarginine is shown to be a causative factor in the development of multiple organ failure.32,33 It has been well documented that dimethylarginine is elevated in patients with a cancer-related diagnosis.34 Increased protein turnover, oxidative stress, and impaired dimethylarginine dimethylaminohydrolase activity occurring in hematological malignancies may lead to increased dimethylarginines production.35 N6,N6,N6Trimethyl-L-lysine, a precursor in synthesis and regulation of carnitine, have not been published related to cancer. Nevertheless, carnitine plays a key role in the production and distribution of cell energy, ensuring fatty acid transfer throughout the mitochondrial membrane to their metabolic oxidation sites. It presents abnormalities in the modulation and expression in the various forms of cancer.36,37 Another potential biomarker, indoxyl, was also increased in lung cancer patients. No investigations have directly shown indoxyl to contribute to cancer development. However, indoxyl is metabolic endproducts of the tryptophan metabolite indole, both of which have been implicated as etiological factors in tumorigenesis and cancer proliferation. Significant variations of modified nucleosides have been demonstrated in association with various types of cancer due to the regulated cell turnover rate, activity of modifying enzymes, and RNA/DNA modifications.38 These newly found potential biomarkers suggest that amino acids and nucleosides metabolic abnormalities might be important in lung cancer patients. In addition, these differentially expressed metabolites observed in our study were in good agreement with recently reported metabolite profiles of breast patients.29 Future work could look at validating these potential biomarkers in a larger patient cohort.
Conclusions An integrated ionization approach by combining ESI, APCI, and APPI with RRLC-MS has been developed for performing global metabonomic analysis and finding more comprehensive potential biomarkers in complex biological samples. In the analysis of representative metabolite standards and human urine, improved ionization efficiency and enlarged metabolite coverage demonstrate its ability and applicability for comprehensive metabolite profiles. The limit of detection and precision 4080
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An et al. were validated to be reliable. This approach had been successfully applied for the metabolic profiling of human urine samples associated with lung cancer. Seventy-four potential biomarkers were detected in positive ion mode and 59 in negative ion mode. Unique potential biomarkers that were specific to one ionization method could be missed if a single ionization method was used. Moreover, identical potential biomarkers of two or three ionization methods could be proved by the same biomarker discovery process. Taken together, these results confirmed the complementary advantages of integrating multiple ionizations, which can be used to find more comprehensive potential biomarkers and further verify identical biomarkers derived from different ionization methods. Thus, this approach makes it possible to achieve a more complete and detailed description of metabolic perturbations based on global metabonomic analysis.
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