Quantitative Analysis of the Human AKR Family Members in Cancer

Article pubs.acs.org/jpr

Quantitative Analysis of the Human AKR Family Members in Cancer Cell Lines Using the mTRAQ/MRM Approach Shenyan Zhang,†,‡,# Bo Wen,§,# Baojin Zhou,§ Lei Yang,† Chao Cha,§ Shaoxing Xu,§ Xuemei Qiu,§ Quanhui Wang,†,§ Haidan Sun,† Xiaomin Lou,† Jin Zi,§ Yong Zhang,§ Liang Lin,*,§ and Siqi Liu*,†,§ †

Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 101318, China Graduate University of the Chinese Academy of Sciences, Beijing, 100049, China § BGI-Shenzhen, Shenzhen, 518083, China ‡

S Supporting Information *

ABSTRACT: Members of human aldo-keto reductase (AKR) superfamily have been reported to be involved in cancer progression, whereas the final conclusion is not generally accepted. Herein, we propose a quantitative method to measure human AKR proteins in cells using mTRAQ-based multiple reaction monitoring (MRM). AKR peptides with multiple transitions were carefully selected upon tryptic digestion of the recombinant AKR proteins, while AKR proteins were identified by SDS-PAGE fractionation coupled with LC−MS/MS. Utilizing mTRAQ triplex labeling to produce the derivative peptides, calibration curves were generated using the mixed lysate as background, and no significantly different quantification of AKRs was elicited from the two sets of calibration curves under the mixed and single lysate as background. We employed this approach to quantitatively determine the 6 AKR proteins, AKR1A1, AKR1B1, AKR1B10, AKR1C1/C2, AKR1C3, and AKR1C4, in 7 different cancer cell lines and for the first time to obtain the absolute quantities of all the AKR proteins in each cell. The cluster plot revealed that AKR1A and AKR1B were widely distributed in most cancer cells with relatively stable abundances, whereas AKR1Cs were unevenly detected among these cells with diverse dynamic abundances. The AKR quantitative distribution in different cancer cells, therefore, may assist further exploration toward how the AKR proteins are involved in tumorigenesis. KEYWORDS: MRM, AKR, mTRAQ labeling, absolute quantification, cancer cell lines

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findings have not been reproduced solidly in other large-scale screenings in genomics, transcriptomics, and proteomics. In a study using immunohistochemistry (IHC) Lin et al. reported AKR1C3 was consistently overexpressed in ductal carcinoma tissues of breast cancer in situ.6 In another study following a similar strategy, Ji et al. achieved the opposite conclusion that selective loss of AKR1C1/C2 was found in 24 paired breast cancer tissues, whereas AKR1C3 was only minimally affected in the same samples.7 As well as these examples, while other AKR members, including as AKR1A1, AKR1B1, AKR1C1, AKR1C2 and AKR1C4, have been detected with abnormal expression in various cancer tissues or cells,8−11 these findings have not been easily replicated or further validated by different approaches or laboratories. These inconsistent observations in the role of AKRs and cancer highlight the need to be able to quantitatively evaluate the AKR abundances in cancer cells and tissues. To solve this issue there are three major questions that need to be addressed.

INTRODUCTION Aldo-keto reductases (AKRs), belonging to a highly conserved enzyme superfamily comprising 14 families and more than 150 members in different species, are key enzymes for detoxification of reactive aldehydes.1 To date 13 AKR proteins have been identified in humans, including AKR1A1 (aldehyde reductase), AKR1B1 and B10 (aldose reductases), AKR1C1, C2, C3, and C4 (hydroxysteroid dehydrogenases), AKR1D1 (Δ4-3-ketosteroid-5-β-reductase), AKR6A3, A5, and A9 (Kvβ proteins), and AKR7A2 and 7A3 (aflatoxin reductases).2 In recent years it has become increasingly evident that many human AKRs are intimately linked with cancer biology; the evidence of which AKR member is related to a particular cancer type, however, is still not clear and in some cases contradictory. For example, it is reported that the overexpression of AKR1B10 was considered as a novel biomarker for non-small-cell lung carcinoma (NSCLC).3−5 The association of AKR1B10 in NSCLC with smoking has been established by microarray analysis and confirmed by reverse transcription PCR, immunoblots and immunohistochemistry in paired samples of squamous cell carcinomas and noncancerous tissue. Contrasting this, these © 2013 American Chemical Society

Received: August 7, 2012 Published: April 2, 2013 2022

dx.doi.org/10.1021/pr301153z | J. Proteome Res. 2013, 12, 2022−2033

Journal of Proteome Research

Article

homology, how are these unique peptides selected for getting higher transitions in MRM? Dealing with multiple targets in different sample backgrounds, how are the universal calibration curves generated for absolute quantification? If IS is introduced in mTRAQ/MRM, how are the MS signals acquired for accurate quantification? With the aim of quantifying AKR proteins, we selected 6 human AKR proteins, AKR1A1, AKR1B1, AKR1B10, AKR1C1/C2, AKR1C3 and AKR1C4, which all have been previously reported to have involvement in cancers. To look at a broad variety of cancer cells putatively expressing the AKR proteins, we selected 7 cancer cell lines, Huh7, A549, H157, BGC823, 786-O, SW480 and 5637, which were derived from 6 different tissues. After carefully evaluating the transition status of the potential AKR peptides for quantification, we labeled the peptides with mTRAQ and created the standard calibration curves of each target transition against the background of the lysate mixture comprising all cancer cell lines and estimated the absolute abundances of AKRs upon MRM signals and calibration curves. We therefore established a general quantitative comparison of the AKR members in cells and provided AKR abundant distributions across the 7 cancer cell lines.

First, most studies about AKR protein levels have been limited to the measurement of one or a few of AKR members and lacked overall information for all the AKRs. As many AKR enzymes convert the substrates following the similar catalytic mechanism in vivo,12 it is likely that the AKR enzyme activities highly correlated with their protein abundances. Moreover, it is reported that some AKR protein members are able to compensate for each other.13,14 Focusing on an individual AKR rather than a broad overview of the AKR family may misinterpret the biological significance of AKRs in cancers. Secondarily, when globally screening tumor-associated genes, most traditional techniques such as DNA/RNA microarrays and protein profiling are often short of accurately quantitative information, meaning that this screening data is not always appropriate in quantitative comparison of targeting biomarkers. Third, most traditional approaches for identifying the tumorassociated AKRs are antibody-based, such as Western blotting or immunohistochemistry (IHC). These classic methods are generally able to detect an antigen of interest, but are obviously limited in their ability to accurately quantify because of diverse specificities and titers of antibody. The ability to globally profile and quantitatively identify AKR proteins, therefore, is a key issue in being able to further and more accurately scrutinize the relationship of AKRs and cancer. The emergence of multiple reaction monitoring (MRM) in quantitative proteomics has led it to become the main method for target proteomics.15 Compared to antibody-based techniques such as Western blot or ELISA, MRM offers a better alternative for quantifying protein abundances using LC or SDS-PAGE to separate proteins, especially when lacking of suitable antibody and requiring highly precise quantification.16,17 Internal standards (IS) are generally required for MRM assays because they provide normalization of MRM peak areas across many samples and enable more reproducible quantification.16 Application of synthetic peptides for generation of calibration curve with IS is a common approach;18 on the other hand, this approach is complicated and timeconsuming dealing MRM with multiple peptide indicators or many samples with different backgrounds. An alternative IS strategy using a labeling reagent has been proposed by AB SCIEX, termed mTRAQ.19,20 Using a similar chemistry structure to iTRAQ, mTRAQ with triplex labeling can globally label sample digests with >95% efficiency and use the labeled peptides as IS. The mTRAQ technique basically serves relative quantification.21−23 For instance, Kang22 used mTRAQ and cICAT to label a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues and quantified a total of 3688 proteins. Siu’s group tried to employ mTRAQ-labeled synthetic peptides with known quantity as internal spiked standards and to measure the absolute proteome on the basis of single-point calibration.19 However, the data accuracy upon single-point calibration is somehow questionable, particularly in many samples with different target abundances, while adjustment of the target abundances in many samples to a comparable level is unfeasible in experiment. In the present study, we initiated the absolute quantification of AKR proteins in different cancer cell lines using the mTRAQ/MRM approach. To date there have been no reports on how to quantitatively determine the abundances of protein family members using such a technique, particularly for absolute quantification. With this mind, special care was taken to evaluate several potential obstacles to the technique. Among the protein family members shared with high

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METHODS

1. Preparation and Selection of the AKR Peptides

Recombinant AKR proteins were expressed by Escherichia coli with His-tags and purified by Ni affinity (Invitrogen, Carlsbad, CA, USA). After reduction with 10 mM DTT for 60 min at 56 °C and then alkylation with 55 mM iodoacetamide for 45 min in the dark at room temperature, the purified proteins were separated by SDS-PAGE (13%). The bands corresponding to the theoretical molecular weight of around 37 kDa were excised and in-gel digested by trypsin overnight. The tryptic peptides were carefully extracted by ACN from the gel particles followed by freeze-drying and reconstitution in 0.1% formic acid (FA). Digested peptides were scanned by a QTRAP5500 mass spectrometer (AB SCIEX, Foster City, CA, USA) in EMS-EPI mode, and the MS/MS data was analyzed with Proteinpilot (Vision 4.0). The identified peptides were further analyzed by MRMpilot (vision 2.1) to select the optimum AKR peptides for MRM. The peptides selected for MRM followed the following criteria: (1) the AKR peptides with unique sequence in the human genome; (2) a maximum m/z of peptide ≤1000 (limitation of LIT scan), with a peptide length range 7−20 aa; (3) without C or M in peptides; and (4) no missed cleavage of trypsin. The MRM transitions for those selected peptides were chosen on the basis of the experimental MS/MS spectra searched in ProteinPilot. If an AKR protein lacks the qualified EMS-EPI results, some AKR peptides based on theoretical prediction have to be taken. To verify the peptides chosen, the AKR peptides were rescanned by MRM-EPI. Finally, all the qualified transitions from the same peptide should share almost identical RT time and similar chromatographic behaviors. On the basis of the amino acid sequence information for the selected AKR peptides, the peptides were synthesized from SciLight Biotechnology, Beijing, China. Synthetic peptides with a chemical purity over 98% were suitable for quantification experiments. 2023



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2. Comparison of the Peptides Detected by MRM between the Unfractionated and SDS-PAGE Separated Cell Lysates

concentrations as internal standard (IS) with mTRAQ 04, and the peptides generated from the mixed seven lysates or the individual lysate with mTRAQ 08. The IS labeled with mTRAQ 04 was spiked in all the runs to normalize MRM signals. The calibration curves were generated using the different concentrations of synthesized peptides with mTRAQ 00 against the background of the mixed lysates with mTRAQ 08 or the single lysate with mTRAQ 08. The AKR abundances were estimated upon the corresponding MRM signals with mTRAQ 08 against calibration curve.

Seven human tumor cell lines were selected in the study, hepatocellular carcinoma (Huh7), lung adenocarcinoma (A549), lung squamous carcinoma (H157), bladder carcinoma (5637), colon adenocarcinoma (SW480), stomach adenocarcinoma (BGC823), and kidney adenocarcinoma (786-O). The cell lines SW480 and BGC823 were cultured in the DMEM medium (Gibco, Grand Island, CA, USA), while the other cancer cell lines were cultured in the RPMI1640 medium (Gibco, Grand Island, CA, USA). All the cell lines were cultured in medium with 10% fetal bovine serum (PAA laboratories, Pasching, Austria) at 37 °C and in 5% CO2. The cells were harvested at >85% confluence followed by scraping and centrifugation to collect the cells. The cells were lysed using the buffer 40 mM Tris-HCl pH8.5 with 1 mM PMSF and 2 mM EDTA (Sangon, Shanghai, China) followed by sonication. The lysate were centrifuged at 20000g for 30 min, and the resulted supernatant was reduced with 10 mM DTT for 60 min at 56 °C and then alkylated with 55 mM iodoacetamide for 45 min in the dark at room temperature. The alkylated lysate was precipitated using cold acetone and reconstituted with 8 M urea in 40 mM Tris-HCl, pH 8.5. Protein concentrations were measured using Bradford assay (Beijing Cowin Biotech Co., Ltd., Beijing, China) with bovine serum albumin as protein standard. The part of Huh7 alkylated lysate was divided into two groups for fractionation and unfractionation comparison. One was digested in solution, while the other was loaded on SDSPAGE gels followed by band excision and tryptic digestion in the gel. For in-solution digestion, the lysate was first diluted by 0.5 M TEAB till the concentration of urea was down to 1 M and then digested by trypsin (Promega, Fitchburg, WI, USA) at 37 °C overnight. For in-gel digestion, the lysate protein was loaded onto 13% SDS-PAGE. After Coomassie blue staining, the bands between 31 and 43 kDa were carefully excised followed by extensive destaining with 25 mM NH4HCO3 and 50% ACN and dehydration. The dried gel particles were reconstituted by 0.5 M TEAB and digested by trypsin for overnight. The peptides generated from digestion either in solution or in gel were used in MS for MRM measurement. The MRM transitions for fractionation comparison are listed in Table S1 (Supporting Information). The MRM transition numbers and the experimental reproducibility were taken in account for comparison. Moreover, both processes, SDS-PAGE separated and unfractionated, were repeated three times, and the four transitions of VAIDAGYR derived from AKR1B10 were monitored for reproducibility analysis.

4. Detection of the AKR Peptides Using LC−MS

The analyses for all the experiments were performed on a QTRAP5500 mass spectrometer (AB SCIEX, Foster City, CA, USA) equipped with an Eksigent NanoLC ultra pump (Eksigent Technologies, Dublin CA, USA). The Mobile phase consisted of solvent A, 0.1% aqueous formic acid and solvent B, 98% acetonitrile with 0.1% formic acid. Peptides were separated on a nanocapillary column (XD-5017, ID75 μm, diameter of particles 3 μm, pore size 200 Å, length 15 cm, Zhengdan, Beijing, China) at 300 nL/min, and eluted with a gradient of 3−10% solvent B for 2 min, 10−30% solvent B for 8 min, and followed by 30−90% solvent B for 3 min. In QTRAP5500, the parameters for all the MRM experiments were set as ionspray voltage, 2800 V; curtain gas, 30.00; ion source gas 1, 10.00; ion source gas 2, 0.00; collision gas, high; interface heater temperature, 150; declustering potential, 100.00; entrance potential, 10.00; Q1 and Q3, unit resolution. Total of 15 target peptides of β-galactosidase at transition per peptide were daily assessed as a QC for MRM. When the MRM peak of the highest signal in QC standard fell below 1 × 105, the IS and GS1 would be readjusted. In the EMS-EPI mode, the digested peptides of AKR were scanned with the collision energy (CE) calculated by a series equation, CE = a·m/z + b, based on the m/z and the charge state of parent ions, in which the parameter pairs of a and b were set as 0.044 and 6 for ions with unknown charge state, 0.058 and 10 for singly charged ions, and 0.044 and 9 for doubly charged ions. In MRM-EPI mode, the MRM peaks for each transition were acquired by setting gradient CEs at a step of 3 eV center-around theoretical CE, and the best CEs were defined as the CE values responding to the highest MRM signals for each transition using MRMPilot (AB SCIEX, Foster City, CA, USA), while the triggered MS2 spectra in EPI mode was used for peptide identification. In MRM mode, the best CEs were applied in routine procedures of acquiring MRM signals. 5. Data Processing and Statistical Methods

The raw data of MRM was processed using Multiquant software 2.0.2. SignalFinder1 (AB SCIEX, Foster City, CA, USA) with saturation correction at 3.5 × 106 applied to calculate the corresponding peak areas of MRM signals. By setting S/N > 10 and LOQ as a threshold, the filtered MRM signals were qualified for quantitative calculation. The data analyses were performed by the programs written in R language (www.r-project.org/), and the statistical analysis in the study was based on the absolutely quantitative results of individual transitions. Specifically, the calibration curves were constructed using the lqs function in the MASS package (four parallel data), the boxplots were generated by the bwplot function of lattice package, and the cluster plots were produced by the heat.map2 function of gplots package. To describe the distribution of the AKR abundances in the different cell lines, the values of log2

3. mTRAQ Labeling Strategy for Absolute Quantification in 7 Cell Lines

The synthetic peptides and the digested peptides derived from in-gel digestion were treated with mTRAQ reagent following the protocol recommended by the manufacturer (AB SCIEX, Foster City, CA, USA). Then, the CEs were optimized for each transition. The labeling efficiency of mTRAQ was examined by comparing the changes of the transition peak areas for all the selected AKR peptides before and after labeling reaction, which were monitored under MRM scan mode. The strategy of triplex mTRAQ labeling was conducted to quantify the AKR abundances in different cancer cell lines. The synthesized peptides with varied concentrations were labeled with mTRAQ 00, the synthetic peptides with certain 2024



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Table 1. Comparison of the Parameters of Calibration Curves for the AKR Peptides in the Huh7 and the Mixed Lysate As Backgrounda Coef.a

Coef.b

Rsq

LOQ (fmol)

dynamic range

Protein_Peptide_Transtions

CE

Huh7

mix

Huh7

mix

Huh7

mix

Huh7

mix

Huh7

mix

AKR1A1_EELFVTSK_y3 AKR1A1_EELFVTSK_y4 AKR1A1_EELFVTSK_y5 AKR1A1_EELFVTSK_y6 AKR1A1_SPAQILLR_y3 AKR1A1_SPAQILLR_y4 AKR1A1_SPAQILLR_y5 AKR1A1_SPAQILLR_y6 AKR1B10_NVIVIPK_b2 AKR1B10_NVIVIPK_y2 AKR1B10_NVIVIPK_y4 AKR1B10_NVIVIPK_y5 AKR1B10_VAIDAGYR_y4 AKR1B10_VAIDAGYR_y5 AKR1B10_VAIDAGYR_y6 AKR1B10_VAIDAGYR_y7 AKR1B1_NLVVIPK_b2 AKR1B1_NLVVIPK_y2 AKR1B1_NLVVIPK_y4 AKR1B1_NLVVIPK_y5 AKR1B1_VAIDVGYR_y4 AKR1B1_VAIDVGYR_y5 AKR1B1_VAIDVGYR_y6 AKR1B1_VAIDVGYR_y7 AKR1C1/C2_AIDGLNR_b2 AKR1C1/C2_AIDGLNR_y2 AKR1C1/C2_AIDGLNR_y4 AKR1C1/C2_AIDGLNR_y5 AKR1C3_AIDGLDR_b2 AKR1C3_AIDGLDR_y2 AKR1C3_AIDGLDR_y4 AKR1C3_AIDGLDR_y5 AKR1C4_VLDGLNR_b2 AKR1C4_VLDGLNR_y4 AKR1C4_VLDGLNR_y5 AKR1C4_VLDGLNR_y6

22 20 25 24 25 32 28 27 19 22 18 16 28 21 14 20 16 26 19 20 26 19 16 20 19 22 28 21 19 22 20 20 27 36 30 30

1.00 1.04 1.02 1.02 0.96 0.94 0.97 0.97 1.08 0.94 1.05 0.94 0.92 1.00 0.97 0.97 0.99 0.98 1.08 1.05 0.69 0.63 0.63 0.63 0.96 0.94 0.90 0.99 0.94 0.96 0.98 0.99 1.00 0.97 1.00 0.99

0.97 0.99 0.96 0.98 0.96 1.00 0.98 0.99 0.98 1.06 0.98 1.00 0.99 0.96 0.99 1.01 1.00 0.96 0.99 1.03 0.80 0.63 0.73 0.81 0.97 0.94 0.94 0.97 0.96 0.92 0.98 0.97 0.94 0.97 0.96 0.99

−1.75 −1.89 −1.83 −1.84 −1.90 −1.92 −1.89 −1.77 −2.06 −1.82 −1.96 −1.63 −1.64 −1.85 −1.75 −1.77 −1.90 −1.97 −2.13 −2.10 −0.75 −0.64 −0.66 −0.67 −1.85 −1.79 −1.77 −1.89 −1.78 −1.79 −1.83 −1.87 −1.97 −1.94 −1.98 −1.97

−1.72 −1.80 −1.72 −1.80 −1.95 −2.10 −1.97 −1.93 −1.93 −2.01 −1.83 −1.87 −1.83 −1.78 −1.87 −1.87 −1.98 −1.95 −1.92 −2.06 −1.13 −0.67 −0.84 −1.13 −1.91 −1.83 −1.81 −1.89 −1.86 −1.77 −1.85 −1.86 −1.86 −1.95 −1.92 −1.99

0.99 1.00 1.00 1.00 0.99 0.99 1.00 1.00 0.98 0.99 1.00 0.98 0.99 1.00 0.99 1.00 0.99 0.99 1.00 0.99 0.97 0.95 0.96 0.96 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.97 0.98 0.99 0.99 1.00 0.99 0.99 1.00 0.99 0.99 0.99 0.99 0.94 0.97 0.97 0.97 1.00 1.00 0.93 1.00 1.00 1.00 0.99 1.00 1.00 1.00 1.00 1.00

7.5 15 15 15 79.62 79.62 79.62 79.62 1.83 1.83 9.13 9.13 1.65 1.65 1.65 1.65 1.83 1.83 9.13 9.13 1.6 16.02 1.6 1.6 1.89 9.43 1.89 9.43 9.41 18.83 1.88 9.41 9.09 1.82 1.82 1.82

7.5 15 7.5 15 79.62 79.62 79.62 15.92 9.13 1.83 9.13 9.13 1.65 1.65 1.65 1.65 9.13 1.83 9.13 9.13 1.6 8.01 8.01 1.6 9.43 18.85 1.89 1.89 9.41 18.83 1.88 9.41 9.09 1.82 1.82 1.82

100 100 100 20 10 10 20 20 1000 100 100 100 1000 100 500 500 200 1000 100 20 1000 100 1000 100 1000 100 1000 200 200 20 100 200 200 200 200 200

100 100 20 100 20 20 20 100 100 100 100 100 100 500 100 100 50 100 50 10 50 100 100 1000 100 100 100 1000 100 20 100 100 200 500 100 500

a

Note: The signs of coef.a and coef.b represent the parameters of linear regression log2(concentration) = a·log2(peak area ratio) + b. LOQ is the abbreviation for limit of quantification. The transitions in bold are selected for comparison of the AKR abundances based upon single or two peptides per AKR protein.

the absolute quantity of each AKR transition were used for generation of cluster plots.

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according to the criteria for selecting the MRM peptide, we predicted the AKR transitions with MRMpilot and used MRMEPI mode (MRM triggered enhanced product ion scan) to acquire more qualified peptides. Combining the scanning results from EMS-EPI and MRM-EPI from the AKR recombinant peptides, two unique peptides from each AKR member were defined except AKR1C1 and AKR1C2. Since AKR1C1 and AKR1C2 shared 98% identity, it is difficult to identify a unique peptide that can clearly distinguish AKR1C1 from AKR1C2. Therefore, AKR1C1 and AKR1C2 are represented by their shared peptides, which are also exclusive to them for quantification and denoted as AKR1C1/C2. All the information of the qualified AKR peptides is listed in Table S2 (Supporting Information).

RESULTS

1. Selection of the Unique AKR Peptides and the Corresponding Transitions

According to the general principles for the selected peptides described in the Methods, there are difficulties in selecting multiple unique peptides of AKR as the candidates for MRM. The members of the AKR protein family have highly homologous amino acid sequences, especially for AKR1C proteins that have average amino acid identity of over 86%. To select transitions based on experimental spectra, we first used data-independent acquisition to detect all the digested peptides of each AKR protein in EMS-EPI scan (the shotgun scan preformed on QTRAP5500, short for the combination of enhanced MS and enhanced product ion scans). Furthermore,

2. mTRAQ-Labeling Efficiency and Optimization of the MS Parameters to Detect the AKR Peptide Transitions

The labeling efficiencies of mTRAQ to these peptides were carefully evaluated. As listed in Table S2 (Supporting 2025



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Figure 1. Comparison of the AKR peptides detected by MRM in the lysates treated with/without fractionation. (A) Detection ratios of the AKR transitions in the 7 cancer lysates treated with/without fractionation. (B) Comparison of the experimental errors induced by fractionation and unfractionation. The AKR1B10 peptide, VAIDAGYR, in Huh7 was selected for the comparison experiment.

used SDS-PAGE and gel-band slicing to simplify the protein contents and enrich the AKR proteins. After loading 1 μg of tryptic peptides extracted from the gel bands, the detection rates to the transitions listed in Table 1 were varied from 25.0 to 62.5% in these 7 cancer cell lines (Figure 1A), the average rate being about 45.3%. The detection rates of all the AKR peptides upon the fractionation enrichment in all the cancer cell lines were thus dramatically improved, implicating that reduction of the complexity of proteins in the samples could benefit the AKR quantification with MRM. Once an additional preparative step is added into the treatment of lysate proteins, a key question to ask is whether the additional step induces more experimental errors. We further evaluated the reproducibility of the identification rates to the unique AKR peptides, by setting a parallel comparison to the lysate proteins treated with/without SDS-PAGE fractionation. The four transitions of VAIDAGYR derived from AKR1B10, which was highly detectable in Huh7, were chosen in such comparison experiment. As shown in Figure 1B, the average CV value induced by the fractionation approach is approximately 11%, while the value from the direct shotgun approach is about 9% (N = 3). Although the CV value without fractionation was slightly higher than that of fractionation, an average CV value less than 15% is commonly accepted for quantitative proteomics. Therefore, we decided to carry on the fractionation treatment of the lysates of cancer cells in all following MRM experiments.

Information), the majority of the synthesized AKR peptides were successfully labeled by mTRAQ with high reaction efficiency (>97%). However, with unclear reason two AKR peptides failed being labeled by mTRAQ, DIVLVAYSALGSHR for AKR1C1/C2 and DIVLVAYSALGSQR for AKR1C3. These technical difficulties forced us to reconsider the MRM strategy to quantify the AKR proteins. With the limitations of the number of unique AKR peptides and the labeling efficiency of mTRAQ, we therefore designed a two-steps MRM quantification strategy for the AKRs. First, one unique peptide for each AKR protein with multiple transitions was used to quantify the absolute AKR abundances. Second, we compared and evaluated the quantitative and statistical results based on one and two peptides per protein for AKR1A1, AKR1B1 and AKR1B10, which had two qualified unique peptide available. Improving the daughter ion intensities is an important issue in allowing sensitive detection of peptides with MRM. By optimizing CEs corresponding with the highest signals of transitions, we obtained the most appropriate CEs for all the transition candidates. In turn, we selected four transitions for each peptide with the highest mass intensity. The optimized CEs for these transitions are summarized in Table 1. 3. Fractionation of the Lysate Proteins for Quantitation of the AKR Proteins

Generally a better quantification requests that sample preparation be as simple as possible to reduce protein loss as well as operation errors. We first adopted shotgun digestion with trypsin to the lysates of the cancer cells and directly loaded the tryptic digestions to LC−MS/MS. When 5 μg of digested peptides were loaded to HPLC, the transitions listed in Table S1 (Supporting Information) indicate the detection rates varied from 2.5 to 42.5% in 7 different cell lines (Figure 1A), and the average detection rate in these cell lines is approximately 23.1%. A number of expected AKR peptides, however, were not detected by the approach. For example, although AKR1C1/C2 was reported highly expressed in Huh7,24 the corresponding peptide was not detected in the same cell lysate by MS. Taking complexity in the mixture of digested peptides and common molecular mass shared in most AKR proteins into account, we

4. Comparison of the Calibration Curves in the Backgrounds Derived from the Mixed and Single Lysate

To achieve an appropriate range of AKR calibration, we made the effort to expand the concentration range of the synthetic peptide in calibration and to make the calibration assays under a background as close to cell lysate as possible. To increase the limit of quantitation (LOQ) and reproducibility of calibration curves, we first obtained a series of data of the MRM peak area ratios, in which the peak areas of target synthetic peptides were normalized by each IS. The calibration curves were generated by the peak area ratios vs the peptide concentrations instead of the peak areas vs the peptide concentration. With such data 2026



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treatment, the LOQ of target peptides and linear ranges of the calibration curves were satisfactorily improved. As the endogenous AKR abundances in different cancer cells varied in a large dynamic range, while establishment of calibration curves, cells by cells, upon choosing the endogenous peptides as IS would be time-consuming and complicated in experiments, we thought that the mTRAQ approach was an appropriate solution to these problems, because it serves global normalization and common matrix effect. A typical MRM chromatogram for all the selected transitions of the synthesized AKR peptides is depicted in Figure S1 (Supporting Information). To mimic the matrix effect of multiple samples, we proposed that a mixed lysate derived from multiple cells could be treated as background to represent the average interference from all the cells. The established calibration upon IS and the matrix labeled by mTRAQ was expected to be fit into MRM quantification in all the cells. In light of this, we conducted a comparison experiment to test this strategy feasibility, in which the AKR calibration curves were generated against the background from either the single cell lysate (Huh7) or the mixed cell lysate, consisting of all 7 cell lysates. During calibration, the MRM signals selected for calibration should meet 3 prerequisite conditions: (1) a positive signal should have a S/N > 10, (2) a positive transition should be detected at least 3 times in 4 injections, and (3) if more than 3 repeats are detected, the qualified area ratio of each repeats should fall into their average ±1.5 standard deviation. We produced the calibration curves for all synthetic peptides seen in Figure S2 (Supporting Information) and listed the calibration parameters in Table 1. All calibration curves from the 36 peptides in the two different backgrounds are fitted well with a linear regression of R2 = 0.99. Moreover, as seen in Figure S3 (Supporting Information), the dynamic ranges of the two calibration sets are very similar, with approximately 80% of calibration curves falling into 3 orders of magnitude and the others into 2 orders. Furthermore, if the lowest peptide concentrations on the curves with their quantification CVs ≤ 25% (N ≥ 3) are set as LOQs, all the transitions could be detected with a range of 1.44−82.00 fmol. More importantly, the distributions of LOQs generated from the two different backgrounds are similar, suggesting that the background differences, at least in such selected cell lines, have little effect on the MRM sensitivity to these peptides. As can be viewed in Figure 2, the transition signals of the mTRAQ labeled peptides measured in Huh7 cell lines are applied into the two sets of calibration curves. The results show high correlation with R2 = 0.99 between the two data sets, indicating that quantifications based on the two calibrations were comparable. From these findings we conclude that adopting triplex labeling for calibration with the mixed lysate as a general background is an effective solution to evaluate the varied AKR abundances in different cell lines.

Figure 2. Comparison of the AKR abundances in Huh7 based on MRM measurement against the two different backgrounds. The AKR MRM signals were acquired in the Huh7 lysate labeled with mTRAQ, and the corresponding AKR abundances were calculated upon the calibration curves of the synthetic AKR peptides, which were generated from the two different backgrounds, single and mixed lysate as described in the Methods. The correlation between the two sets of the AKR abundances were evaluated by linear regression (R2 = 0.99).

good quality in data accuracy with over 80% transitions having CV < 5% (Figure S4, Supporting Information), demonstrating that MRM is an appropriate choice in quantitative proteomics for protein family members; (2) in most cells examined in this study except Huh7, the abundances of the AKR proteins in each cell are within relatively narrow dynamic range, approximately 2 orders of magnitude; and (3) for a specific AKR, its abundances in different cancer cell lines are in relatively large dynamic range approximately over 3 orders of magnitude. We adopted box plots to describe the quantitative distribution of AKR proteins in the different cancer cell lines. As depicted in Figure 3, the box plots present the distribution modes of different AKRs in a certain cancer cell line and of a certain AKR in different cancer cells. In Figure 3A, comparing the AKRs distributions in all the cancer cell lines suggests two cell lines possessing relatively higher AKR abundances, A549 containing high values in AKR1B1, AKR1B10, AKR1C1/C2 and AKR1C3 with narrow dynamic range, with Huh7 having the highest levels of AKR1B10, AKR1C1/C2 and AKR1C3 in all of the cell-lines examined with wide dynamic range. Except for AKR1C4, the AKRs are generally perceived in most cancer cells. Alternatively, the abundant distributions of AKRs are likely a criterion to distinguish the different cancer cell lines. In Figure 3B, the distribution modes of AKRs among cancer cell lines reveals that the AKR1A and AKR1B proteins are widely distributed in most cancer cells tested, whereas the abundances of AKR1A1 and AKR1B1 were detected within relatively narrow dynamic range of less than 2 orders of magnitude, and that of AKR1B10 was found in larger dynamic range with more than 3 orders of magnitude. With regards to the AKR1C proteins, they are not evenly distributed among the cancer cells, especially the dynamic ranges of AKR1C1/C2 and AKR1C3 at approximately more than 3 orders of magnitude.

5. Absolute Quantities of AKR Abundances in Cancer Cell Lines

As described previously, we took the single unique peptides from each AKR with the four transitions to quantitatively evaluate the AKR abundances in cancer cell lines. Table 2 summarizes that the absolute quantities of AKR abundances distribute across in the 7 cancer cell lines. For the first time this table presents whole set of quantitative abundances for the AKR proteins in cells. (1) The absolute quantities of AKRs in such cancer cell lines measured by mTRAQ/MRM display 2027



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Table 2. Absolute Quantification Results of AKR Proteins in 7 Cancer Cell Linesa protein

AKR1A1

AKR1B1

AKR1B10

AKR1C1/C2

AKR1C3

AKR1C4

peptide

SPAQILLR

NLVVIPK

VAIDAGYR

AIDGLNR

AIDGLDR

VLDGLNR

sample

mean

SD

mean

SD

mean

SD

mean

SD

mean

SD

mean

SD

5637 786-O A549 BGC823 H157 Huh7 SW480

1.24 2.47 1.14 − 0.96 3.03 2.90

0.02 0.19 0.05 − 0.06 0.16 0.15

0.76 7.97 36.50 1.70 3.89 2.49 −

0.07 0.44 0.93 0.07 0.15 0.10 −

0.18 0.11 28.70 0.48 0.38 197.00 −

0.07 0.00 0.44 0.03 0.06 9.83 0.11

− − 7.67 0.08 0.08 39.10 0.18

− − 0.38 0.00 0.01 2.52 0.01

− − 17.60 0.35 − 24.90 0.76

− − 1.91 0.01 − 13.80 0.04

− − − − − 0.08 −

− − − − − 0.01 −

a Note: Absolute AKR abundances are evaluated on the basis of the unit as fmol/μg, which means fmols of the individual AKR in 1 μg of cell lysated proteins.

Figure 3. Distributions of the AKR abundances in 7 cancer cell lines represented by box plot. In each box, the spots represent medians, and the tops and bottoms indicate the 25th and 75th percentiles of the correspondent AKRs in samples. (A) The AKR proteins distributed in individual cancer cell line and (B) individual AKR protein distributed in 7 cancer cell lines.

6. Quantitative Evaluation of the AKR Abundances Using mTRAQ/MRM with Two Unique Peptides Per AKR Protein

Although the CV values in most transitions are accepted in the two sets of MRM data, 30% of the quantitative results based on the two different peptides show a large variances with CV > 40%. For instance, the AKR1B1 abundances in these cells quantified upon the peptide of NLVVIPK are generally higher than of VAIDVGYR, while the AKR1B10 abundances quantified upon the peptide of NVIVIPK are globally less than of VAIDAGYR. The conflicted MRM quantification obtained from the different peptides of the same protein is not rare.25 As the mTRAQ labeling efficiency was carefully evaluated in sample preparation, the quantification differences were unlikely to be caused by the labeling process. However, since we quantified the standard peptides by weighing them on a microbalance, which was not as accurate as amino acid analysis, the errors introduced by the relatively rough quantity of standard peptides may cause the different quantification results of the peptides from the same protein. We also suspect that the quality of the synthesized peptides, the differential digestion rates, the peptide recovery, or the instability of the related AKR peptides may lead to the conflicting data. Just relying on such data, it is difficult for us to determine which quantitative data truly represent the absolute quantities of AKRs in these cells. On the other hand, the most important

Because of many peptides generated from tryptic digestion of a protein, it is generally accepted that choosing multiple unique peptides can improve the quality of protein quantification using MRM. As described above, the high homology of amino acid sequences among AKRs and the strict criteria to select the qualified AKR transitions for MRM limited our ability to select multiple peptides per AKR in this study. We thus had to use single unique peptide with multiple transitions to quantify the AKR abundances in the cancer cells at the first step. An obvious issue raised by this is whether the quantitative data elicited from single unique AKR peptide is different from that obtained by multiple unique AKR peptides. Therefore, we designed a comparison experiment in which the two unique peptides per AKR protein from AKR1A1, AKR1B1, and AKR1B10 were selected, and took them for AKR quantification in the 7 cancer cell lines. From Table S3 (Supporting Information), which presents a comparison of AKR abundances derived from the MRM upon one and two unique peptides per AKR protein, we found that the two sets of quantitative data obtained from the different one unique AKR peptide were not well matched. 2028



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Figure 4. The cluster plots of three absolute AKR abundances, AKR1A1, AKR1B1 and AKR1B10 in 7 cancer cell lines based on the MRM measurement with single (A) or two (B) unique peptides per AKR. The color keys located at the top left side on each cluster plot represent the gradient of log2 the absolute quantity of each AKR transition, from lower (blue) to higher (yellow).

7. Biological Significance of Cluster Plots against the Quantitative AKRs and Cancer Cell Lines

issue is whether the two sets of data are useful to evaluate the AKR abundant distributions in these cells and to distinguish the AKR abundant patterns among different cell lines. As shown in Figure S5 (Supporting Information), the ratios between any two AKRs’ absolute quantities in the same cancer cell lines were statistically evaluated on the basis of quantification from either one or two peptides per AKR. Over 90% of ratios from the two sets of data were comparable with p < 0.05. Therefore, one peptide per protein for absolute quantification was acceptable to study the AKR distribution pattern in each cancer cell lines. Clustering is the most popular method used in the first step of gene expression matrix analysis.26 It reduces the dimensionality of the measured values and groups together objects with similar properties. Since the AKR abundances in 7 cancer cell lines were measured by different peptides with multiple transitions in MRM, the clustering approach was expected to provide the statistical views of the abundant distributions of AKRs in such cell lines. We hierarchically clustered the transitions from the three AKRs by their absolutely quantitative results in all the cancer cell lines. Figure 4A represents the cluster plot from the AKR quantification upon one peptide and reveals that most transitions in vertical axis are well clustered to the corresponding AKRs, and the cell lines in horizontal axis are generally clustered to two groups. Furthermore, we applied cluster analysis to all the transitions from the 6 unique peptides of the three AKRs in the cancer cell lines. As shown in Figure 4B, except EELFVTSK_y6 (AKR1A1) the other transitions in vertical axis are remarkably clustered to the AKR proteins as similar as Figure 4A. The result of horizontal clustering upon the two unique peptides is also similar to the cell line clustering pattern in Figure 4A, in which the 7 cancer cell lines are mainly categorized into two groups, Huh7 and A549 as one and the other cell lines as one group. The cluster comparison described above clearly indicates that the AKR abundances acquired from one or two unique peptides per AKR could not lead to different conclusions regarding the relationship of the AKR abundances and the cancer cell types.

As AKR MRM quantification upon one unique peptide could provide a comprehensive profile of the AKR distribution and AKR1Cs lack two qualified peptides for MRM, we performed cluster analysis to all the transitions of AKRs obtained from single unique peptide in 7 cancer cell lines in order to gain a deeper understanding of the relationship between the AKR abundances and different cancer cell lines. In Figure 5, the vertical axis of the plot shows that most AKR transitions are clustered together to their corresponding AKR peptides, suggesting transition similarity among each AKR peptide in all the cancer cell lines. Specifically, the AKRs are broadly clustered to two major groups. In a group containing AKR1A1 and AKR1C4, the abundances of AKR1A1 are

Figure 5. The cluster plot of the absolute abundances for all the AKR members in 7 cancer cell lines based on the MRM measurement. 2029



Article

not contribute to significant osmotic changes.14 These early observations have suggested that the AKR proteins might be functionally compensable for each other. Again, lack of an approach to globally monitor the AKR proteins is an obstacle that has prevented systematic exploration of the AKR’s function and roles. In our laboratory, we have developed an approach to globally estimate the AKR abundances based upon antibody recognition. Derived from bioinformatic analysis of the AKR amino acid sequences, several consensus peptides with different potential antigenicities were synthesized, conjugated with the carrier protein, and further delivered to generate polyclonal antibodies. An antibody, Pan-AKR-P4, was finally produced with high immunoaffinity to all the AKR proteins.31 When this antibody was used for 2-DE Western blotting to examine the AKR abundances in mouse liver and kidney, the semiquantitative data were in good agreement with other previous reports. This technique is useful to evaluate the sum abundances of the AKRs; nevertheless, it is useless to globally estimate and compare of the individual AKRs. Importantly, antibody-based measurement of AKRs offers relatively limited information in accurate quantification. In this study, we utilized the mTRAQ/MRM approach and quantitatively measured the absolute abundances of the individual AKRs in seven cancer cell lines. The results of this study for the first time offer the global distribution of AKR in different cell lines with large dynamic range and acceptable CV values (Figures S2 and S3, Supporting Information). The absolute quantification of AKRs thus makes it possible to globally and quantitatively evaluate the AKR contributions to the different cancer cells (Figure 3). It is reasoned that quantification of AKRs upon mTRAQ/MRM could be also used in many other research areas for overall and quantitative evaluation of the AKR proteins. While establishing this new protocol to quantify the AKR abundances using MRM, some technique challenges in MRM have to be carefully evaluated. The first issue was the need to choose the appropriate transitions for the target proteins, which decides sensitivity and specificity in MRM. For the sake of the better choice, we first used data-independent acquisition to scan all the digested peptides of each recombinant AKR protein in EMS-EPI mode. As some unique peptide signals did not meet the IDA threshold to trigger MS2 scan, probably due to signal suppression by the shared peptides with more MS responses, we did not detect enough unique peptides that met our criteria for the MRM measurement to AKRs. Then, we predicted the transitions for the undetected unique peptides in silica and specifically traced up several transitions in MRM-EPI mode with lower threshold to trigger MS2 spectra. With the combination of the two EPI scans, we were able to achieve more qualified AKR peptides and transitions, which was critically important to monitor the MRM signals in a high homology protein family. The second issue was how to sensitively detect the target AKR peptides from the protein lysate. The complexity of the peptide mixture has always been a limiting factor for the identification as well as quantification of peptides. In general, sample fractionation is an effective way to simplify the components in the peptide mixture, such as immunoaffinity enrichment or depletion;32 however, the experimental errors induced by the fractionation operation may result in peptide loss and poor reproducibility. A characteristic of AKRs is that all members of the protein family share a close range of molecular masses of around 37 kDa without significant posttranslational modification leading to shift of molecular masses, and this is an advantage in

generally consistent in most cell lines, while AKR1C4 is absent in most cells except Huh7. The data implies that AKR1A1 with less specific distribution in the cells could not serve as a tissue specific biomarker, while as in agreement with previous study, AKR1C4 is a liver specific protein.27,28 In the other group having AKR1B1, AKR1B10, AKR1C1/C2 and AKR1C3, their abundances are extremely varied in different cancer cells. For example, AKR1B1 has low abundance in Huh7 but higher in 786-O; AKR1B10 possesses extremely high abundance in Huh7; and AKR1C1/C2 and AKR1C3 share similar distribution patterns in most cancer cells. Therefore, we deduce that all the AKRs in this group, in their abundances or distribution patterns, are likely useful to evaluate the different tumor cell lines. The horizontal axis of the plot illustrated in Figure 5 reaches the same conclusion drawn from Figure 4, in which the cancer cell lines are generally divided into two groups based on the AKR abundant distribution, one for Huh7 and A549, and the other for H157, SW480, 5637, 786-O and BGC823. The AKR abundances in these cells are quite diverse, even in the three cell lines all immortalized from the gastroenteric system, Huh7, SW480 and BGC823. Moreover, the AKR abundances of H157 and A549, both lung cancer cell lines, do not share the similar pattern. In the cells with relatively lower abundances of AKRs, the patterns of the AKR distribution seem to not offer a clear criterion to specify the cell type or the tissue source; however, in these cells having relatively higher AKR abundances such as Huh7 and A549, AKRs in abundance or abundant distribution could be regarded as potential indicators to further explore involvement of this protein family in such tumorgenesis.

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DISCUSSION Because of differing similarities of amino acid sequences, AKR superfamily is classified into several subfamilies.29 The AKR enzymes catalyze reactions on a broad and overlapping spectrum of substrates so that in vivo some AKRs share the same substrates. As an example, AKR1B1, AKR1B10, and AKR1C1 are all able to reduce 4-hydroxynonenal (HNE) to yield 1,4-dihydroxynonene, even with different catalytic efficiencies.30 AKR1C is the largest subfamily in AKR1 family, in which several members can interconvert potent androgens, estrogens, and progestins into their cognate inactive metabolites.24 Since several AKR enzymes can convert similar substrates during catalytic reactions in vivo, it is likely that their gene expression would be compensable once the abundance of an AKR is somehow attenuated. A global estimation of the AKR protein abundances with the common standards and protocols is therefore expected to clarify whether a single AKR or the action of several AKRs are important in cellular physiology. Supporting this, functional redundancy of AKRs has been observed by many investigators. The group of Chang utilized the budding yeast Saccharomyces cerevisiae as a model to systematically explore the physiological roles for yeast and mammalian AKRs and revealed that deletion of all the three AKR genes led to a marked change for sensitivity of heat shock, but deletion of single or any two genes of AKRs did not lead to any phenotypic change.13 AKR1B1 is thought to be important for protecting the renal epithelial cells against the hypertonic urine and a detoxifying reagent to remove various cytotoxic aldehydes such as methylglyoxal, 3-deoxyglucosone and HNE. In AKR1B1 knockout mice, however, their growth and reproductivity remained at normal level, and their kidneys contained only slightly increased levels of sorbitol, which could 2030



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absolute quantifications of AKRs acquired from one single peptide were different, more or less, from the other single peptide with relatively large CVs (Table S3, Supporting Information). Another important observation was that most CVs for transition quantification in every single peptide were well admitted with

Quantitative Analysis of the Human AKR Family Members in Cancer

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