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A Colorimetric Sensor Array for Thiols Discrimination Based on Urease-Metal Ion Pairs Chunyang Lei, Huang Dai, Yingchun Fu, Yibin Ying, and Yanbin Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01493 • Publication Date (Web): 17 Aug 2016 Downloaded from http://pubs.acs.org on August 22, 2016
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A Colorimetric Sensor Array for Thiols Discrimination Based on Urease-Metal Ion Pairs Chunyang Lei †, Huang Dai †, Yingchun Fu *†, Yibin Ying †, Yanbin Li *†‡
†
College of Biosystems Engineering and Food Science, Zhejiang University,
Hangzhou 310058, China ‡
Department of Biological and Agricultural Engineering, University of Arkansas,
Fayetteville, Arkansas 72701, the United States
* E-mail:
[email protected]. Tel: (+) 86 571-88982534; E-mail:
[email protected]. Tel: (+) 1 4795752881.
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Abstract Thiols play a curial role in various physiological functions, and the discrimination of thiols is a significant but difficult issue. Herein, we presented a new strategy for strengthening the discrimination of thiols by a facile colorimetric sensor array composed of a series of urease-metal ion pairs. The proposed sensor array was fabricated based on the interactions between thiols and metal ions, and the effective activation of urease by thiols. Different thiols exhibited different affinities towards the metal ions, producing differential retentions of urease activity and generating distinct colorimetric response patterns. These response patterns are characteristic for each thiol and can be quantitatively differentiated by linear discriminant analysis (LDA). Cysteine (Cys), glutathione (GSH), and other four kinds of thiols have been well distinguished based on this sensor array at a low concentration (1.0 µM). Remarkably, the practicability of the proposed sensor array was further validated by high accuracy (96.67%) identification of 30 unknown thiol samples. In this strategy, urease and its metal ion inhibitors were adapted to fabricate the sensor array, offering a facile way to develop sensitive array sensing systems based on inexpensive and commercially available enzymes and their inhibitors.
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Introduction Thiols play curial roles in various physiological functions and pathological conductions, due to their widespread existence in many proteins and in small biomolecules such as cysteine (Cys) and glutathione (GSH).1 In particular, the reversible redox reactions between thiols and the corresponding disulfide forms have numerous biological functions in detoxification and metabolism.1,2 Generally, alternations in the level of cellular thiols have been considered as biomarkers of cancer, liver damage, psoriasis, and AIDS.3,4 Cys at a deficient level is associated with many syndromes including hair depigmentation, edema, slower growth and skin lesions.5 GSH is the most abundant nonprotein thiol in cells, participating in maintenance of intracellular redox activity, detoxification of toxic substances, and regulation of the nitric oxide cycle.6,7 As a consequence, assessments of the changes in thiol levels may provide critical insight into physiological functions or aid early diagnosis of disease states. Over the past decades, especially in recent years, considerable efforts have been devoted to the development of in vitro detection or in vivo imaging of GSH and Cys. Instrumental techniques including
high-performance
liquid chromatography,8
capillary electrophoresis,9 and mass spectrometry,10 could quantitatively detect and identify thiols, but with drawbacks of complicated manipulations and low cost performance. Therefore, fluorometric methods based on organic fluorophores, quantum dots and metal nanoclusters have attracted much interest, and many researchers have developed numerous fluorescent probes for highly sensitive 3 / 28
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detection of thiols.11-13 The fluorescent probes distinguish thiols from other molecules by specific reactions that can take place between the sulfhydryl group and the probes, including cleavage of certain chemical bonds (ca. disulfide bond, selenium-nitrogen bond, and sulfonamide and sulfonate ester), Michael addition, and metal-thiol complexes.14 Due to the similar reactivity of thiols, most of these probes could only determine the total thiols, and failed to distinguish GSH/Cys from each other, only a few studies have realized identification of thiols so far.14,15 Considering the practical requirements, it is of significant importance to develop a simple, and cost-efficient assay that can both sensitively detect and accurately discriminate thiols. Fortunately, recent progresses in pattern-recognition methods and sensor-array technologies represent an appealing alternative to make such a task possible. Array-based sensing techniques have emerged as a potentially powerful approach towards the detection and the recognition of diverse analytes.16,17 Basing on the cross-responsive sensing elements, rather than specific receptor-analyte binding interactions, sensor arrays generate composite responses unique to an analyte in a fashion similar to the mammalian olfactory system.18 Therefore, array-based sensing approaches are called vividly as “chemical nose/tongue” strategies, and the unique responses can be further differentiated using linear discriminant analysis (LDA), which provides versatile system that can be “trained” to identify various analytes.19 Currently, functional nanomaterials including gold nanoparticles, quantum dots and gold nanocluster, have been adapted as novel candidates to develop sensing arrays for identification of cells, bacteria, proteins and nucleobases.20-23 Recently, gold 4 / 28
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nanoparticles and metal indicators based sensing arrays were developed for biothoils discrimination with high selectivity, but the sensitivity still needs to improve.24,25 Benefiting from high efficiency of enzyme-catalyzed reactions and commercial availability of enzymes, sensing arrays based on enzymes have greater viability to be used in practical applications. For instance, an enzyme-based colorimetric sensor array was developed lately for detection and identification of organophosphorus and carbamate pesticides with high selectivity and sensitivity.26 Therefore, enzyme-based sensing array technique may provide a new choice for simple, sensitive and inexpensive identification of different thiols. Urease is highly efficient, speeding up the hydrolysis of urea into carbon dioxide and ammonia by about 1014 times. The hydrolytic reaction raises the pH value and ionic strength of the solution, which has been adapted as effective signal amplifier to develop colorimetric and electrochemical biosensors.27,28 Many metals ions, such as Ag+, Hg2+, Cu2+, and Zn2+ can efficiently inhibit the activity of urease.29,30 Meanwhile, sulfhydryl group of thiols have high affinity with these metal ions. On the basis of the interactions between thiols and metal ions, and the effective activation of urease by thiols, four urease-metal ion pairs are taken to fabricate a colorimetric sensor array for discrimination of thiols. Different thiols exhibited different affinities towards the metal ions, producing a differential retentions of urease activity, and generating various colorimetric responses. Thus, the variation in the colorimetric responses could be used as a fingerprint to accurately differentiate thiols by LDA. Furthermore, a fingerprint-based barcode was established and facilitated a simple, convenient and 5 / 28
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cost-effective approach to discriminate thiols. Experimental Section Materials Urease (from Jack bean), urea, phenol red glycine, tryptophan, proline and asparagine were obtained from Sangon Biotech (Shanghai, China). Fetal bovine serum (FBS) was
purchased
from
Life
Technologies
(Carlsbad,
USA).
5,5’-Dithiobis(2-nitrobenzoic acid) (DTNB) was purchased from Sigma-Aldrich (Shanghai, China). L-glutathione reduced (GSH), L-cysteine (Cys), dithiothreitol (DTT),
cysteamine
hydrochloride
(Cm),
3-mercaptopropionic
acid
(MPA),
2-mercaptoethanol (MCE), silver nitrate, mercury(II) nitrate monohydrate, copper(III) nitrate trihydrate, zinc nitrate hexahydrate and other reagents were analytical grade or better. All the commercial available regents were used without further purification. All solutions were prepared using ultrapure water (18.2 MΩ•cm) from Milli-Q automatic ultrapure water system. Inhibition of Urease by Metal Ions In a 96-well plate, 10 µL of urease (6 µg/ml) diluted in ultrapure water without buffer substance, was pipetted into each well, and 10 µL of metal ions (Ag+, Hg2+, Cu2+ or Zn2+) with various concentrations was added, then 50 µL of fresh ultrapure water was added. After incubation at room temperature (25 °C) for 10 min, 20 µL of phenol red (0.01%, m/v) and 10 µL of urea (1 M) were added to a final volume of 100 µL. Immediately, absorptions of each sample at 558 nm were recorded on a SynergyTM H1 Multi-Mode Reader (Biotek, USA) at an interval of 30 s for 15 min. 6 / 28
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Experimental Procedure for the Discrimination of Thiols In a 96-well plate, 10 µL of each thiol was pipetted into each well, and 10 µL of metal ions (Ag+, Hg2+, Cu2+ or Zn2+) at the optimized concentrations was added. After incubation at room temperature (25 °C) for 10 min, 10 µL of urease (6 µg/ml) and 50 µL of fresh ultrapure water were added, and incubated for another 10 min. Finally, 20 µL of phenol red (0.01%, m/v) and 10 µL of urea (1 M) were added to a final volume of 100 µL, and the finally concentration of sulfhydryl group is 1.0 µM for each thiol. Immediately, absorptions of each sample at 558 nm were recorded on a SynergyTM H1 Multi-Mode Reader at an interval of 30 s for 15 min. This process was repeated for the six kinds of thiols to produce five replicates of each. Thus, the six kinds of thiols were tested against the four kinds of urease-metal ion arrays five times to give a 6 thiols × 4 arrays × 5 replicates training data matrix. The raw data matrix was processed using classical LDA treatment in Matlab R2012a. Identification of unknown thiol samples In a 200 µL tube, 10 µL of 0.1 M Tris buffer (pH 8.0), 10 uL of DTNB (5 mM) and 30 µL of water were mixed, and then 50 µL of Cys with different concentrations (5, 10, 20, 40, 60 µM) was added to the tube, and incubated at room temperature (25 °C) for 5 min. Absorbance of the samples at 412 nm were recorded on an Agilent 8453 UV-visible spectrophotometer (Agilent, USA). After that, a calibration curve for sulfhydryl group using Cys as the standard was obtained, which was further used for determining the concentrations of sulfhydryl group in unknown samples. To test unknown thiol samples, stock solutions of six thiols in at different 7 / 28
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concentrations in water were prepared first. Then, 50 µL of each sample was added to the mixture solution of 10 µL Tris buffer (0.1 M, pH 8.0), 10 µL DTNB (5 mM) and 30 µL water. After incubation for 5 min. The absorbance was measured, and the concentration of sulfhydryl group in the sample was calculated based on the calibration curve. Each sample was diluted by water to get a solution, in which the concentration of sulfhydryl group is 10 µM. Then, aliquots of 10 µL of the diluted unknown thiol samples were added into the wells, and 10 µL of metal ions (Ag+, Hg2+, Cu2+ or Zn2+) at the optimized concentrations was added and incubated at room temperature for 10 min. After that, 10 µL of urease (6 µg/ml) and 50 µL of fresh ultrapure water were added, and incubated for another 10 min. Finally, 20 µL of phenol red (0.01%, m/v) and 10 µL of urea (1 M) were added to a final volume of 100 µL. Immediately, absorptions of each sample at 558 nm were recorded on a SynergyTM H1 Multi-Mode Reader at an interval of 30 s for 15 min. The resulting colorimetric responses were analyzed by LDA to identify the tested thiols by comparing the results with the training matrix obtained in the former section. Thiol Discrimination in Real Biological Samples Fetal bovine serum (FBS) was diluted by ultrapure water to get 10% FBS solution, and then the solution was adjusted to pH 6.0 with dilute nitric acid, which can eliminate the interferences on color responses of phenol red caused by the buffering capacity of FBS solution. Each thiol was diluted by 10% FBS to get a solution with 50 µM of sulfhydryl group. The samples were ultrafiltrated using a centrifugal filter unit (Amicon Ultra-0.5 mL with MWCO 10000, Millipore) to remove the proteins in 8 / 28
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FBS that can bind metal ions. Ten µL of the ultrafiltrated samples was used for tests, and the procedure was the same as that in the described section of Experimental Procedure for the Discrimination of Thiols. Results and Discussion Principle and Fabrication of the Array Sulfhydryl groups are known to exhibit high affinities for metal ions, such as Ag+, Hg2+, Cu2+, Zn2+, and etc., accompanied by the formation of metal thiolate complexes. Besides, other functional groups in thiol, such as amino group and carboxyl group also participate in the formation of metal thiolate complexes.31-33 Due to the commonality and diversity of thiols, the interactions between thiols and metal ions are affected by sulfhydryl group, as well as amino group and carboxyl group. On the basis of the inactivation of urease by metal ions, and the high affinities between thiols and metal ions, discrimination of thiols shall be possible through an array of urease-metal ion pairs. The principle of the as-fabricated array is shown in Scheme 1. Four kinds of urease-metal ion pairs were fabricated as the sensing array. In the presence of thiols, the strong affinity between metal ions and thiols induces the formation of metal thiolate complexes, which prevents metal ions from binding urease. As a result, the activity of urease is retained, and ammonia is generated by the hydrolysis of urea, which in turn causes the increase of pH value and induces a color change from yellow to pink with phenol red as the indicator. For each thiol, the array generates a unique response pattern that can be further differentiated via using LDA. Inhibition of Urease Activity by Metal Ions 9 / 28
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Owing to the basic principle of the fabricated sensor array in this study is based on preventing the inactivation of urease by metal ions through the interactions between thiols and metal ions, appropriate concentrations of metal ions are critical to achieve good discrimination performances. First, the hydrolysis of urea by urease was investigated with phenol red as the indicator. The absorption at 558 nm, maximum absorption wavelength of phenol red in alkali solution, increased gradually with increasing reaction time, and reached a saturation point at 15 min (Figure S1, Supporting Information). Thus, a reaction time of 15 min for the hydrolysis was performed in the following experiments. Furthermore, the inhibition of urease activity by metal ions including Ag+, Hg2+, Cu2+ and Zn2+, was studied. As shown in Figure 1a, the absorption decreased continuously as the concentration of Ag+ increased from 2.0 to 10 nM, indicating a gradual inhibition of the hydrolysis catalyzed by urease. And the inhibition efficiency (IE) was determined by the following equation: IE = (AU - A) / (AU - A0) × 100%
(1)
where AU and A0 are the absorption of the mixture of phenol red and urea in the presence and absence of urease, respectively, A is the absorption with both urease and metal ions. According to the plot of inhibition efficiency versus the concentration of Ag+ (Figure 1b), the inhibition of urease by Ag+ presented a dose dependent manner, and the inhibition efficiency reached a plateau at 8.0 nM of Ag+. Similarly, plateaued inhibition efficiency concentrations for Hg2+, Cu2+ and Zn2+ were evaluated to be 20, 30 and 1000 nM, respectively (Figure S2-4 in Supporting Information). For each kind 10 / 28
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of metal ion, the concentration with the plateaued inhibition efficiency was applied for fabricating the corresponding urease-metal ion pair in the following experiments. Additionally, the plateaued inhibition efficiencies of Ag+, Hg2+, Cu2+ and Zn2+ were calculated to be 96.8%, 98.9%, 89.5% and 80.0%, respectively. From the above results, it can be concluded that the inhibition capacity is Ag+ ~ Hg2+ > Cu2+ > Zn2+, which is in good agreement with previous studies.29 Urease contains sulfhydryl groups as integral parts of its catalytically active site, and metal ions can react with these sulfhydryl groups, leading to the inactivation of urease.30 According to Pearson acid base concept, soft acids (e.g., Ag+ and Hg2+) exhibiting a higher affinity towards soft bases (e.g., sulfhydryl group) of urease than that of borderline (e.g., Cu2+) and hard acids (e.g., Zn2+).34 Therefore, Ag+ and Hg2+ shall have better inhibition performance than Cu2+ and Zn2+ in theory, which demonstrates that our results are coincident with the theoretical analysis. Responses of the Sensor Array to Thiols On the basis of the above results, urease-metal ion pairs composed of urease-Ag+, urease-Hg2+, urease-Cu2+ and urease-Zn2+ pairs, were used to fabricate a sensor array. GSH, Cys, and other four thiols with extensive usage (dithiothreitol in biochemistry, cysteamine, 3-mercaptopropionic acid and 2-mercaptoethanol in surface chemical modification), were chosen as analytes for testing the proposed sensor array. The basic chemical properties of the six thiols used in the sensing experiments are shown in Table 1. To facilitate accurate discrimination of thiols, we generated response patterns with thiols at the concentration of 1.0 µM, the lowest detectable 11 / 28
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concentration of many previously reported methods.11,15,35 The effects of these thiols on the activity of urease under the experimental conditions, were studied at first. The changes in activity of urease were in the range from - 4.0% to 10 % in the presence of these thiols (Figure S5, Supporting Information), which indicated that thiols only have a small influence on the activity of urease at such a concentration. Next, the absorption responses of the proposed sensor array in the presence of these thiols were investigated. The responses of the sensor array to each thiol were tested five times in parallel, generating a 6 × 4 × 5 matrix. As shown in Figure 2a, the absorption changes (∆Abs) induced by different thiols are distinct, suggesting the feasibility of thiol discrimination using such a sensor array. To further generate the colorimetric response patters of the urease-metal ion pair assay against the six thiols, the raw data obtained were subjected to LDA using Matlab R2012a. By means of recognizing the linear combination of features that differentiate two or more classes of events or objects, LDA can maximize the variance ratio of events and enable maximal data separation. The LDA reduced the size of the training matrix (4 sensors × 6 thiols × 5 replicates) and transformed them into canonical factors. After the analysis, four canonical factors were generated (83.15%, 13.71%, 2.98%, and 0.16% of the variation) that represented linear combinations of the response matrices obtained from the colorimetric response patterns. The canonical patterns were clustered into six different groups, which were visualized as a well clustered two-dimensional plot (Figure 2B). This clear discrimination means that our technique can accurately identify thiols and that the proposed strategy can give a 12 / 28
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different colorimetric response fingerprint for individual target thiols. Next, a test was conducted to see whether the sensing array could identify biothiols at different concentrations. The data in Figure 3A and Figure S6 indicated different bithiols at concentrations ranging from 0.1 to 2.0 µM for Cys and 0.1-1.0 µM for GSH were well discriminated on a two-dimension LDA plot. The sensor array is sufficiently sensitive to detect biothoils as low as 0.1 µM, which is better than the previously reported sensor arrays.24,25 Furthermore, the mixtures of Cys and GSH with different molar ratios (Cys/GSH = 75/25, 50/50, 25/75 with 1.0 µM total thiol) were adopted to evaluate the ability of the sensor array to distinguish co-existing thiols. As shown in Figure 3B, these mixtures, as well as pure Cys and GSH were clearly distinguished from each other in the LDA plot. To verify the specific pattern-based responses of the array to thiols, the responses of the sensor array to several amino acids (glycine, tryptophan, proline and asparagine) were tested. The results confirmed that these amino acids at a concentration of 10 µM did not induce any significant responses from the array (Figure S7). Moreover, the possible interferences of other ions (Na+, K+, Mg2+, Ca2+ and Cl-) that may exist in biological samples were also investigated. The influences of the mixture of these ions to the array were studied first. There were only some effects on the urease-Ag+, urease-Cu2+ and urease-Zn2+ pairs (Figure S8). However, a significant effect on the urease-Hg2+ pair was observed, which might be attributed to the low solubility product constant of HgCl2. Therefore, a three urease-ion pairs based sensor array was used to discriminate thiols in the presence of the ions mixture. As shown in Figure S9, 13 / 28
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the 30 colorimetric response pattern (3 sensors × 6 thiols × 5 replicates) were clustered into 6 distinct groups with the classification accuracy of 96.67% on a two-dimension LDA plot. Unknown Thiol Samples Identification After successful discrimination of thiols at the specific concentration (1.0 µM), the next challenge is to identify thoils at unknown concentrations. Varying the concentrations of thiols would be expected to lead to the drastic alteration of colorimetric response patterns for the target thiols, resulting in the identification of thiols with both unknown concentration and unknown identity challenging. To simply the problem, pre-determination of the concentrations of the content using a general-purpose method is the common strategy.22,23 Herein, a method for rapidly determining the concentration of the total sulfhydryl group was introduced, and then the type of thiols can be further identified by the sensor array-based method. A protocol combing classical Ellman method and the proposed sensor array was designed to overcome the challenge of identification of unknown thiol samples. Ellman’s reagent (DTNB) has been the favorite reagent for spectrophotometric measurement of sulfhydryl groups in a sample since its introduction.36 The reactions between DTNB and the six kinds of thiols were estimated at first. The reactions were very rapid (in less than 5 min, data not shown) and generated a yellow solution, and subsequently absorption spectra were recorded. All the thiols processed similarity in reaction activity towards DTNB (Figure S10, Supporting Information), and the average molar extinction coefficient was calculated to be 14082 M-1 cm-1, which 14 / 28
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consists with the previously reported data (14150 M-1 cm-1).37 Using Cys as the standard, the calibration curve for further determination of sulfhydryl groups in unknown thiol samples was established (Figure S11, Supporting Information). Once the concentrations of sulfhydryl groups in unknown thiol samples were determined, a dilution procedure was introduced to get a final solution containing 1.0 µM of sulfhydryl group. Then the diluted unknown samples were analyzed by the proposed sensor array. Colorimetric response pattern were obtained and LDA analysis were performed to differentiate the unknown samples based on the training matrix obtained above. Notably, 29 of the 30 unknown thiol samples were correctly identified, affording an identification accuracy of 96.67% (Table S1, Supporting Information). The above results clearly implied that the urease-metal ions sensor array has practical applications for discrimination of thiol samples at unknown concentrations. Potential Applicability in Real Samples On the basis of the obtained results, the possible applicability of the sensor array for the measurement of thiols in biological samples was finally tested. The thoils diluted in fetal bovine serum (10% FBS, v/v) were used as an example. Owing to the complex composition of FBS, sample pretreatment procedures involving pH adjustment and ultrafiltration, were introduced to minimize the interferences of FBS on the sensor array. Then the influence of the FBS to the sensor array was investigated, and only some slight effects on the urease-Ag+, urease-Hg2+ and urease-Zn2+ pairs could be observed (Figure S12A, supporting information). While the effect on urease-Cu2+ pair was significant, plateaued inhibition of urease can be achieved with increasing the 15 / 28
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concentration of Cu2+ to 150 nM (Figure S12B, supporting information). After the optimizations, these thiols in FBS were tested using the sensor array. The colorimetric responses of the array the six thiols at 5 µM in the presence of 1% FBS were shown in Table S2 in supporting information, and these different responses provided unique patterns that effectively classified these thoils into six distinct groups by LDA (Figure 4). The results imply that the proposed sensor array has potential for the discrimination of thiols in real biological samples.
Conclusion In this study, a colorimetric sensor array that is simply comprised of inexpensive and commercially available regents was developed for thiol discrimination. Using this sensor array, six thiols including GSH, Cys, and other four types of thiols with widespread usage were successfully discriminated at a relatively low concentration (1.0 µM). Furthermore, combined with Ellman method, blind samples of thiol were identified at a high accuracy (96.67%) by this sensor array, indicating the potential for practical applications. The present study provides a new function of urease and its metal ion inhibitors, as a sensor array for thiols discrimination. Considering the diversity of enzymes and corresponding inhibitors, sensor arrays fabricated by enzyme-inhibitor pairs can be applied to other systems for sensing and differentiating a broad range of analytes, exhibiting great promise for environmental monitoring, biomedical diagnosis or specific biological purposes. Acknowledgments 16 / 28
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This research was financially supported in part by National Natural Science Foundation of China (Project No. 21505120), and China Postdoctoral Science Foundation (Project Nos. 2015M570499 and 2016T90536). Notes The authors declare no competing financial interest. Supporting Information Available Additional information including extensive figures as noted in text is available free of charge via the Internet at http://pubs.acs.org.
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Reference (1) Haugaard, N. Ann. N.Y. Acad. Sci. 2000, 899, 148-158. (2) Reddie, K. G.; Carroll, K. S. Curr. Opin. Chem. Biol. 2008, 12, 746-754. (3) Coulter, C. V.; Kelso, G. F.; Lin, T. K.; Smith, R. A.; Murphy, M. P. Free Radical Biol. Med. 2000, 28, 1547-1554. (4) Herzenberg, L. A.; De Rosa, S. C.; Dubs, J. G.; Roederer, M.; Anderson, M. T.; Ela, S. W.; Deresinski, S. C.; Herzenberg, L. A. Proc. Natl. Acad. Sci. U. S. A. 1997, 94, 1967-1972. (5) Weerapana, E.; Wang, C.; Simon, G. M.; Richter, F.; Khare, S.; Dillon, M. B.; Bachovchin, D. A.; Mowen, K.; Baker, D.; Cravatt, B. F. Nature 2010, 468, 790-795. (6) Kumar, C.; Igbaria, A.; D'Autreaux, B.; Planson, A. G.; Junot, C.; Godat, E.; Bachhawat, A. K.; Delaunay-Moisan, A.; Toledano, M. B. EMBO J. 2011, 30, 2044-2056. (7) Ha, S. B.; Smith, A. P.; Howden, R.; Dietrich, W. M.; Bugg, S.; O'Connell, M. J.; Goldsbrough, P. B.; Cobbett, C. S. Plant Cell 1999, 11, 1153-1164. (8) Ivanov, A. R.; Nazimov, I. V.; Baratova, L.; Lobazov, A. P.; Popkovich, G. B. J. Chromatogr., A 2001, 913, 315-318. (9) Inoue, T.; Kirchhoff, J. R. Anal. Chem. 2002, 74, 1349-1354. (10) Liu, P.; Huang, Y.-Q.; Cai, W.-J.; Yuan, B.-F.; Feng, Y.-Q. Anal. Chem. 2014, 86, 9765-9773. (11) Hu, Y.; Heo, C. H.; Kim, G.; Jun, E. J.; Yin, J.; Kim, H. M.; Yoon, J. Anal. Chem. 2015, 87, 3308-3313. 18 / 28
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(12) Banerjee, S.; Kar, S.; Perez, J. M.; Santra, S. J. Phys. Chem. C. 2009, 113, 9659-9663. (13) Yuan, X.; Tay, Y.; Dou, X.; Luo, Z.; Leong, D. T.; Xie, J. Anal. Chem. 2013, 85, 1913-1919. (14) Miao, Q.; Li, Q.; Yuan, Q.; Li, L.; Hai, Z.; Liu, S.; Liang, G. Anal. Chem. 2015, 87, 3460-3466. (15) Liu, J.; Sun, Y.-Q.; Huo, Y.; Zhang, H.; Wang, L.; Zhang, P.; Song, D.; Shi, Y.; Guo, W. J. Am. Chem. Soc. 2014, 136, 574-577. (16) Askim, J. R.; Mahmoudi, M.; Suslick, K. S. Chem. Soc. Rev. 2013, 42, 8649-8682. (17) Diehl, K. L.; Anslyn, E. V. Chem. Soc. Rev. 2013, 42, 8596-8611. (18) You, C. C.; Miranda, O. R.; Gider, B.; Ghosh, P. S.; Kim, I. B.; Erdogan, B.; Krovi, S. A.; Bunz, U. H.; Rotello, V. M. Nat. Nanotechnol. 2007, 2, 318-323. (19) De, M.; Rana, S.; Akpinar, H.; Miranda, O. R.; Arvizo, R. R.; Bunz, U. H. F.; Rotello, V. M. Nat. Chem. 2009, 1, 461-465. (20) Sun, W.; Lu, Y.; Mao, J.; Chang, N.; Yang, J.; Liu, Y. Anal. Chem. 2015, 87, 3354-3359. (21) Yang, X.; Li, J.; Pei, H.; Zhao, Y.; Zuo, X.; Fan, C.; Huang, Q. Anal. Chem. 2014, 86, 3227-3231. (22) Liu, J.; Li, G.; Yang, X.; Wang, K.; Li, L.; Liu, W.; Shi, X.; Guo, Y. Anal. Chem. 2015, 87, 876-883. (23) Yuan, Z.; Du, Y.; Tseng, Y. T.; Peng, M.; Cai, N.; He, Y.; Chang, H. T.; Yeung, E. 19 / 28
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S. Anal. Chem. 2015, 87, 4253-4259. (24) Ghasemi, F.; Hormozi-Nezhad, M.R.; Mahmoudi, M. Anal. Chim. Acta 2015, 882, 58-67. (25) Qian, S.; Lin, H. Anal. Bioanal. Chem. 2014, 406, 1903-1908. (26) Qian, S.; Lin, H. Anal. Chem. 2015, 87, 5395-5400. (27) Tram, K.; Kanda, P.; Salena, B. J.; Huan, S.; Li, Y. Angew. Chemie., Int. Ed. 2014, 53, 12799-12802. (28) de la Rica, R.; Baldi, A.; Fernandez-Sanchez, C.; Matsui, H. Anal. Chem. 2009, 81, 7732-7736. (29) Shaw, W. H. R. J. Am. Chem. Soc. 1954, 76, 2160-2163. (30) Shaw, W. H. R.; Raval, D. N. J. Am. Chem. Soc. 1961, 83, 3184-3187. (31) Fleischer, H.; Dienes, Y.; Mathiasch, B.; Schmitt, V.; Schollmeyer, D. Inorg. Chem. 2005, 44, 8087-8096. (32) Pesonen, H.; Aksela, R.; Laasonen, K. J. Phys. Chem. A. 2010, 114, 466-473. (33) Leng, Y. M.; Qian, S. H.; Wang, Y. H.; Lu, C.; Ji, X. X. ; Lu, Z. W.; Lin, H. W. Sci. Pep. 2016.6, 25354 (34) Jolly, W. L. Modern Inorganic Chemistry. McGraw-Hill: New York, 1984. (35) Zhu, F.; Li, X. Y.; Li, Y. C.; Yan, M.; Liu, S. Q. Anal. Chem. 2015, 87, 357-361. (36) Ellman, G. L. Arch. Biochem. Biophys. 1959, 82, 70-77. (37) Riddles, P. W.; Blakeley, R. L.; Zerner, B. Methods Enzymol. 1983, 91, 49-60.
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Figure Captions Scheme 1. Schematic of the sensor array based on urease-metal ion pairs for discrimination of thiols. Dithiothreitol (DTT) L-cysteine (Cys), L-glutathione reduced (GSH), cysteamine (Cm), 3-mercaptopropionic acid (MPA), and 2-mercaptoethanol (MCE) were used as analytes. Figure 1.Inactivation of urease by metal ions. (A) Hydrolysis of urea by urease with the presence of various concentrations of Ag+. Urease 0.6 µg mL-1; urea 0.1 M; and phenol red 0.002%. (B) Plot of inhibition efficiency versus the concentration of Ag+. Error bars represent the standard deviation of triple replicates. Figure 2. (A) Colorimetric response patterns of the Urease-metal ions array towards thiols as an average of five parallel measurements. The concentration of sulfhydryl group is 1.0 µM in each thiol sample. Urease 0.6 µg mL-1; urea 0.1 M; phenol red 0.002%; Ag+ 8.0 nM; Hg2+ 20 nM; Cu2+ 30 nM; and Zn2+ 1.0 µM. (B) Canonical score plots for the first two factors of colorimetric response pattern analyzed by LDA. Figure 3. (A) Canonical score plot for colorimetric response patterns against different concentrations of Cys (0.1, 0.25, 0.5, 1.0 and 2.0 µM). (B) Canonical score plot against thiol mixtures. The total thiol concentration was 1.0 µM. Urease 0.6 µg mL-1; urea 0.1 M; phenol red 0.002%; Ag+ 8.0 nM; Hg2+ 20 nM; Cu2+ 30 nM; and Zn2+ 1.0 µM. Figure 4. Canonical score plot for colorimetric response patterns against the six thiols in the presence of 1% FBS. Urease 0.6 µg mL-1; urea 0.1 M; phenol red 0.002%; Ag+ 8.0 nM; Hg2+ 20 nM; Cu2+ 150 nM; and Zn2+ 1.0 µM. 21 / 28
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Scheme 1
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Figure 1
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Figure 2
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Figure 3
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Figure 4
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Table 1. Chemical properties of the target thiols. Functional Thiols Abbr. MW group -SH, -COOH, L-glutathione GSH 307.32 -NH2 -SH, -COOH, L-cysteine Cys 121.15 -NH2 dithiothreitol
DTT
154.25
-SH
cysteamine 3-mercaptopropionic acid 2-mercaptoethanol
Cm
77.15
-SH, -NH2
MPA
106.14
-SH, -COOH
MCE
78.13
-SH
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For TOC only
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