Simultaneous Detection of Human C-Terminal p53 Isoforms by Single

Feb 6, 2018 - School of Pharmacy, Nanjing Medical University, 818 Tian Yuan East Road, ... China State Key Laboratory of Reproductive Medicine, Nanjin...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/ac

Cite This: Anal. Chem. XXXX, XXX, XXX−XXX

Simultaneous Detection of Human C‑Terminal p53 Isoforms by Single Template Molecularly Imprinted Polymers (MIPs) Coupled with Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/ MS)-Based Targeted Proteomics Wenting Jiang,† Liang Liu,† and Yun Chen*,†,‡ †

School of Pharmacy, Nanjing Medical University, 818 Tian Yuan East Road, Nanjing, Jiangsu, China, 211166 China State Key Laboratory of Reproductive Medicine, Nanjing, China 210029

‡

S Supporting Information *

ABSTRACT: Abnormal expression of C-terminal p53 isoforms α, β, and γ can cause the development of cancers including breast cancer. To date, much evidence has demonstrated that these isoforms can differentially regulate target genes and modulate their expression. Thus, quantification of individual isoforms may help to link clinical outcome to p53 status and to improve cancer patient treatment. However, there are few studies on accurate determination of p53 isoforms, probably due to sequence homology of these isoforms and also their low abundance. In this study, a targeted proteomics assay combining molecularly imprinted polymers (MIPs) and liquid chromatography-tandem mass spectrometry (LCMS/MS) was developed for simultaneous quantification of C-terminal p53 isoforms. Isoform-specific surrogate peptides (i.e., KPLDGEYFTLQIR (peptide-α) for isoform α, KPLDGEYFTLQDQTSFQK (peptide-β) for isoform β, and KPLDGEYFTLQMLLDLR (peptide-γ) for isoform γ) were first selected and used in both MIPs enrichment and mass spectrometric detection. The common sequence KPLDGEYFTLQ of these three surrogate peptides was used as single template in MIPs. In addition to optimization of imprinting conditions and characterization of the prepared MIPs, binding affinity and cross-reactivity of the MIPs for each surrogate peptide were also evaluated. As a result, a LOQ of 5 nM was achieved, which was >15-fold more sensitive than that without MIPs. Finally, the assay was validated and applied to simultaneous quantitative analysis of C-terminal p53 isoforms α, β, and γ in several human breast cell lines (i.e., MCF10A normal cells, MCF-7 and MDA-MB-231 cancer cells, and drug-resistant MCF-7/ADR cancer cells). This study is among the first to employ single template MIPs and cross-reactivity phenomenon to select isoform-specific surrogate peptides and enable simultaneous quantification of protein isoforms in LC-MS/MS-based targeted proteomics.

D

the development of cancers including breast cancer. For instance, several previous studies have indicated that isoforms β and γ, but not α, can induce the expression of insulin-like growth factor-binding protein 3 (IGFBP3),2 which plays an important role in breast cancer cell growth.5 Therefore, quantification of individual p53 isoform expression may help to link clinical outcome to p53 status and to improve cancer patient treatment. Given the great clinical potential of p53 isoforms, there are few studies on accurate determination of p53 and its isoforms, which may be attributed to low abundance of the protein isoforms6,7 and the limitation of traditional analysis, such as Western blotting, 6 ELISA, 8 and immunohistochemistry (IHC).9 While these antibody-based assays have advanced the research of p53 isoforms, they are normally high-cost, lack specificity, and reproducibility and mostly belong to semiquantitative analysis that shows poor consistency between

ecades of research has revealed that human cellular tumor antigen p53 encoded by tumor suppressor TP53 gene regulates cell-fate outcome such as growth arrest, differentiation, and apoptosis in response to cellular environment and stress.1 Recently, an additional layer of regulatory mechanism has emerged with the identification of p53 isoforms due to alternative promoters, splicing sites, and translational initiation sites in the TP53 gene. Among this layer, alternative splicing of intron-9 of TP53 produces three different Cterminal p53 isoforms α, β, and γ.2 Specifically, complete excision of intron-9 results in expression of isoform α with classical p53 C-terminal domain (oligomerization domain, OD) that is closely related to p53 regulation capability, while partial retention of intron-9 generates isoform β or isoform γ, in which the OD is replaced by 10 or 15 new amino acids, respectively (Figure 1).3 Although the mechanisms that control the alternative splicing of intron-9 are still unknown, much evidence has demonstrated that these C-terminal p53 isoforms can differentially regulate target genes and modulate their expression.2−4 Furthermore, abnormal expression of these isoforms may cause © XXXX American Chemical Society

Received: July 23, 2017 Accepted: February 6, 2018

A

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Figure 1. Schematic representation of C-terminal p53 isoforms α, β, and γ produced by the alternative splicing of intron-9 of TP53.

Figure 2. Schematic representation of MIPs coupled with LC-MS/MS-based targeted proteomics for the quantification of C-terminal p53 isoforms α, β, and γ. First, the common sequence of the isoform-specific surrogate peptides was selected as single template, and then different functional monomers and cross-linkers were examined to acquire the MIPs with high performance. After the template peptide was removed, cavities complementary to the surrogate peptides were left on the surface of the MIPs and were ready for the sample analysis. On the other hand, the p53 isoforms in cell samples were extracted and digested, and the isoform-specific surrogate peptides were released. The prepared MIPs can recognize and enrich these surrogate peptides, followed by the LC-MS/MS-based targeted proteomics analysis.

assays and laboratories.10 More importantly, p53 antibodies capable of specifically identifying each C-terminal isoform are not commercially available. Thus, liquid chromatographytandem mass spectrometry (LC-MS/MS)-based targeted proteomics was developed in this study as an alternative method. Currently, there is an increased interest in targeted proteomics, which detects proteins of interest in complicated biological samples with high sensitivity, quantitative accuracy,

and reproducibility.11,12 In principle, target proteins are digested into peptides with a certain enzyme. The “best” proteolytic peptides with the sequence specific to the target protein and abundant response on a triple quadrupole instrument are selected as surrogate analytes. Selected/multiple reaction monitoring (SRM/MRM) is generally employed to detect the surrogate peptides. To date, targeted proteomics has been increasingly applied to protein quantification in various circumstances.13 Our previous work has also successfully B

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

■

determined the interaction between p53 and MDM2 in breast cancer using targeted proteomics.14 In the case of p53 isoform detection, the low abundance of p53 isoforms, as described earlier, could be a great challenge. Antibody-based pretreatment methods such as immunoprecipitation and stable isotope standards with capture by antipeptide antibodies (SISCAPA)15 are normally effective in the enrichment of proteins/peptides, whereas the instability and high cost of antibodies limit their wide application.16 Taking these aspects into consideration, a molecularly imprinted polymers (MIPs) approach, termed as “epitope approach”, can potentially overcome this limitation with selective peptide recognition properties.17 MIPs normally create synthetic receptors capable of selectively recognizing specific target molecules18 and display significant advantages, including high chemical stability, ease of preparation, potential reusability, and low manufacturing cost.19 To date, MIPs for low-weight molecules are well-established, whereas the success of protein imprinting is limited.20 Despite few recent remarkable achievements in the field, no general rules have been set to successfully imprint proteins.21 The major obstacles of protein imprinting include acquirement of pure protein templates, three-dimensional structure of proteins being sensitive to harsh conditions, restricted transfer of proteins, and insolubility of proteins in typical polymerization mixtures used for imprinting.19,22 One proposed method to reduce these complications is “epitope approach”,23 which identifies a proper epitope (used as the substitute of target protein) on the surface of proteins while the proteins as target analytes are kept intact. However, this process of molecular recognition can be influenced by other parts of proteins.22 Therefore, we previously proposed a modified molecular imprinting strategy that recognized the proteolytic peptides of proteins instead of the whole proteins and enriched the proteins at surrogate peptide level.17 Our result demonstrated that this MIPs epitope approach can be well coupled to LCMS/MS-based targeted proteomics for protein quantification using the same surrogate peptide in both enrichment and quantification. On the other hand, there is evidence indicating that cross-reactivity of MIPs is useful for application to nonimprinted molecules with similar features as the template, which extends their utility beyond molecular recognition of the template.24 Given the sequence homology of C-terminal p53 isoforms, there is a great potential for isolation and enrichment of these isoforms using single template MIPs for their surrogate peptides. In this study, we combined MIPs with LC-MS/MS-based targeted proteomics to simultaneously detect C-terminal p53 isoforms α, β, and γ (Figure 2). Isoform-specific surrogate peptides with high homology were first selected and used in both MIPs enrichment and mass spectrometric detection. Ideally, the common sequence of these three surrogate peptides was used as the single template in MIPs. After optimization of imprinting conditions, characteristics of the prepared MIPs including FT-IR absorption, adsorption capacity and kinetics, and stability were investigated. In addition, binding affinity and cross-reactivity of the MIPs for each surrogate peptide were also evaluated. Then, the developed assay was validated and applied to simultaneous quantitative analysis of p53 isoforms α, β, and γ in several human breast cell lines (i.e., MCF-10A normal cells, MCF-7 and MDA-MB-231 cancer cells, and drugresistant MCF-7/ADR cancer cells).

Article

MATERIALS AND METHODS

Chemicals and Reagents. Please see the Supporting Information. Preparation of Stock Solutions, Calibration Standards, and Quality Controls (QCs). Stock solutions (1 mg/ mL) were prepared by accurately weighing the peptides and dissolving them in deionized water. The solutions were stored at −20 °C in brown glass tubes to protect them from light. The corresponding isotope-labeled synthetic peptides were used as internal standards (IS). The internal standards were also weighed, and 1 mg/mL stock solutions were prepared in deionized water. Then, a 100 nM internal standard solution containing all the internal standards was prepared by diluting the stock solutions with an ACN/water mixture (50:50, v/v) containing 0.1% FA. The calibration standards of surrogate peptides were prepared by sequential dilution of the stock solutions with p53-depleted cell extract (please see the Supporting Information). The concentrations of the standards were 5, 10, 25, 50, 100, 250, and 500 nM for each peptide in a mixture. Correspondingly, the QC samples for lower limit of quantification (LLOQ), low QC, mid QC, and high QC were set at 5, 15, 50, and 400 nM in the same matrix and frozen prior to use. Notably, all the following solutions were prepared in p53-depleted cell extract if the condition was not specified. Cell Culture and Protein Extraction. Please see the Supporting Information. In-Solution Tryptic Digestion. A total of 50 μL of 50 mM NH4HCO3 was added to 100 μL of each sample (calibration standards, QC samples, and cell protein extracts). The mixture was boiled at 95 °C for 8 min.25,26 The proteins were then reduced by adding 50 mM DTT to reach a 10 mM final concentration and incubated at 60 °C for 20 min, followed by a final concentration of 50 mM IAA for 45 min at room temperature in the dark. Afterward, the samples were incubated with sequencing grade trypsin at 37 °C for 24 h. The reaction was stopped by adding 10 μL of 0.1% TFA. Then, 100 μL of the internal standard solution was added to the mixture, followed by transferring to a preconditioned (with 3 mL of ACN and 3 mL of deionized water) Oasis HLB cartridge (60 mg/3 mL; Waters, Milford, MA, U.S.A.). After loading the sample, the cartridge was washed with 2 mL of water and 2 mL of ACN/water (50:50, v/v) and then eluted with 1 mL of ACN. Finally, the eluent was dried down in a vacuum centrifuge and the dried extracts were resuspended in water before further process using MIPs. MIPs Synthesis. The template peptide (10 mg) was dissolved in 300 μL of methanol, prior to the addition of monomer, cross-linker, and initiator. The mixture was then sonicated for 3 min and purged with nitrogen for 1 min, followed by polymerization under nitrogen for 12 h in a 60 °C water bath. After 2 h drying in a vacuum oven at 60 °C, the polymer was ground and sieved (100−180 μm). The obtained particles were collected and washed with 10% FA/methanol, methanol and water sequentially, until the remaining template peptide was completely removed and no longer detectable in LC-MS/MS. As a parallel control, nonimprinted polymers (NIPs) were prepared leaving out the template under the identical conditions of MIPs. MIPs Characterization. FT-IR. For MIPs characterization, FT-IR spectra were recorded using a FT-IR Tensor 27 spectrometer (Bruker, Germany) coupled with a liquid C

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Figure 3. (A) Product ion spectra of KPLDGEYFTLQIR (peptide-α) for isoform α, KPLDGEYFTLQDQTSFQK (peptide-β) for isoform β, and KPLDGEYFTLQMLLDLR (peptide-γ) for isoform γ, and (B) the LC-MS/MS chromatograms of peptide-α, β, and γ using synthetic peptides and cell lysate sample.

concentrations (500 nM). Furthermore, nine samples (S1−S9) containing surrogate peptides with known concentrations between 50 nM and 500 nM were also analyzed. Instruments and Conditions. Please see the Supporting Information.

nitrogen-cooled mercury−cadmium−telluride detector (MCT) that had a resolution of 2 cm−1 and a spectral range of 4000− 400 cm−1. Samples were dried at 80 °C in a vacuum oven for at least 12 h prior to fabrication of KBr pellet. In this context, 2 mg of each sample was thoroughly mixed with 100 mg of KBr, and the mixture was used for the pellet fabrication. The obtained data were interpreted according to a list of absorption peaks and frequencies, typically reported in wavenumbers, for common types of molecular bonds and functional groups.27,28 Adsorption Tests. To evaluate the recognition capability of the MIPs, a static adsorption test was first conducted. A series of solutions were prepared for template peptide and surrogate peptides. Then, 4 mg MIPs/NIPs was suspended in an aliquot of 1 mL of each solution and the mixtures were shaked gently at room temperature for 30 min. Afterward, the polymer particles were centrifuged at 10000 rpm for 10 min. After discarding the supernatant, the particles were washed with 1 mL PBS containing 0.02% Tween-20, followed by an addition of 200 μL of 10% FA/methanol twice to elute the bound peptides. The obtained solution was further centrifuged at 10000 rpm for 10 min and the supernatant was collected and analyzed using LC-MS/MS. In addition, dynamic adsorption test was also performed. Following the same procedure described above, the samples were collected within the time range of 0−60 min and then analyzed. Selectivity Tests. The selectivity test was first carried out using a competing peptide PVDGDYFT. In addition, another set of calibration standards was prepared by spiking isotopelabeled peptides in p53-depleted cell extract. For further validation, each peptide at various assigned concentrations (5 to 500 nM) was mixed with the other two peptides at high

■

RESULTS AND DISCUSSIONS Selection of C-terminal p53 Isoform-Specific Surrogate Peptides. The most critical part for simultaneous detection of C-terminal p53 isoforms in this study is to choose the isoform-specific surrogate peptides that meet the criteria of peptide selection for both targeted proteomics and MIPs. Currently, the selection of appropriate surrogate peptides and the corresponding MRM transitions in LC-MS/MS-based targeted analysis are straightforward. There are some empirical rules for the choice of peptides, which may be helpful at the primary stage of searching.12 Furthermore, it is possible to predict which peptides and product ions are most appropriate for MRM by in silico prediction by various algorithms and computational tools.29 Finally, the reliability of selection can be confirmed by experimental data. In the case of p53, inferring correct surrogate peptides with the target protein isoforms could be confounded by the presence of their shared digested peptides.30 In detail, p53 isoforms α, β, and γ share ∼80% identity and only differ from each other at the C-terminus (Figure 1S). Distinguishing these isoforms is only feasible if any isoform-specific C-terminal digested peptides could be obtained. On the other hand, single template MIPs isolation and enrichment owe their success to the cross-reactivity resulting from the similarity of analytes. In such a delicate D

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Table 1. Binding Results for Different Cross-Linkers and Functional Monomers in 1 mL of 1 μg/mL (0.763 μM) Template Solutiona adsorption capacity (μmol/g) functional monomer a

cross-linker MAA TBAm

EGDMA

TRIM

DVB

0.125 ± 0.002 0.103 ± 0.018

0.137 ± 0.008 0.113 ± 0.012

0.083 ± 0.011 0.110 ± 0.022

The molar ratio for template/functional monomer/cross-linker is 1:10:40.

the equimolar synthetic peptide standard in the digestion.32 The result indicated that the efficiency values were 93.6 ± 0.7% for peptide-α, 92.7 ± 1.2% for peptide-β, and 90.6 ± 0.8% for peptide-γ. Because the occurrence of successive lysine could lead to various tryptic products,33 the LC-MS/MS chromatograms of potential product peptides were monitored to further illustrate the digestion process. Using KKKPLDGEYFTLQIRGR (substrate peptide of peptide-α) as an example, its tryptic peptides theoretically included KKKPLDGEYFTLQIR, KKPLDGEYFTLQIR, KPLDGEYFTLQIR, and PLDGEYFTLQIR. As shown in Figure 2S, KPLDGEYFTLQIR was the main product. The results of peptide-β and peptide-γ were also consistent with this observation. Single Template and Isoform-Specific Surrogate Peptides Enrichment in MIPs. As described previously, the rationale of epitope approach is to use a short peptide as template that represents only part of a longer peptide or protein, which in turn can be recognized by MIPs.34 There is evidence indicating that the polymers imprinted with a short peptide can efficiently recognize both the template and longer peptides that possess the same part of the structure even though other parts of the peptides may influence the process of molecular recognition.34 Thus, the sequence KPLDGEYFTLQ shared by peptide-α, β, and γ at the N-terminus could be in principle employed as the template peptide here. The apparent cross-reactivity of the MIPs against the surrogate peptides will be a key factor for group-selective recognition of these peptides. MIPs Preparation and Template Recognition. To choose appropriate polymerization solvent, cross-linker, and functional monomer, adsorption capacity was estimated using a FITC-labeled template peptide (i.e., KPLDGEYFTLQKFITC)35 and HPLC. The reactive FITC derivative was employed to better track the released template molecules.36 Adsorption capacity, Qe (μmol/g), was calculated according to eq 1:37

situation, the selected surrogate peptides should share certain sequence similarity (but not be the same) theoretically. In this report, we first searched the ExPASy database to predict the proteolytic peptides of p53 isoforms α, β, and γ. After ruling out the peptides that were not unique to these isoforms by a BLAST search, we fortunately found KPLDGEYFTLQIR (peptide-α) for isoform α, KPLDGEYFTLQDQTSFQK (peptide-β) for isoform β, and KPLDGEYFTLQMLLDLR (peptide-γ) for isoform γ using trypsin as digestion enzyme. These peptides are indeed at C-terminal domains of the isoforms and have the same first 11 amino acids (i.e., KPLDGEYFTLQ) at their N-terminus. Isoform-Specific Surrogate Peptides in LC-MS/MS-Based Targeted Proteomics. The occurrence of peptide-α, β, and γ was confirmed by a LC-MS/MS analysis of cell lysate. These tryptic peptides coeluted well with their synthetic counterparts and exhibited identical fragmentation patterns. The product ion spectra and LC-MS/MS chromatograms of three surrogate peptides are illustrated in Figure 3. The LC-MS/MS chromatograms of these peptides in cell lysate sample are also shown. In addition, synthetic stable isotope-labeled peptides, KPL*DGEYFTL*QIR, KPL*DGEYFTL*QDQTSFQK, and KPL*DGEYFTL*QMLLDLR with 13C6-labeled leucin, which were 12 Da heavier than the unlabeled ones, were prepared and used as internal standards (IS), respectively. The chromatographic and mass spectrometric behaviors of IS were similar to those of the unlabeled ones (data not shown). To achieve good sensitivity, the MRM transitions with best signal-to-noise (S/N) and limit of quantification (LOQ) were used. In detail, the transitions were m/z 527.3 → m/z 175.0, m/z 527.3 → m/z 416.2, m/z 527.3 → m/z 529.1, m/z 527.3 → m/z 630.4, and m/z 527.3 → m/z 640.0 (IS: m/z 531.3 → m/z 175.0, m/z 531.3 → m/z 416.2, m/z 531.3 → m/z 535.1, m/z 531.3 → m/z 636.4, and m/z 531.3 →m/z 646.0) for peptide-α; m/z 715.9 → m/z 147.0, m/z 715.9 → m/z 610.2, m/z 715.9 → m/z 853.3, m/z 715.9 → m/z 981.3, and m/z 715.9 → m/z 1051.4 (IS: m/z 719.9 → m/z 147.0, m/z 719.9 → m/z 610.2, m/z 719.9 → m/z 853.3, m/z 719.9 → m/z 981.3, and m/z 719.9 → m/z 1057.4) for peptide-β; m/z 684.8 → m/z 288.2, m/z 684.8 → m/z 403.2, m/z 684.8 → m/z 629.5, m/z 684.8 → m/z 760.3, and m/z 684.8 → m/z 888.5 (IS: m/z 688.8 → m/z 288.2, m/z 688.8 → m/z 403.2, m/z 688.8 → m/z 629.5, m/z 688.8 → m/z 760.3, and m/z 688.8 → m/z 888.5) for peptide-γ. The peak areas from five transitions for each peptide were summed and used in the following quantitative analysis.31 The digestion completeness and repeatability in targeted proteomics were evaluated following the same process in our previous work.17,32 Three synthetic substrate peptides KKKPLDGEYFTLQIRGR, KKKPLDGEYFTLQDQTSFQKEN, and KKKPLDGEYFTLQMLLDLRWC, each containing the corresponding surrogate peptide sequence (i.e., peptide-α, β, and γ), were subject to digestion and digestion efficiency was referred to as response ratio of the digested peptide product to that of

Qe =

(C0 − Ce)V W

(1)

where Ce (μM) is the template concentration in the supernatant, C0 (μM) is the initial template concentration, V (mL) is the volume of the template solution, and W (mg) is the weight of the MIPs incubated. First, several monomers and cross-linkers with different properties were evaluated (Table 1). As a result, TRIM had the highest adsorption capacity among the cross-linkers investigated (i.e., EGDMA, TRIM, and DVB). Compared with TBAm, MAA was the monomer with a better adsorption performance. Following the selection of cross-linker and monomer, polymerization solvent was also evaluated. Among the solvents (i.e., N,N-dimethylformamide, acetonitrile, methanol, ethanol, and water) examined, the binding amount of the template on the MIPs was in the order of methanol > ethanol > N,N-dimethylformamide > water ∼ acetonitrile (data not E

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Figure 4. Optimization results of imprinting conditions including (A) different ratio of the selected cross-linker (TRIM) and functional monomer (MAA) and (B) different ratio of the template and the selected monomer (MAA). (C) Static adsorption isotherms and (D) kinetic binding curves of the template peptide to the MIPs and the NIPs are also shown.

and the result indicated that the pseudo-second-order model could better describe the adsorption kinetics of the template peptide at a concentration of 15.3 μM (20 μg/mL) with a higher correlation coefficient of 0.999 (Table 2S). Similarly, adsorption of the template peptide at other concentrations also followed pseudo-second-order kinetics (Figure 3S). This outcome suggested that the rate-limiting step may be chemical adsorption.39 MIPs Characterization. FT-IR Spectra. FT-IR spectroscopy is useful for monitoring changes in chemical structure resulting from the addition or removal of functional groups in MIPs.40 For a comparison, FT-IR spectra of the MIPs (before and after removal of the template) and the NIPs were examined (Figure 4S). In the figure, the strong adsorption peaks at 1725 and 1150 cm −1 were attributed to CO and C−O stretching, respectively. These bands suggested the existence of TRIM in all the studied polymers. The additional bands at 3461 and 1517 cm−1 before template removal corresponded to characteristic N−H stretching, which confirmed the formation of imprinted polymers. After removal of the template peptide, these peaks disappeared. Cross-Reactivity for Adsorption of Isoform-Specific Surrogate Peptides. Most importantly, we studied the recognition ability of the synthesized MIPs to peptide-α, β, and γ because cross-reactivity is the essential feature for this study as mentioned earlier. According to the binding amount of each surrogate peptide (Figure 5), the MIPs possessed a high degree of cross-reactivity for all these three peptides, while the NIPs showed low binding values.41 The values of Qmax are 1.04 ± 0.05 μmol/g for peptide-α, 0.64 ± 0.05 μmol/g for peptide-β, and 0.71 ± 0.03 μmol/g for peptide-γ, which were approximately 20% of that of the template. Interestingly, the maximum adsorption capacity decreases with the increase in the number of amino acids in surrogate peptide sequence (i.e.,

shown). Finally, the ratio of template, monomer, and crosslinker was optimized by varying their concentrations. The result indicated that the optimal monomer to cross-linker molar ratio was 1:5 (Figure 4A). Under this premise, the ratio of template and monomer was further optimized and the greatest adsorption was afforded when their ratio was 1:15 (Figure 4B). Using the above optimized conditions, the static and dynamic binding abilities of the MIPs were examined using LC-MS/MS. Figure 4C shows that the adsorption amount of the MIPs increased quickly with the rising concentration of the template and reached saturation when the concentration was over 22.9 μM (∼30 μg/mL). Comparatively, the NIPs had a significantly lower saturated adsorption amount than the MIPs. This recognition selectivity was evaluated by imprinting factor (IF), which was calculated by eq 2 IF = Q MIPs/Q NIPs

(2)

where QMIPs and QNIPs are the adsorption capacity of analytes on MIPs and NIPs, respectively.37 The resulted IF value in the experiment of 22.9 μM template was 3.27 ± 0.67. After fitting the data to four classical isotherm models including Langmuir, Freundlich, Scatchard, and Langmuir− Freundlich, the best fit was obtained using the Langmuir model (Figure 3S). Correlation coefficient (r2) was 0.998 (Table 1S). The good-match with the Langmuir adsorption demonstrated a saturated monolayer of peptide molecules on the adsorption surface with no lateral interaction between the adsorbed molecules.38 In addition, the result of dynamic binding experiments showed that the adsorption amount on the MIPs increased and reached equilibrium within 10 min (Figure 4D). This rapid adsorption allowed fast mass transfer. Then, pseudo-first-order and pseudo-second-order kinetic models were applied to reveal the mechanism underlying the adsorption process (Figure 3S) F

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Table 2. Imprinting Factors and Cross-Reactivity Values of the MIPs for Adsorption of the Isoform-Specific Surrogate Peptides adsorption amount (μmol/g) surrogate peptides peptide-α peptide-β peptide-γ template

peptide-α (13aa) > peptide-γ (17aa) > peptide-β (18aa)). This observed variation in adsorption may be explained by the fact that the surrogate peptides are longer than the template and their extra residues other than the template can influence the process of molecular recognition.42 To further compare the recognition properties of the MIPs to peptide-α, β, and γ, we also calculated the cross-reactivity (CR) values for each surrogate peptide with the following equation:43

IFsurrogate peptide IFtemplate

± ± ± ±

0.05 0.05 0.03 0.16

NIPs 0.31 0.21 0.21 1.11

± ± ± ±

0.06 0.04 0.05 0.22

IF 3.32 3.18 3.34 3.27

± ± ± ±

CR 0.48 0.56 0.73 0.67

1.02 ± 0.07 0.97 ± 0.04 0.99 ± 0.02

peptide in this study. The obtained IF and CR values are 1.26 ± 0.16 and 0.39 ± 0.05, suggesting weak cross-reactivity of the MIPs to other coexisting peptides in cell lysate. Then, both the MIPs and the NIPs were subjected to a mixture of four peptides composed of peptide-α, β, γ, and PVDGDYFT at three concentration ratios (i.e., each surrogate peptide/PVDGDYFT = 1:1, 1:3, or 1:10). The result also indicated that the MIPs were of excellent selectivity for the surrogate peptides (Figure 5S). Notably, the issue of nonspecific adsorption though not high was lessened here because the exquisite specificity of the mass spectrometers in the subsequent targeted analysis can overcome this issue. MRM measurements served as the “secondary separation” for detection and quantification, which has been comprehensively explored in our previous work.17 Reusability. The stability and potential regeneration/reuse of the MIPs were also investigated. As shown in Figure 6S, the MIPs can still retain >90% of its original capacity after 10 adsorption cycles. In addition, there was no significant decrease in adsorption capacity after storing at room temperature for 30 days, suggesting acceptable storage stability. Validation of MIPs Coupled with LC-MS/MS-Based Targeted Proteomics. In this study, we prepared calibration standards and QC samples using p53-depleted cell extract as a surrogate matrix. The level of depletion was significant as determined using both Western blotting and LC-MS/MS-based targeted proteomics (Figure 7S). The calibration curves were constructed by a weighted linear regression model with a weighting factor of 1/x2. The relative peak area ratio of the surrogate peptide and the corresponding stable isotope-labeled internal standard was plotted against the surrogate peptide concentration. Calibration curves are shown in Figure 8S. The LOQ was 5 nM. The chromatograms of the LLOQs with and without MIPs, and the blanks are shown in Figure 9S. The assay was >15-fold more sensitive than that without MIPs enrichment (Figure 10S). To further validate the assay selectivity, “reverse” calibration standards were prepared by spiking isotope-labeled peptides in p53-depleted cell extract (Figure 11S).46 The obtained slopes of calibration curves were not significantly different from those using unlabeled ones (Table 3S). Furthermore, we also prepared another series of calibration curves. Each calibration curve contained all three surrogate peptides, with one peptide with increasing concentration 5−500 nM and the other two with a constant concentration of 500 nM each (Figure 12S). The result indicated that the presence of other homologous peptides at high concentrations did not significantly alter the calibration curves (Table 4S). Finally, we prepared nine samples with all the surrogate peptide concentrations between 50 and 500 nM (Figure 13S). The result also demonstrated high selectivity of the whole assay.

Figure 5. Adsorption capacity of peptide-α, β, and γ to the MIPs and the NIPs.

CR =

MIPs 1.04 0.64 0.71 3.63

(3)

Being IFsurrogate peptide and IFtemplate the imprinting factors for the surrogate peptides and the template. The obtained CR values are listed in Table 2. In general, the template molecule gives a value of 1, and less retained analogues give smaller values.44 The high CR values of the surrogate peptides indicated that molecular imprinting with the template peptide provided the MIPs with selectivity to these surrogate peptides. Notably, this approach saves on the number of templates required and also overcomes the problem of template bleeding at some extent. Indeed, the level of template bleeding was negligible (∼0.004% of the imprinted template).45 Moreover, peptide PVDGDYFT with maximum sequence identity percent to the template peptide by BLAST was chosen as a competing G

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Table 3. Expression Levels of C-Terminal p53 Isoforms in MCF-10A, MCF-7, MDA-MB-231, and MCF-7/ADR Cellsa

a

protein amount per cell (amol/cell)

MCF-10A

MCF-7

MDA-MB-231

MCF-7/ADR

isoform-α isoform-β isoform-γ

0.596 ± 0.076 0.105 ± 0.022 0.022 ± 0.009

1.12 ± 0.20 (1.88) 1.06 ± 0.09 (10.1) 0.45 ± 0.06 (20.5)

1.95 ± 0.12 (3.28) 1.91 ± 0.09 (18.2) 1.44 ± 0.08 (65.3)

3.37 ± 0.18 (5.65) 5.09 ± 0.22 (48.5) 2.24 ± 0.12 (102)

The numbers in parentheses are fold changes of the isoform levels in the cancer cells relative to MCF-10A cells.

significantly change clinical practice has become one of the biggest challenges in translational research,59 where quantitative measurement is a critical step. Our lab is among the first to combine MIPs and LC-MS/MSbased targeted proteomics for protein quantification.17 In principle, cross-reactivity resulting from structural analogues is usually avoided in MIPs separation. Rather than being a curse, this phenomenon is used as a blessing here to group-select the isoform-specific surrogate peptides of C-terminal p53 isoforms, which share >80% sequence identity. Using their common sequence as single template, all of the isoform-specific surrogate peptides can be recognized and enriched together but distinguished and quantified later by mass spectrometric detection. In this way, we can work on a number of peptides in one synthesis approach and reduce template requirement for multiple peptides, which are especially useful for protein isoforms. Our developed MIPs coupled with LC-MS/MS-based targeted proteomics afford a potential strategy for the study of protein isoforms and further facilitate translation.

To assess the accuracy and precision of this coupled assay, QC samples of each surrogate peptide were tested at four different concentrations in three validation runs. Notably, all of the surrogate peptides act as binding ligands to the single template MIPs and the maximum adsorption corresponds to their total amount,47 which reduces the saturation concentration of each peptide. Thus, a narrower range of peptide concentration (5−500 nM) was selected even though that good linearity was observed up to 1.5 μM for individual surrogate peptides. The intra- and interday precisions were expressed as the percent coefficient of variation (%CV). The accuracy was obtained by comparing the averaged calculated concentrations to their nominal values (%bias). The results are listed in Table 5S. Both accuracy and precision were ≤±15% (LLOQ, ≤±20%).48 Quantification of C-Terminal p53 Isoforms in Breast Cells. Using the combined assay developed above, the amount of C-terminal p53 isoforms was determined in several human breast cells lines (i.e., MCF-10A, MCF-7, MDA-MB-231, and MCF-7/ADR cells). The LC-MS/MS chromatogram of cell lysate sample and the calculated results are shown in Figure 14S and Table 3. As shown in the table, all three C-terminal p53 isoforms had a similar expression pattern and their cellular level was in the order of MCF-7/ADR cells > MDA-MB-231 cells > MCF-7 cells > MCF-10A. This observation is consistent with the previous findings. Specifically, p53 isoforms are abnormally expressed in different types of human cancers including breast cancer, which could contribute to cancer formation and cancer progression.49 In addition, higher level of p53 has been previously observed in invasive MDA-MB-231 cells compared to noninvasive MCF-7 ones.9 Moreover, enhanced expression of p53 isoforms in MCF-7/ADR cells could be related to ADR induced DNA damage,50 which can activate p53.51 On the other hand, isoforms β and γ had a much greater fold increase (i.e., >10-fold) in breast cancer cells than isoform α. Correspondingly, several previous studies have demonstrated that C-terminal p53 isoforms β and γ, but not α, can induce the expression of IGFBP3, while breast cancer cell growth correlates positively with IGFBP3 expression.52,53

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.7b02890. Additional supporting tables and figures, and additional experimental details (PDF).

■

AUTHOR INFORMATION

Corresponding Author

*Phone: 86-25-86868326. E-mail: [email protected]. Fax: 86-25-86868467. ORCID

Yun Chen: 0000-0002-0952-8851 Notes

The authors declare no competing financial interest.

■

■

ACKNOWLEDGMENTS The National Natural Science Fund (21722504, 21675089, 21175071), t he SEU-NJMU cooperation project (2242017K3DN12), and the Open Foundation of State Key Laboratory of Reproductive Medicine (SKLRM-GA201804) awarded to Y.C. are gratefully acknowledged.

CONCLUSIONS The structural space of the human proteome is large and diverse mainly due to the presence of various protein isoforms, including post-translational modifications, splice variants, proteolytic products, genetic variations, and somatic recombination.54 For example, the estimated percentage of human gene products that went through alternative splicing was identified as 35%55 and 60%,56 to as high as 95%.57 Protein isoforms can play important roles in biological processes and some of them have been used as biomarkers or therapeutic targets/mediators in various types of cancer, including breast cancer.58 Yet, the exact role of most isoforms is still poorly understood. This is, in part, because of the lack of techniques to differentiate these isoforms and accurately identify/quantify them in clinical practice. Indeed, the quest for suitable molecules that will

■

REFERENCES

(1) Marcel, V.; Dichtel-Danjoy, M. L.; Sagne, C.; Hafsi, H.; Ma, D.; Ortiz-Cuaran, S.; Olivier, M.; Hall, J.; M?llereau, B.; Hainaut, P.; Bourdon, J. C. Cell Death Differ. 2011, 18, 1815−1824. (2) Harms, K. L.; Chen, X. Mol. Cell. Biol. 2005, 25, 2014−2030. (3) Surget, S.; Khoury, M. P.; Bourdon, J. C. OncoTargets Ther. 2013, 7, 57−68. (4) Bourdon, J.-C.; Fernandes, K.; Murray-Zmijewski, F.; Liu, G.; Diot, A.; Xirodimas, D. P.; Saville, M. K.; Lane, D. P. Genes Dev. 2005, 19, 2122−2137.

H

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry (5) Yu, H.; Rohan, T. J. Natl. Cancer Inst. 2000, 92, 1472−1489. (6) Marcel, V.; Khoury, M. P.; Fernandes, K.; Diot, A.; Lane, D. P.; Bourdon, J.-C. In p53 Protocols; Deb, S., Deb, S. P., Eds.; Humana Press: Totowa, NJ, 2012; pp 15−29. (7) Khoury, M. P.; Marcel, V.; Fernandes, K.; Diot, A.; Lane, D. P.; Bourdon, J.-C. In p53 Protocols; Deb, S., Deb, S. P., Eds.; Humana Press: Totowa, NJ, 2013; pp 1−14. (8) Tendler, Y.; Weisinger, G.; Coleman, R.; Diamond, E.; Lischinsky, S.; Kerner, H.; Rotter, V.; Zinder, O. Mol. Brain Res. 1999, 72, 40−46. (9) Milicevic, Z.; Bajic, V.; Zivkovic, L.; Kasapovic, J.; Andjelkovic, U.; Spremo-Potparevic, B. Sci. World J. 2014, 2014, 618698. (10) Solier, C.; Langen, H. Proteomics 2014, 14, 774−783. (11) Marx, V. Nat. Methods 2013, 10, 19−22. (12) Chen, Y.; Wang, F.; Xu, F.; Yang, T. In Modern Proteomics: Sample Preparation, Analysis and Practical Applications; Mirzaei, H., Carrasco, M., Eds.; Springer International Publishing: Cham, 2016; pp 255−279. (13) Thomas, S. N.; Zhang, H. Expert Rev. Proteomics 2016, 13, 1−3. (14) Zhang, W.; Zhong, T.; Chen, Y. J. Proteomics 2017, 152, 172− 180. (15) Anderson, N. L.; Anderson, N. G.; Haines, L. R.; Hardie, D. B.; Olafson, R. W.; Pearson, T. W. J. Proteome Res. 2004, 3, 235−244. (16) Cakir, P.; Cutivet, A.; Resmini, M.; Bui, B. T.; Haupt, K. Adv. Mater. 2013, 25, 1048−1051. (17) Liu, L.; Zhong, T.; Xu, Q.; Chen, Y. Anal. Chem. 2015, 87, 10910−10919. (18) Janiak, D. S.; Kofinas, P. Anal. Bioanal. Chem. 2007, 389, 399− 404. (19) Lv, Y.; Tan, T.; Svec, F. Biotechnol. Adv. 2013, 31, 1172−1186. (20) Whitcombe, M. J.; Chianella, I.; Larcombe, L.; Piletsky, S. A.; Noble, J.; Porter, R.; Horgan, A. Chem. Soc. Rev. 2011, 40, 1547−1571. (21) Ambrosini, S.; Beyazit, S.; Haupt, K.; Tse Sum Bui, B. Chem. Commun. (Cambridge, U. K.) 2013, 49, 6746−6748. (22) Verheyen, E.; Schillemans, J. P.; van Wijk, M.; Demeniex, M. A.; Hennink, W. E.; van Nostrum, C. F. Biomaterials 2011, 32, 3008− 3020. (23) Bossi, A.; Bonini, F.; Turner, A. P.; Piletsky, S. A. Biosens. Bioelectron. 2007, 22, 1131−1137. (24) Lejeune, J.; Spivak, D. A. Biosens. Bioelectron. 2009, 25, 604− 608. (25) Optimization of Trypsin Digestion for MRM Quantification of Therapeutic Proteins in Serum, (2012) Available at: http://www. waters.com/webassets/cms/library/docs/720004507en.pdf (Date of access: Sep 28, 2017). (26) Immunoprecipitation protocol, Available at: http://www.abcam. com/protocols/immunoprecipitation-protocol-1 (Date of access: Sep 28, 2017). (27) Larkin, P. Infrared and Raman Spectroscopy; Principles and Spectral Interpretation; Elsevier: Oxford, U.K., 2011. (28) Sokrates, G. Infrared and Raman Characteristic Group Frequencies: Tables and Charts; Wiley: Chichester, U.K., 2004. (29) Fusaro, V. A.; Mani, D. R.; Mesirov, J. P.; Carr, S. A. Nat. Biotechnol. 2009, 27, 190−198. (30) Tang, H. Y.; Beer, L. A.; Tanyi, J. L.; Zhang, R.; Liu, Q.; Speicher, D. W. J. Proteomics 2013, 89, 165−178. (31) Kuhn, E.; Wu, J.; Karl, J.; Liao, H.; Zolg, W.; Guild, B. Proteomics 2004, 4, 1175−1186. (32) Yang, T.; Xu, F.; Fang, D.; Chen, Y. Sci. Rep. 2015, 5, 16733. (33) Keil, B. Specificity of Proteolysis; Springer-Verlag: New York, 1992. (34) Rachkov, A.; Minoura, N. Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol. 2001, 1544, 255−266. (35) Martins, G. V.; Marques, A. C.; Fortunato, E.; Sales, M. G. Biosens. Bioelectron. 2016, 86, 225−234. (36) El Kirat, K.; Bartkowski, M.; Haupt, K. Biosens. Bioelectron. 2009, 24, 2618−2624. (37) Zhao, K.; Feng, L.; Lin, H.; Fu, Y.; Lin, B.; Cui, W.; Li, S.; Wei, J. Catal. Today 2014, 236, 127−134.

(38) Zheng, H.; Liu, D.; Zheng, Y.; Liang, S.; Liu, Z. J. Hazard. Mater. 2009, 167, 141−147. (39) Ngah, W. S.; Ab Ghani, S.; Kamari, A. Bioresour. Technol. 2005, 96, 443−450. (40) Cormack, P. A.; Elorza, A. Z. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2004, 804, 173−182. (41) Cristallini, C.; Bellotti, E.; Spezia, F.; Rosellini, E.; Cascone, M. G.; Barbani, N. J. Appl. Biomater. Funct. Mater. 2016, 14, e129−136. (42) Suedee, R.; Seechamnanturakit, V.; Suksuwan, A.; Canyuk, B. Int. J. Mol. Sci. 2008, 9, 2333−2356. (43) Suedee, R.; Srichana, T.; Chuchome, T.; Kongmark, U. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2004, 811, 191−200. (44) Haginaka, J. TrAC, Trends Anal. Chem. 2005, 24, 407−415. (45) Batlokwa, B. S.; Mokgadi, J.; Nyokong, T.; Torto, N. Chromatographia 2011, 73, 589−593. (46) LeBlanc, A.; Michaud, S. A.; Percy, A. J.; Hardie, D. B.; Yang, J.; Sinclair, N. J.; Proudfoot, J. I.; Pistawka, A.; Smith, D. S.; Borchers, C. H. J. Proteome Res. 2017, 16, 2527−2536. (47) Kyzas, G. Z.; Nanaki, S. G.; Koltsakidou, A.; Papageorgiou, M.; Kechagia, M.; Bikiaris, D. N.; Lambropoulou, D. A. Anal. Chim. Acta 2015, 866, 27−40. (48) Biopharmaceutics Coordinating Committee in CDER, Guidance for industry-bioanalytical method validation, (2001) Available at: http://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ucm070107. pdf (Date of access: Jun 28, 2017). (49) Khoury, M. P.; Bourdon, J. C. Genes Cancer 2011, 2, 453−465. (50) Longley, D. B.; Johnston, P. G. J. Pathol. 2005, 205, 275−292. (51) Hirao, A.; Kong, Y. Y.; Matsuoka, S.; Wakeham, A.; Ruland, J.; Yoshida, H.; Liu, D.; Elledge, S. J.; Mak, T. W. Science 2000, 287, 1824−1827. (52) Gill, Z. P.; Perks, C. M.; Newcomb, P. V.; Holly, J. M. J. Biol. Chem. 1997, 272, 25602−25607. (53) O’Han, M. K.; Baxter, R. C.; Schedlich, L. J. Growth Factors 2009, 27, 394−408. (54) The human isoform proteome, Available at: http://www. proteinatlas.org/humanproteome/isoform (Date of access: Sep 28, 2017). (55) Mironov, A. A.; Fickett, J. W.; Gelfand, M. S. Genome Res. 1999, 9, 1288−1293. (56) Black, D. L. Annu. Rev. Biochem. 2003, 72, 291−336. (57) Pan, Q.; Shai, O.; Lee, L. J.; Frey, B. J.; Blencowe, B. J. Nat. Genet. 2008, 40, 1413−1415. (58) Hayes, N. V.; Gullick, W. J. J. Mammary Gland Biol. Neoplasia 2008, 13, 205−214. (59) Fu, Q.; Schoenhoff, F. S.; Savage, W. J.; Zhang, P.; Van Eyk, J. E. Proteomics: Clin. Appl. 2010, 4, 271−284.

I

DOI: 10.1021/acs.analchem.7b02890 Anal. Chem. XXXX, XXX, XXX−XXX