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Application of Mass Spectrometry in Analysis of Non-Enzymatic Glycation Proteins in Diabetic Blood
CHINESE JOURNAL OF ANALYTICAL CHEMISTRY Volume 47, Issue 11, November 2019 Online English edition of the Chinese language journal
Cite this article as: Chinese J. Anal. Chem., 2019, 47(11): 1732–1741
REVIEW
Application of Mass Spectrometry in Analysis of Non-Enzymatic Glycation Proteins in Diabetic Blood LI Wei-Feng1,2, YAN De-Wen2, JIN Yu2, LI Hai-Yan2, MA Min1,*, WU Zheng-Zhi1,2,3,* 1
Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou 510632, China The First Affiliated Hospital of Shenzhen University, Shenzhen 518020, China 3 Shenzhen Institute of Gerontology, Shenzhen 518020, China 2
Abstract:
Diabetes mellitus (DM) is a metabolic disorder disease characterized by hyperglycemia. Long-term physiological
hyperglycemia will aggravate the degree of non-enzymatic glycation (NEG) proteins. Because the abnormal changes of non-enzymatic process can cause a series of pathological variation, NEG proteins have attracted more and more attention. Mass spectrometry (MS) is an indispensable and powerful analytical tool for the qualitative and quantitative analysis of proteins due to its ultra-high sensitivity, excellent detection limits, and multi-component simultaneous analysis, and has been applied to the identification and qualification of NEG proteins. Herein, the principle of NEG proteins and the application of MS in the identification and quantitation of NEG plasma proteins and hemoglobin (Hb) in blood are reviewed. The forecast in the analysis of NEG proteins based on MS is also provided. Key Words:
Mass spectrometry; Non-enzymatic glycation; Proteins; Diabetes mellitus; Review
1 Introduction Non-enzymatic glycation (NEG) is the reaction between carbonyl groups of reducing sugars and α-amino group of ε-side chain or N-terminal amino acid residues of proteins without any enzyme. Reversible aldehyde imines (Schiff base) are firstly generated and then go through a rearrangement to form stable Amadori products. Subsequently, Amadori products are affected by oxidation, degradation, crosslinking or other reactions, forming variously heterogeneous products, namely advanced glycation end products (AGEs)[1]. The detailed process is shown in Fig.1. The formed AGEs will accumulate in cells, interact with receptors in body, and cause tissue damage, leading to the development of diabetes mellitus (DM). Hence, it is clinically significant to study the distribution, cycles, and metabolites of AGEs, affording to understand the mechanism of DM at the micro-level and screen new biomarkers[2]. There are several methods for determination of global
AGEs levels. The methods based on spectrometry can supply real-time monitoring of reaction according to the color changes but with no structure information[3]. High performance liquid chromatography (HPLC) has been employed for the determination of AGEs due to its high accuracy and sensitivity. However, this method lacks specificity[4]. The high-specific performance of immunoassaysbased methods has made them become important candidates for determination of NEG proteins. But problem exists in terms of rigorous sample preparation, long time needed for preparing antibody, and interferences from complex samples, constraining their application in the universal detection of AGEs, especially for the unknown ones. With the advancement of mass spectrometry (MS), proteomics based on MS has become a powerful tool for the identification and quantitation of proteins and their post-translational modifications (PTMs). As AGEs are prevalent in diabetes, particularly for type II diabetes because of its high percent in DM (> 90%)[5], this paper mainly reviews the qualitative and quantitative analysis
________________________ Received 8 May 2019; accepted 8 July 2019 *Corresponding author. Email: [email protected], [email protected] This work was supported by the Sanming Project of Medicine in Shenzhen, China (No. SZSM201612049), the Natural Science Foundation of China (No. 81574038), and the Shenzhen Discipline Layout Project of China (No. JCYJ20170412161254416). Copyright © 2019, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences. Published by Elsevier Limited. All rights reserved. DOI: 10.1016/S1872-2040(19)61197-7
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Fig.1 General patterns of chemistry involved in the Maillard reaction
of NEG proteins in type II DM using MS, and provides some perspective for the future study.
2 Biological principles of NEG proteins Generally, glycation study is based on glucose because it is the major nutrient in our body. It has to be noted that the NEG is not limited to glucose. Ribose, galactose and intermediates of glucose metabolism such as methylglyoxal (MG) and glycolic aldehyde are also involved in the reaction. Studies have shown that NEG can influence the structure and property of proteins[6]. For example, space conformation of myoglobin is changed by glycation, leading to the variation in biological functions, such as oxygen release, peroxidase and hydrolytic enzyme activity[7]. Compared to glyoxal and glyceraldehyde modification, human serum albumin (HSA) with MG modification shows the most obvious change in spatial structures, indicating that proteins with different modifications exhibit varieties in spatial structure[8]. According to the existing clinical study, NEG proteins have been proved to be closely related to the occurrence and development of various diseases, including DM, kidney disease, and Alzheimer’s
Fig.2
disease[8,9]. Therefore, comprehensive study of NEG proteins is crucial for the in-depth understanding of these diseases. As various kinds of compounds are involved in the protein glycation, the distinct characteristic and difficulty in the analysis of AGEs are their inherently spatial heterogeneities. Even to single protein with modification based on glucose, diverse spatial structures are produced because multiple sites in protein can be attacked. Generally, amino acids with lower pH and strong affinity are believed to promote their glycation. Meanwhile, the glycated sites are affected by their surrounding environment. The presence of positive charged amino acids such as histidine, lysine, and acidic amino acids at adjacent positions (sequence or spatial proximity) will facilitate the glycation[10,11]. For example, glycation of Val-1 of β-Hb is higher than that of α-Hb mainly because of the presence of His-2. Moreover, Wang et al[12] reported that the phosphate triangle constructed by the histidine and lysine in HGKK peptides of α-chain and β-chain of Hb provides the proton transfer pathway for rearrangement, promoting their glycation. As shown in Fig.2, similar structure of HGKK can be obtained in α-chain and β-chain of Hb. Of note, only the β-Lys-66 and α-Lys-61 with adjacent His in HGKK are glycated according to the MS results. Hence, it can be inferred that NEG proteins contain rich biological information. Shedding light on relationship among the protein structure, property, function, and biological significance after glycation might provide new clues for the mechanism of the occurrence and development of DM.
3 Analysis of NEG proteins using MS As the abnormal expression of NEG proteins results in a series of disease, it is of significance to illuminate the bio-information carried by NEG proteins. The rapid development in software and hardware of MS, advancement in chromatographic separation technology, and emergence of diverse enrichment methods for specific PTMs have laid solid foundation for the determination of PTMs using MS. The application of MS in the quantitative analysis of NEG proteins has obtained some progress. The involved MS techniques and their characteristics are shown in Table 1.
Molecular model of HGKK consensus on (A) β-Lys-66 and (B) α-Lys-61 site of hemoglobin[12]
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Table 1 Ionization types Matrix assisted laser desorption/ionization (MALDI)
Sample states
Summary of mass spectrometry for analysis of NEG proteins Classification Non-label
Solid /liquid Label Non-label Bottom-up Label
Electrospray ionization (ESI)
Liquid Top-down/middle down
3.1 Application of MALDI-TOF MS in analysis of NEG proteins Matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) has played an important role in the qualitative analysis of compounds with integrated advantages such as simple mass spectra, highautomation, and high throughput. Routine workflow for protein analysis based on MALDI is shown in Fig.3. Generally, the targeted protein or its digests at concentration level of µM are mixed (1:1–1:10, V/V) with matrix such as sinapic acid (SA) or α-cyanide-4-hydroxy-cinnamic acid (HCCA). Then the mixture is transferred to the sample target and dried to form crystal for subsequent MS analysis. Hitherto, MALDI-TOF MS has been applied in the analysis of various NEG proteins, including Hb and plasma proteins, showing potential value in clinical diagnosis. 3.1.1
Characteristics Suitable for analysis at protein/peptide level, simple sample preparation, high-throughput, rapid analysis speed, poor stability Suitable for peptide level analysis with high-throughput, quantitative analysis of glycated peptides, complex sample preparation
Application of MALDI-TOF MS in analysis of NEG proteins in plasma
The first application of MALDI-TOF MS for NEG proteins analysis were dated back to 1990s. Lapolla et al[13] and Jørgensen et al[14] studied the m/z of bovine serum albumin as function of incubation time and glucose level. It was shown that high glucose concentration in DM significantly accelerated the plasma proteins glycation, such as HSA. Subsequently, glycation level of HSA was employed to reflect
Suitable for peptide level analysis, qualitative and quantitative analysis for unknown glycated protein, lacking protein spatial information Suitable for peptide level analysis, excellent quantitative performance, no protein spatial information, complex sample preparation Suitable for protein/large peptides level analysis, simple sample pretreatment, providing protein spatial information, protein variant information, challenge exists in protein separation and purification, relying on high-resolution mass spectrometer
the glucose level in the previous 2–3 weeks, which was in favour of designing short-term glucose controlled protocol. With visual reading of HSA m/z and its glycation states by MALDI-TOF MS, blood glucose level could be easily and quickly obtained. Meanwhile, MALDI-TOF MS could offer glycation level in immunoglobulins (Igs) and show the binding glucose number with the tendency as poorly glucose controlled group > blood glucose under controlled group > healthy group[15]. Moreover, fragments of antigen binding in papain digests (Fab) and crystallization section could be obtained by MALDI-TOF MS. The MS results showed that glucose was inclined to combine with Fab fragments, revealing the reason for immune deficiency in DM at the molecular level[16]. As a powerful analytical technique, MALDI-TOF MS provides the global glycation level of proteins but without PTMs[17]. The introduction of isotope labeling technology combined with digested strategy can effectively bypass the above problems. With the integrated advantage of 16O and 18O labeling technology and MALDI-TOF MS, quantitative analysis of glycation HSA peptides have been achieved as shown in Fig.4[18–21]. But this method is only suitable for vitro study. Using the characteristic of the 1 Da mass difference of the same peptide under the reduction of NaBH4 and NaBD4, qualitative and quantified analysis of glycation peptides were fulfilled in vivo[22]. However, the mass deviation of 1 Da is still interfered by the isotope peaks of the analyzed peptides, which limits its further application. As shown in Fig.5, the combination of agent (NaBH3CN, NaBD3CN) for reducing protein with isotopic dimethylation agent (CH2O, CD2O) during protein digesting can lead to the mass difference at 4m + 3n, where m indicates the number of N-terminal and lysine residues, while n represents the number of glycated sites. Using the newly developed protocol, quantitative analysis of NEG proteins in plasma are acquired[23]. 3.1.2
Fig.3
Workflow of routine MALDI for protein/peptide analysis
Application of MALDI-TOF MS in analysis of glycated Hb
Compared with glycated HSA, glycated hemoglobin (GHb)
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Fig.4
Workflow of analysis of glycated sites of HSA using stable isotope labeling with MALDI-TOF MS: (A) 16O-labeled HSA, 18O-labeled glycated HSA, and the mixed digests of HSA and glycated HSA with ratio of 50:50 (V/V), (B) mass spectra of 16O, 18O and mixed oxygen labeled peptides[18]
Fig.5
Workflow of reductive amination combining dimethylation[23]
has longer half-life cycle. The GHb concentration is not affected by sports or diets but is related to the red blood cell life and the average blood glucose level. As GHb can clinically represent the average blood glucose concentration in the previous 2–3 months, it has been employed as the gold standard for assessing the blood glucose status in DM [24]. Since then, MALDI-TOF MS has been used for the determination of GHb. Although the early GHb results of MALDI-TOF MS are different from those of the conventional methods based on liquid chromatography or immunoassay, a conclusion is drawn that NEG can occur in both the Hb chains[25–27]. MALDI-TOF MS has advantages in terms of simple spectra
and good qualitative performance. However, the Gaussian distribution of laser energy and heterogeneous crystallization of the samples makes it difficult to obtain desirably quantitative results. Increasing the laser frequency following the optimized matrix crystallization conditions can effectively compensate for the signal fluctuation caused by those defects[28]. In the case of optimizing the crystallization of SA matrix and Hb, the quantitation of glycated and glutathionylated Hb were achieved by keeping the total number of ions consistent for each sample[29]. This new strategy can offer precise coefficient of variation (CVs) in the intraday and interday which are within the international federation of clinical chemistry (IFCC) recommendation
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limits. Benefiting from the improved sensitivity of the MS instrument, elevated sampling speed (laser frequency increases from 10 Hz to 1 kHz), and increasing speed in data analysis, high precision and accuracy were gained for the determination of GHb[30]. Using high laser frequency, averaging all the spectra for each single spot could greatly increase the accuracy (CVs ≤ 2.5%). The obtained β-Hb glycation results were comparable to those obtained by the clinically validated HPLC. Of note, only 0.5 nL of blood was needed for GHb analysis. To fully take advantage of high-throughput and rapid analysis ability of MALDI-TOF MS, Li et al[31] developed a laser-assisted proteolysis which had the capability for accelerating proteolysis and enhancing N-termini analysis. This facilitated to the quantitative analysis of GHb peptides in 2000 samples within 8 h. It was noteworthy that all the steps including the sample loading, proteolysis, laser irradiation, and detection were completed in the same sample target, avoiding the cumbersome sample pretreatment in traditional methods. With the continuous increase of MS hardware and the improvement of laser frequency, application of MALDI-TOF MS for NEG proteins analysis will be further broadened. 3.2
Application of ESI-MS in analysis of NEG proteins
MALDI-TOF MS has strengths in aspects of the qualitative and quantitative analysis of NEG proteins in high-throughput way, but the preparation method of solid sample is not suitable for the analysis of all proteins. Liquid injection of electrospray mass spectrometry (ESI-MS) can not only realize the qualitative and quantitative analysis of proteins, but also reflect proteins in their inherent states[32,33]. As the inlet of ESI is compatible with the detector of chromatography, it is particularly suitable for analyzing the complex samples. Therefore, ESI-MS/MS has been widely used for the AGEs analysis in blood[34]. Figure 6 shows a regular identification process for protein modification sites. As shown in Fig.6, cells or tissues were broken to extract protein firstly. Then, enrichment was employed for the specific protein followed by hydrolysis. The desalted peptides were detected by MS for obtaining full scan or MS/MS spectra. Finally, the raw data were searched against the Uniprot fasta database, realizing the identification of proteins and PTMs. 3.2.1
Application of ESI-MS/MS in analysis of NEGs in plasma
Lapolla et al[34] found that LC-MS/MS could offer information that the glycated reaction would reduce the hydrolysis efficiency for HSA. Meanwhile, five sites including Lys-233, Lys-276, Lys-378, Lys-545 and Lys-525 were found to be sensitive to the reducing sugars, which were in good agreement with the theoretical results. Although the
Fig.6
Workflow of identification of protein modification sites using ESI-MS/MS
peptides with modification could be directly analyzed by LC-MS/MS, the lower glycated sites might be missing. The enrichment for glycation peptides by boronic acid affinity chromatography could elevate the identification of glycated sites[35,36]. By combining this method with the electron transfer dissociation (ETD), Zhang et al[37] found that 76 plasma proteins and 31 red cell membrane proteins were simultaneously glycated. More importantly, the number of NEG proteins and glycated sites in DM were higher than those of the controls. Using immune-depletion, enrichment, and fractionation strategies, totally 7749 unique glycated peptides corresponding to 3742 unique NEG proteins were identified[38]. The development of isotope labeling technique has become a new path for proteomics study. By the use of high-energy collisional dissociation (HCD) and collision-induced dissociation (CID) technique, Priego-Capote et al[39,40] identified 161 glycation sites corresponding to 50 plasma proteins. The quantitation of glycation peptides was achieved by calculating the ratio of intensity of peptides treated by 13C6 and 12C6 labelled glucose, respectively. The detailed process is shown in Fig.7. Due to the characteristic of selective glycation in HSA, the glucose-sensitive peptides and glucose-insensitive peptides were discovered by isotope labeling MS. Hence, Zhang et al[41] developed a standard-free, label-free MS method for the determination of glycated peptides by employing glucose-insensitive peptides as internal standards, promoting to screening 8 potential markers for the early diagnosis of diabetes. The introduction of new ion dissociation improves the performance of protein identification. The optimizing HCD mode, developed by Frolov et al[42], was proven to be helpful for the discovery of new modification glycation sites. Besides, 101 early glycation sites as well as numerous AGEs sites on diverse plasma proteins in 1 µL of undepleted and unenriched blood were identified with good reproducibility. In addition to ion dissociation mode, the MS acquisition mode also affects the protein identification. Hoffmann et al[43] developed a gas phase fractionation approach which fulfilled the qualitative and quantitative determination of human AGEs. With the
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Fig.7
Workflows for quantitative analysis of NEG proteins labeling with 12C and 13C glucose[39]
newly developed method, the glycation degree of 19 sites was found to be increased in DM compared to the controls. With the utilization of multiple reaction monitoring (MRM), Sandro Spiller screened six glycated peptides from glycation HSA in DM with significant higher levels than in the non-diabetic men. Moreover, the combination of glycated Lys-141 of haptoglobin, fasting plasma glucose, and HbA1c could provide a sensitivity of 94%, specificity of 98% for newly diagnosed DM[44–46]. As has been shown that glycation such as Nε-(carboxyethyl)-(CEL)- and Nε-(carboxymethyl) lysine (CML) were involved in cell adhesion, signaling pathways, and angiogenesis in human body[47]. However, the level of these modifications in plasma was too low to detect. Therefore vitro incubation was indispensable[48]. By use of the characteristic fragmentation of CEL and CML modified peptides, Graifenhagen et al[49] identified 21 CML and CEL modifications corresponding to 17 proteins in human plasma. With the rapid development in the fields such as computer science and semiconductor, on-line automatic processing method was produced and applied to the analysis of NEG proteins. Zhang et al[50] developed a method for online
separation and analysis of glycated peptides in plasma by two-dimensional LC-MS/MS. The detection limit of the method was down to 1.2 × 10–12 g with relative deviation of less than 20%. The number of glycated peptides obtained by the two-dimensional LC-MS/MS was three times that of onedimensional LC-MS/MS. Jeona et al[51] developed a highthroughput protein pretreatment system which integrated steps such as protein reduction, alkylation, hydrolysis, and enrichment of glycation peptide in a 96-well plate. This new system had good reproducibility and high-throughput, which was of great significance for the label-free quantitative analysis of proteins. In addition, with the continuous upgrading of MS hardware and software, various new acquisition modes have emerged, including parallel reaction monitoring (PRM), and data-independent acquisition/ sequential window acquisition of all theoretical fragment ions (DIA/SWATH). Korwar et al[52] used PRM, SWATH and MSE to study the distribution of HSA glycated peptides in DM with different pathological conditions. It was of interest to find that similar results were obtained using the three strategies, which indicated their feasibility in screening
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potential biomarkers for the diagnosis of diabetes. Compared with data-dependent acquisition (DDA), DIA/SWATH can obtain the fragmentation for all proteins in samples without any targeted peptides, offering performance with good reproducibility and high throughput. It is noteworthy that DDA is the most effective method for identifying both the known and unknown proteins. Therefore, DDA is always adopted for the establishment of peptide spectrum library to take full advantage of DIA. Bruderer et al[53] used DDA to establish the library of plasma proteins in the obesity and diabetes. Based on the DDA database, good reproducibility and stable quantification of 565 plasma proteins in 1508 clinical samples were achieved by DIA. For the first time, 234 glycation sites were determined without the enrichment of glycated peptides. According to the clinical data, it was found that the glycation level of protein was increased first and then decreased between the periods of losing weight stage and maintaining weight stage. Although bottom-up proteomics aforesaid was the dominant methods for NEG proteins analysis, the steps such as denaturation, reduction, alkylation, and digestion were necessarily needed. However, these pretreatment would result in failure to reconstruct the proteoforms presented in a given sample, making it difficult to study the linkages among different PTMs. The development of middle-down and top-down proteomics strategy can alleviate the aforesaid limitations[54]. Different from the enzymes used in the bottom-up proteomics, the enzymes employed in middle-down proteomics produced larger peptides, promoting to a wider application[55]. For top-down proteomics, the intact proteins were directly analyzed without any enzyme digestion, providing more accurate and ample biological information[56–58]. Because a variety of species were involved in NEG, AGEs exhibited heterogeneities. The utilization of middle-down and top-down techniques can effectively identify the proteoforms in AGEs[59]. Liu et al[60] found that middle-down proteomics could not only offer the rapid identification of proteins containing FC fragments, but also effectively improve the identification of various PTMs in heterogeneous proteins, such as glycosylation, glycation, and oxidation. More importantly, it could be used to obtain the dynamic changes of glycation during the development and application of clinical antibody drugs. As has been known that human apolipoprotein A-I (ApoA-I), a mediator of high-density-lipoprotein cholesterol efflux (HDL-E), is inversely associated with coronary heart disease risk. But proteoforms of ApoA-I, and the relationship between each proteoform and the level of HDL-E remain to be studied. Seckler et al[61] studied the ApoA-I using top-down proteomics and found that ApoA-I contained 18 proteoforms and 11 PTMs, including glycosylation, phosphorylation, carboxymethyl, and palmitoylation. Six proteoforms were showed significantly (p < 0.0005) higher intensity in high
HDL-E individuals. 3.2.2
Application of ESI-MS/MS in analysis of glycated Hb
In 2002, LC-ESI/MS was recognized as the standard method for the determination of GHb as approved by IFCC Working Group. The peptide VHLTPE at the N-terminal of β-Hb chain was first isolated by the HPLC and then determined by ESI-MS[62]. Priego-Capote et al[63] provided the quantitative information of glycated human hemolysate associated to the glucose level by glycation isotopic labeling. The results showed that the glycation level of erythrocyte proteins exhibited positively correlation with the GHb value[63]. Wang et al[12] used trypsin, chymotrypsin and endoproteinase Glu-C to fully identify the glycation sites in Hb. By utilizing the complementary of the three enzymes, in-depth glycation sites were obtained. Each glycated site exhibited significant difference in normal and diabetes. As the main component of red blood cells, Hb is affected not only by glucose, but also by other metabolites in vivo and vitro. Therefore, it is clinically significant to obtain other PTMs to further understand the mechanism of diabetes. For example, the concentration of MG modified Hb of diabetes is higher than those of the controls[64,65]. It is reported that the increased MG in human always causes insulin resistance and typical metabolic disorder in diabetes, indicating that MG might be the reason for diabetes and its complications[66]. In addition, Kulkarni et al[67] targeted the PTMs (glycation, CEL, and CML) in diabetes with different phenotypes using PRM. They found that the combination of the three glycation modifications could effectively reflect the status of diabetes. Using the Nano-LC-MS/MS system, Chen et al[68–71] studied various PTMs in Hb in diabetes with smoking or non-smoking. The MS results showed that the influence of smoking on diabetes could be reflected by PTMs in Hb, providing theoretical foundation for studying on the correlation between smoking and DM.
4
Conclusion and perspective
Nowadays, mass spectrometry has been widely used in the analysis AGEs. Global glycation level of proteins could be achieved by MALDI-TOF MS, while multiple PTMs in proteins can simultaneously be obtained via bottom-up proteomic. However, the dynamic glycation and variable abundance of the various PTMs pose a great challenge for analyzing both the high and low abundance PTMs simultaneously. In addition, bottom-up proteomics provide the protein information at peptide level, losing the attributive information between peptides and proteins, which makes it difficult to analyze the interaction between different PTMs. Given the advantages of middle-down and top-down
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proteomics, it is a tendency to study NEG proteins for gaining biological information including structure and function. It is still a problem for the quantitation of proteins with high speed, good stability, and reproducibility. The integrated sample preparation technology combined with DIA can offer more accurate and stable protein quantitative information with the good reproducibility and high throughput. The newly developed BoxCar strategy allows the signal-to-noise ratio of low abundance peptides to increase with an order of magnitude in the case of no separation enrichment. These results can be obtained by dividing the precursor window into multiple narrow mass-to-charge segments, improving ion injection time, and averaging signal for both the high abundance and low abundance peptides, which is another tendency for the quantitation of NEG proteins. These new strategies will not only meet the needs of automated analysis in clinical field, but also will be in line with the developing needs in the medical big data era[72–75].
[15] Liu Z,Schey K L. J. Am. Soc. Mass Spectrom., 2005, 16(4): 482–490 [16] Lapolla A, Fedele D, Garbeglio M, Martano L, Tonani R, Seraglia R, Favretto D, Fedrigo M A, Traldi P. J. Am. Soc. Mass Spectrom., 2000, 11(2): 153–159 [17] Lapolla A, Brancia F L, Bereszczak J, Fedele D, Baccarin L, Seraglia R, Traldi P. Mol. Nutr. Food Res., 2007, 51(4): 456–461 [18] Barnaby O S, Cerny R L, Clarke W, Hage D S. Clin. Chim. Acta, 2011, 412(17): 1606–1615 [19] Barnaby O S, Cerny R L, Clarke W, Hage D S. Clin. Chim. Acta, 2011, 412(3): 277–285 [20] Wa C, Cerny R L, Clarke W A, Hage D S. Clin. Chim. Acta, 2007, 385(1): 48–60 [21] Barnaby O S, Wa C, Cerny R L, Clarke W, Hage D S. Clin. Chim. Acta, 2010, 411(15): 1102–1110 [22] Liu H, Ponniah G, Neill A, Patel R, Andrien B. J. Chromatogr. B, 2014, 958: 90–95 [23] Tong Q-H, Yan T Y, Tao T, Zhang L, Xie L Q, Lu H J. Anal.
References
Chem., 2018, 90(6): 3752–3758 [24] Lipska K J, Krumholz H M. JAMA, 2017, 317(10): 1017–1018
[1]
Lapolla A, Fedele D, Traldi P. Mass Spectrom. Rev., 2000, 19(5): 279–304
[2]
Lapolla A, Fedele D, Traldi P. Diabetes-Metab. Res., 2001, 17(2): 99–112
[3]
Gusev S I, Borovkova M A, Strepitov M A, Khodzitsky M K. Chinese Optics, 2018, 11(2): 182–189
[4]
Johnson R N, Metcalf P A, Baker J R. Clin. Chim. Acta, 1983, 127(1): 87–95
[5]
Association A D. Diabetes Care, 2014, 37(Supplement 1): S81–S90
[6]
[27] Lapolla A, Tubaro M, Fedele D, Reitano R, AricòN C, Ragazzi E, Seraglia R, Vogliardi S, Traldi P. Rapid Commun. Mass Spectrom., 2005, 19(2): 162–168 [28] Duncan M W, Nedelkov D, Walsh R, Hattan S J. Clin. Chem., 2016, 62(1): 134 [29] Biroccio A, Urbani A, Massoud R, di Ilio C, Sacchetta P, Bernardini S, Cortese C, Federici G. Anal. Biochem., 2005, 336(2): 279–288
You Y, Liu F, Lin Y W, Du K J, Liu J H, Wen G B. Chem. Life, 2013, 33(06): 627–632 Khan M S, Tabrez S, Rabbani N, Shah A. J. Fluoresc., 2015, 25(6): 1721–1726
[9]
Chem., 1995, 67(6): 1034–1041
Ragone R, Zambruno G, Carbone V, Ribatti D, Peschle C, 2806–2818
[8]
9(1): 1–7 [26] Gusev A I, Wilkinson W R, Proctor A, Hercules D M. Anal.
Facchiano F, D’Arcangelo D, Russo K, Fogliano V, Mennella C, Capogrossi M C, Facchiano A. Mol. Endocrinol., 2006, 20(11):
[7]
[25] Niu S, Zhang W, Chait B T. J. Am. Soc. Mass Spectrom., 1998,
Shuvaev V V, Laffont I, Serot J-M, Fujii J, Taniguchi N, Siest G. Neurobiol. Aging., 2001, 22(3): 397–402
[30] Hattan S J, Parker K C, Vestal M L, Yang J Y, Herold D A, Duncan M W. J. Am. Soc. Mass Spectrom., 2016, 27(3): 532–541 [31] Li L, Zang W, Zhang X. Anal. BioanaI. Chem., 2016, 408(5): 1507–1513 [32] Zehl M,Allmaier G. Rapid Commun. Mass Spectrom., 2004, 18(17): 1932–1938 [33] Nakanishi T, Miyazaki A, Kishikawa M, Yasuda M, Tokuchi Y,
[10] Acosta J, Hettinga J, Flückiger R, Krumrei N, Goldfine A,
Kanada Y, Shimizu A. J. Mass Spectrom., 1997, 32(7): 773–778
Angarita L, Halperin J. P. Natl. Acad. Sci. USA, 2000, 97(10):
[34] Lapolla A, Fedele D, Reitano R, AricòN C, Seraglia R, Traldi P,
5450 [11] Johansen M B, Kiemer L, Brunak S. Glycobiology, 2006, 16(9): 844–853 [12] Wang S H, Wang T F, Wu C H, Chen S H. J. Am. Soc. Mass Spectrom., 2014, 25(5): 758–766 [13] Lapolla A, Gerhardinger C, Baldo L, Fedele D, Keane A, Seraglia R, Catinella S, Traldi P. BBA-Mol. Basis Dis., 1993, 1225(1): 33–38 [14] Jørgensen T J D, Bojesen G, Rahbek-Nielsen H. Eur. Mass Spectrom., 1998, 4(1): 39–45
Marotta E, Tonani R. J. Am. Soc. Mass Spectrom., 2004, 15(4): 496–509 [35] Zhang Q, Tang N, Brock J W C, Mottaz H M, Ames J M, Baynes J W, Smith R D, Metz T O. J. Proteome Res., 2007, 6(6): 2323–2330 [36] Frolov A, Hoffmann R. Ann. N.Y. Acad. Sci., 2008, 1126(1): 253–256 [37] Zhang Q, Tang N, Schepmoes A A, Phillips L S, Smith R D, Metz T O. J. Proteome Res., 2008, 7(5): 2025–2032 [38] Zhang Q, Monroe M E, Schepmoes A A, Clauss T R W,
LI Wei-Feng et al. / Chinese Journal of Analytical Chemistry, 2019, 47(11): 1732–1741
Gritsenko M A, Meng D, Petyuk V A, Smith R D, Metz T O. J. Proteome Res., 2011, 10(7): 3076–3088 [39] Priego-Capote F, Scherl A, Müller M, Waridel P, Lisacek F, Sanchez J C. Mol. Cell. Proteomics, 2010, 9(3): 579–592 [40] Priego-Capote F, Ramírez-Boo M, Finamore F, Gluck F, Sanchez J C. J. Proteome Res., 2014, 13(2): 336–347 [41] Zhang M, Xu W, Deng Y L. Diabetes, 2013, 62(11): 3936 [42] Keilhauer E C, Geyer P E, Mann M. J. Proteome Res., 2016, 15(8): 2881–2890 [43] Frolov A, Blüher M, Hoffmann R. Anal. Bioanal. Chem., 2014, 406(24): 5755–5763 [44] Spiller S, Frolov A, Hoffmann R. Protein Pept. Lett., 2017, 24(10): 887–896 [45] Spiller S, Li Y, Blüher M, Welch L, Hoffmann R. Clin. Proteom., 2017, 14(1): 10 [46] Spiller S, Li Y, Blüher M, Welch L, Hoffmann R. Pharmaceuticals, 2018, 11(2): 38, doi: 10.3390/ph11020038 [47] Bollineni R C, Fedorova M, Blüher M, Hoffmann R. J. Proteome Res., 2014, 13(11): 5081–5093 [48] Ahmed N, Thornalley P J. Ann. N.Y. Acad. Sci., 2005, 1043(1): 260–266 [49] Greifenhagen U, Nguyen V D, Moschner J, Giannis A, Frolov A, Hoffmann R. J. Proteome Res., 2015, 14(2): 768–777 [50] Zhang L, Liu C W, Zhang Q. Anal. Chem., 2018, 90(2): 1081–1086 [51] Jeon J, Yang J, Park J M, Han N Y, Lee Y B, Lee H. J. Chromatogr. B, 2018, 1092: 88–94 [52] Korwar A M, Vannuruswamy G, Jagadeeshaprasad M G, Jayaramaiah R H, Bhat S, Regin B S, Ramaswamy S, Giri A P, Mohan V, Balasubramanyam M, Kulkarni M J. Mol. Cell Proteomics, 2015, 14(8): 2150–2159 [53] Bruderer R, Muntel J, Müller S, Bernhardt O M, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris W H M, Hager J, Valsesia A, Dayon L, Reiter L. Mol. Cell Proteomics, 2019, 18(6): 1242 [54] Sun R X, Luo L, Chi H, Liu C, He S M. Prog. Biochem. Biophys., 2015, 42(02): 101–114 [55] Fornelli L, Ayoub D, Aizikov K, Beck A, Tsybin Y O. Anal. Chem., 2014, 86(6): 3005–3012 [56] Li H, Nguyen H H, Ogorzalek Loo R R, Campuzano I D G, Loo J A. Nat. Chem., 2018, 10: 139–148 [57] Li H, Sheng Y, McGee W, Cammarata M, Holden D, Loo J A. Anal. Chem., 2017, 89(5): 2731–2738
[58] Li H, Wolff J J, van Orden S L, Loo J A. Anal. Chem., 2014, 86(1): 317–320 [59] Yang Y, Wang G, Song T, Lebrilla C B, Heck A J R. mAbs, 2017, 9(4): 638–645 [60] Liu T, Guo H, Zhu L, Zheng Y, Xu J, Guo Q, Zhang D, Qian W, Dai J, Guo Y, Hou S, Wang H. Chromatographia, 2016, 79(21): 1491–1505 [61] Seckler H d S, Fornelli L, Mutharasan R K, Thaxton C S, Fellers R, Daviglus M, Sniderman A, Rader D, Kelleher N L, Lloyd-Jones D M, Compton P D, Wilkins J T. J. Proteome Res., 2018, 17(6): 2156–2164 [62] Jeppsson J O, Kobold U, Barr J, Finke A, Hoelzel W, Hoshino T, Miedema K, Mosca A, Mauri P, Paroni R, Thienpont L, Umemoto M, Weykamp C. Clin. Chem. Lab. Med., 2002, 40(1): 78–79 [63] Priego-Capote F, Ramirez-Boo M, Hoogland C, Scherl A, Mueller M, Lisacek F, Sanchez J C. Anal. Chem., 2011, 83(14): 5673–5680 [64] Shimada S, Tanaka Y, Ohmura C, Tamura Y, Shimizu T, Uchino H, Watada H, Hirose T, Nakaniwa T, Miwa S, Kawamori R. Diabetes Res. Clin. PR, 2005, 69(3): 272–278 [65] Chen H J C, Chen Y C, Hsiao C F, Chen P F. Chem. Res. Toxicol., 2015, 28(12): 2377–2389 [66] Moraru A, Wiederstein J, Pfaff D, Fleming T, Miller A K, Nawroth P, Teleman A A. Cell Metab., 2018, 27(4): 1–9 [67] Jagadeeshaprasad M G, Batkulwar K B, Meshram N N, Tiwari S, Korwar A M, Unnikrishnan A G, Kulkarni M J. Clin. Proteom., 2016, 13(1): 7 [68] Chen H J C, Lin W P, Chiu S D, Fan C H. Chem. Res. Toxicol., 2014, 27(5): 864–872 [69] Chen H J C, Yang Y F, Lai P Y, Chen P F. Anal. Chem., 2016, 88(18): 9276–9284 [70] Chen H J C, Ip S W, Lin F D. Chem. Res. Toxicol., 2017, 30(11): 2074–2083 [71] Chen H J C, Chen Y C. Anal. Chem., 2012, 84(18): 7881–7890 [72] Geyer Philipp E, Kulak Nils A, Pichler G, Holdt Lesca M, Teupser D, Mann M. Cell Syst., 2016, 2(3): 185–195 [73] Lin L, Yu Q, Zheng J, Cai Z, Tian R. Clin. Proteom., 2018, 15(1): 42 [74] Humphrey S J, Karayel O, James D E, Mann M. Nat. Protoc., 2018, 13(9): 1897–1916 [75] Meier F, Geyer P E, Virreira Winter S, Cox J, Mann M. Nat. Methods, 2018, 15(6): 440–448
Cite this article as: Chinese J. Anal. Chem., 2019, 47(11): 1732–1741
REVIEW
Application of Mass Spectrometry in Analysis of Non-Enzymatic Glycation Proteins in Diabetic Blood LI Wei-Feng1,2, YAN De-Wen2, JIN Yu2, LI Hai-Yan2, MA Min1,*, WU Zheng-Zhi1,2,3,* 1
Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou 510632, China The First Affiliated Hospital of Shenzhen University, Shenzhen 518020, China 3 Shenzhen Institute of Gerontology, Shenzhen 518020, China 2
Abstract:
Diabetes mellitus (DM) is a metabolic disorder disease characterized by hyperglycemia. Long-term physiological
hyperglycemia will aggravate the degree of non-enzymatic glycation (NEG) proteins. Because the abnormal changes of non-enzymatic process can cause a series of pathological variation, NEG proteins have attracted more and more attention. Mass spectrometry (MS) is an indispensable and powerful analytical tool for the qualitative and quantitative analysis of proteins due to its ultra-high sensitivity, excellent detection limits, and multi-component simultaneous analysis, and has been applied to the identification and qualification of NEG proteins. Herein, the principle of NEG proteins and the application of MS in the identification and quantitation of NEG plasma proteins and hemoglobin (Hb) in blood are reviewed. The forecast in the analysis of NEG proteins based on MS is also provided. Key Words:
Mass spectrometry; Non-enzymatic glycation; Proteins; Diabetes mellitus; Review
1 Introduction Non-enzymatic glycation (NEG) is the reaction between carbonyl groups of reducing sugars and α-amino group of ε-side chain or N-terminal amino acid residues of proteins without any enzyme. Reversible aldehyde imines (Schiff base) are firstly generated and then go through a rearrangement to form stable Amadori products. Subsequently, Amadori products are affected by oxidation, degradation, crosslinking or other reactions, forming variously heterogeneous products, namely advanced glycation end products (AGEs)[1]. The detailed process is shown in Fig.1. The formed AGEs will accumulate in cells, interact with receptors in body, and cause tissue damage, leading to the development of diabetes mellitus (DM). Hence, it is clinically significant to study the distribution, cycles, and metabolites of AGEs, affording to understand the mechanism of DM at the micro-level and screen new biomarkers[2]. There are several methods for determination of global
AGEs levels. The methods based on spectrometry can supply real-time monitoring of reaction according to the color changes but with no structure information[3]. High performance liquid chromatography (HPLC) has been employed for the determination of AGEs due to its high accuracy and sensitivity. However, this method lacks specificity[4]. The high-specific performance of immunoassaysbased methods has made them become important candidates for determination of NEG proteins. But problem exists in terms of rigorous sample preparation, long time needed for preparing antibody, and interferences from complex samples, constraining their application in the universal detection of AGEs, especially for the unknown ones. With the advancement of mass spectrometry (MS), proteomics based on MS has become a powerful tool for the identification and quantitation of proteins and their post-translational modifications (PTMs). As AGEs are prevalent in diabetes, particularly for type II diabetes because of its high percent in DM (> 90%)[5], this paper mainly reviews the qualitative and quantitative analysis
________________________ Received 8 May 2019; accepted 8 July 2019 *Corresponding author. Email: [email protected], [email protected] This work was supported by the Sanming Project of Medicine in Shenzhen, China (No. SZSM201612049), the Natural Science Foundation of China (No. 81574038), and the Shenzhen Discipline Layout Project of China (No. JCYJ20170412161254416). Copyright © 2019, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences. Published by Elsevier Limited. All rights reserved. DOI: 10.1016/S1872-2040(19)61197-7
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Fig.1 General patterns of chemistry involved in the Maillard reaction
of NEG proteins in type II DM using MS, and provides some perspective for the future study.
2 Biological principles of NEG proteins Generally, glycation study is based on glucose because it is the major nutrient in our body. It has to be noted that the NEG is not limited to glucose. Ribose, galactose and intermediates of glucose metabolism such as methylglyoxal (MG) and glycolic aldehyde are also involved in the reaction. Studies have shown that NEG can influence the structure and property of proteins[6]. For example, space conformation of myoglobin is changed by glycation, leading to the variation in biological functions, such as oxygen release, peroxidase and hydrolytic enzyme activity[7]. Compared to glyoxal and glyceraldehyde modification, human serum albumin (HSA) with MG modification shows the most obvious change in spatial structures, indicating that proteins with different modifications exhibit varieties in spatial structure[8]. According to the existing clinical study, NEG proteins have been proved to be closely related to the occurrence and development of various diseases, including DM, kidney disease, and Alzheimer’s
Fig.2
disease[8,9]. Therefore, comprehensive study of NEG proteins is crucial for the in-depth understanding of these diseases. As various kinds of compounds are involved in the protein glycation, the distinct characteristic and difficulty in the analysis of AGEs are their inherently spatial heterogeneities. Even to single protein with modification based on glucose, diverse spatial structures are produced because multiple sites in protein can be attacked. Generally, amino acids with lower pH and strong affinity are believed to promote their glycation. Meanwhile, the glycated sites are affected by their surrounding environment. The presence of positive charged amino acids such as histidine, lysine, and acidic amino acids at adjacent positions (sequence or spatial proximity) will facilitate the glycation[10,11]. For example, glycation of Val-1 of β-Hb is higher than that of α-Hb mainly because of the presence of His-2. Moreover, Wang et al[12] reported that the phosphate triangle constructed by the histidine and lysine in HGKK peptides of α-chain and β-chain of Hb provides the proton transfer pathway for rearrangement, promoting their glycation. As shown in Fig.2, similar structure of HGKK can be obtained in α-chain and β-chain of Hb. Of note, only the β-Lys-66 and α-Lys-61 with adjacent His in HGKK are glycated according to the MS results. Hence, it can be inferred that NEG proteins contain rich biological information. Shedding light on relationship among the protein structure, property, function, and biological significance after glycation might provide new clues for the mechanism of the occurrence and development of DM.
3 Analysis of NEG proteins using MS As the abnormal expression of NEG proteins results in a series of disease, it is of significance to illuminate the bio-information carried by NEG proteins. The rapid development in software and hardware of MS, advancement in chromatographic separation technology, and emergence of diverse enrichment methods for specific PTMs have laid solid foundation for the determination of PTMs using MS. The application of MS in the quantitative analysis of NEG proteins has obtained some progress. The involved MS techniques and their characteristics are shown in Table 1.
Molecular model of HGKK consensus on (A) β-Lys-66 and (B) α-Lys-61 site of hemoglobin[12]
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Table 1 Ionization types Matrix assisted laser desorption/ionization (MALDI)
Sample states
Summary of mass spectrometry for analysis of NEG proteins Classification Non-label
Solid /liquid Label Non-label Bottom-up Label
Electrospray ionization (ESI)
Liquid Top-down/middle down
3.1 Application of MALDI-TOF MS in analysis of NEG proteins Matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) has played an important role in the qualitative analysis of compounds with integrated advantages such as simple mass spectra, highautomation, and high throughput. Routine workflow for protein analysis based on MALDI is shown in Fig.3. Generally, the targeted protein or its digests at concentration level of µM are mixed (1:1–1:10, V/V) with matrix such as sinapic acid (SA) or α-cyanide-4-hydroxy-cinnamic acid (HCCA). Then the mixture is transferred to the sample target and dried to form crystal for subsequent MS analysis. Hitherto, MALDI-TOF MS has been applied in the analysis of various NEG proteins, including Hb and plasma proteins, showing potential value in clinical diagnosis. 3.1.1
Characteristics Suitable for analysis at protein/peptide level, simple sample preparation, high-throughput, rapid analysis speed, poor stability Suitable for peptide level analysis with high-throughput, quantitative analysis of glycated peptides, complex sample preparation
Application of MALDI-TOF MS in analysis of NEG proteins in plasma
The first application of MALDI-TOF MS for NEG proteins analysis were dated back to 1990s. Lapolla et al[13] and Jørgensen et al[14] studied the m/z of bovine serum albumin as function of incubation time and glucose level. It was shown that high glucose concentration in DM significantly accelerated the plasma proteins glycation, such as HSA. Subsequently, glycation level of HSA was employed to reflect
Suitable for peptide level analysis, qualitative and quantitative analysis for unknown glycated protein, lacking protein spatial information Suitable for peptide level analysis, excellent quantitative performance, no protein spatial information, complex sample preparation Suitable for protein/large peptides level analysis, simple sample pretreatment, providing protein spatial information, protein variant information, challenge exists in protein separation and purification, relying on high-resolution mass spectrometer
the glucose level in the previous 2–3 weeks, which was in favour of designing short-term glucose controlled protocol. With visual reading of HSA m/z and its glycation states by MALDI-TOF MS, blood glucose level could be easily and quickly obtained. Meanwhile, MALDI-TOF MS could offer glycation level in immunoglobulins (Igs) and show the binding glucose number with the tendency as poorly glucose controlled group > blood glucose under controlled group > healthy group[15]. Moreover, fragments of antigen binding in papain digests (Fab) and crystallization section could be obtained by MALDI-TOF MS. The MS results showed that glucose was inclined to combine with Fab fragments, revealing the reason for immune deficiency in DM at the molecular level[16]. As a powerful analytical technique, MALDI-TOF MS provides the global glycation level of proteins but without PTMs[17]. The introduction of isotope labeling technology combined with digested strategy can effectively bypass the above problems. With the integrated advantage of 16O and 18O labeling technology and MALDI-TOF MS, quantitative analysis of glycation HSA peptides have been achieved as shown in Fig.4[18–21]. But this method is only suitable for vitro study. Using the characteristic of the 1 Da mass difference of the same peptide under the reduction of NaBH4 and NaBD4, qualitative and quantified analysis of glycation peptides were fulfilled in vivo[22]. However, the mass deviation of 1 Da is still interfered by the isotope peaks of the analyzed peptides, which limits its further application. As shown in Fig.5, the combination of agent (NaBH3CN, NaBD3CN) for reducing protein with isotopic dimethylation agent (CH2O, CD2O) during protein digesting can lead to the mass difference at 4m + 3n, where m indicates the number of N-terminal and lysine residues, while n represents the number of glycated sites. Using the newly developed protocol, quantitative analysis of NEG proteins in plasma are acquired[23]. 3.1.2
Fig.3
Workflow of routine MALDI for protein/peptide analysis
Application of MALDI-TOF MS in analysis of glycated Hb
Compared with glycated HSA, glycated hemoglobin (GHb)
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Fig.4
Workflow of analysis of glycated sites of HSA using stable isotope labeling with MALDI-TOF MS: (A) 16O-labeled HSA, 18O-labeled glycated HSA, and the mixed digests of HSA and glycated HSA with ratio of 50:50 (V/V), (B) mass spectra of 16O, 18O and mixed oxygen labeled peptides[18]
Fig.5
Workflow of reductive amination combining dimethylation[23]
has longer half-life cycle. The GHb concentration is not affected by sports or diets but is related to the red blood cell life and the average blood glucose level. As GHb can clinically represent the average blood glucose concentration in the previous 2–3 months, it has been employed as the gold standard for assessing the blood glucose status in DM [24]. Since then, MALDI-TOF MS has been used for the determination of GHb. Although the early GHb results of MALDI-TOF MS are different from those of the conventional methods based on liquid chromatography or immunoassay, a conclusion is drawn that NEG can occur in both the Hb chains[25–27]. MALDI-TOF MS has advantages in terms of simple spectra
and good qualitative performance. However, the Gaussian distribution of laser energy and heterogeneous crystallization of the samples makes it difficult to obtain desirably quantitative results. Increasing the laser frequency following the optimized matrix crystallization conditions can effectively compensate for the signal fluctuation caused by those defects[28]. In the case of optimizing the crystallization of SA matrix and Hb, the quantitation of glycated and glutathionylated Hb were achieved by keeping the total number of ions consistent for each sample[29]. This new strategy can offer precise coefficient of variation (CVs) in the intraday and interday which are within the international federation of clinical chemistry (IFCC) recommendation
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limits. Benefiting from the improved sensitivity of the MS instrument, elevated sampling speed (laser frequency increases from 10 Hz to 1 kHz), and increasing speed in data analysis, high precision and accuracy were gained for the determination of GHb[30]. Using high laser frequency, averaging all the spectra for each single spot could greatly increase the accuracy (CVs ≤ 2.5%). The obtained β-Hb glycation results were comparable to those obtained by the clinically validated HPLC. Of note, only 0.5 nL of blood was needed for GHb analysis. To fully take advantage of high-throughput and rapid analysis ability of MALDI-TOF MS, Li et al[31] developed a laser-assisted proteolysis which had the capability for accelerating proteolysis and enhancing N-termini analysis. This facilitated to the quantitative analysis of GHb peptides in 2000 samples within 8 h. It was noteworthy that all the steps including the sample loading, proteolysis, laser irradiation, and detection were completed in the same sample target, avoiding the cumbersome sample pretreatment in traditional methods. With the continuous increase of MS hardware and the improvement of laser frequency, application of MALDI-TOF MS for NEG proteins analysis will be further broadened. 3.2
Application of ESI-MS in analysis of NEG proteins
MALDI-TOF MS has strengths in aspects of the qualitative and quantitative analysis of NEG proteins in high-throughput way, but the preparation method of solid sample is not suitable for the analysis of all proteins. Liquid injection of electrospray mass spectrometry (ESI-MS) can not only realize the qualitative and quantitative analysis of proteins, but also reflect proteins in their inherent states[32,33]. As the inlet of ESI is compatible with the detector of chromatography, it is particularly suitable for analyzing the complex samples. Therefore, ESI-MS/MS has been widely used for the AGEs analysis in blood[34]. Figure 6 shows a regular identification process for protein modification sites. As shown in Fig.6, cells or tissues were broken to extract protein firstly. Then, enrichment was employed for the specific protein followed by hydrolysis. The desalted peptides were detected by MS for obtaining full scan or MS/MS spectra. Finally, the raw data were searched against the Uniprot fasta database, realizing the identification of proteins and PTMs. 3.2.1
Application of ESI-MS/MS in analysis of NEGs in plasma
Lapolla et al[34] found that LC-MS/MS could offer information that the glycated reaction would reduce the hydrolysis efficiency for HSA. Meanwhile, five sites including Lys-233, Lys-276, Lys-378, Lys-545 and Lys-525 were found to be sensitive to the reducing sugars, which were in good agreement with the theoretical results. Although the
Fig.6
Workflow of identification of protein modification sites using ESI-MS/MS
peptides with modification could be directly analyzed by LC-MS/MS, the lower glycated sites might be missing. The enrichment for glycation peptides by boronic acid affinity chromatography could elevate the identification of glycated sites[35,36]. By combining this method with the electron transfer dissociation (ETD), Zhang et al[37] found that 76 plasma proteins and 31 red cell membrane proteins were simultaneously glycated. More importantly, the number of NEG proteins and glycated sites in DM were higher than those of the controls. Using immune-depletion, enrichment, and fractionation strategies, totally 7749 unique glycated peptides corresponding to 3742 unique NEG proteins were identified[38]. The development of isotope labeling technique has become a new path for proteomics study. By the use of high-energy collisional dissociation (HCD) and collision-induced dissociation (CID) technique, Priego-Capote et al[39,40] identified 161 glycation sites corresponding to 50 plasma proteins. The quantitation of glycation peptides was achieved by calculating the ratio of intensity of peptides treated by 13C6 and 12C6 labelled glucose, respectively. The detailed process is shown in Fig.7. Due to the characteristic of selective glycation in HSA, the glucose-sensitive peptides and glucose-insensitive peptides were discovered by isotope labeling MS. Hence, Zhang et al[41] developed a standard-free, label-free MS method for the determination of glycated peptides by employing glucose-insensitive peptides as internal standards, promoting to screening 8 potential markers for the early diagnosis of diabetes. The introduction of new ion dissociation improves the performance of protein identification. The optimizing HCD mode, developed by Frolov et al[42], was proven to be helpful for the discovery of new modification glycation sites. Besides, 101 early glycation sites as well as numerous AGEs sites on diverse plasma proteins in 1 µL of undepleted and unenriched blood were identified with good reproducibility. In addition to ion dissociation mode, the MS acquisition mode also affects the protein identification. Hoffmann et al[43] developed a gas phase fractionation approach which fulfilled the qualitative and quantitative determination of human AGEs. With the
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Fig.7
Workflows for quantitative analysis of NEG proteins labeling with 12C and 13C glucose[39]
newly developed method, the glycation degree of 19 sites was found to be increased in DM compared to the controls. With the utilization of multiple reaction monitoring (MRM), Sandro Spiller screened six glycated peptides from glycation HSA in DM with significant higher levels than in the non-diabetic men. Moreover, the combination of glycated Lys-141 of haptoglobin, fasting plasma glucose, and HbA1c could provide a sensitivity of 94%, specificity of 98% for newly diagnosed DM[44–46]. As has been shown that glycation such as Nε-(carboxyethyl)-(CEL)- and Nε-(carboxymethyl) lysine (CML) were involved in cell adhesion, signaling pathways, and angiogenesis in human body[47]. However, the level of these modifications in plasma was too low to detect. Therefore vitro incubation was indispensable[48]. By use of the characteristic fragmentation of CEL and CML modified peptides, Graifenhagen et al[49] identified 21 CML and CEL modifications corresponding to 17 proteins in human plasma. With the rapid development in the fields such as computer science and semiconductor, on-line automatic processing method was produced and applied to the analysis of NEG proteins. Zhang et al[50] developed a method for online
separation and analysis of glycated peptides in plasma by two-dimensional LC-MS/MS. The detection limit of the method was down to 1.2 × 10–12 g with relative deviation of less than 20%. The number of glycated peptides obtained by the two-dimensional LC-MS/MS was three times that of onedimensional LC-MS/MS. Jeona et al[51] developed a highthroughput protein pretreatment system which integrated steps such as protein reduction, alkylation, hydrolysis, and enrichment of glycation peptide in a 96-well plate. This new system had good reproducibility and high-throughput, which was of great significance for the label-free quantitative analysis of proteins. In addition, with the continuous upgrading of MS hardware and software, various new acquisition modes have emerged, including parallel reaction monitoring (PRM), and data-independent acquisition/ sequential window acquisition of all theoretical fragment ions (DIA/SWATH). Korwar et al[52] used PRM, SWATH and MSE to study the distribution of HSA glycated peptides in DM with different pathological conditions. It was of interest to find that similar results were obtained using the three strategies, which indicated their feasibility in screening
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potential biomarkers for the diagnosis of diabetes. Compared with data-dependent acquisition (DDA), DIA/SWATH can obtain the fragmentation for all proteins in samples without any targeted peptides, offering performance with good reproducibility and high throughput. It is noteworthy that DDA is the most effective method for identifying both the known and unknown proteins. Therefore, DDA is always adopted for the establishment of peptide spectrum library to take full advantage of DIA. Bruderer et al[53] used DDA to establish the library of plasma proteins in the obesity and diabetes. Based on the DDA database, good reproducibility and stable quantification of 565 plasma proteins in 1508 clinical samples were achieved by DIA. For the first time, 234 glycation sites were determined without the enrichment of glycated peptides. According to the clinical data, it was found that the glycation level of protein was increased first and then decreased between the periods of losing weight stage and maintaining weight stage. Although bottom-up proteomics aforesaid was the dominant methods for NEG proteins analysis, the steps such as denaturation, reduction, alkylation, and digestion were necessarily needed. However, these pretreatment would result in failure to reconstruct the proteoforms presented in a given sample, making it difficult to study the linkages among different PTMs. The development of middle-down and top-down proteomics strategy can alleviate the aforesaid limitations[54]. Different from the enzymes used in the bottom-up proteomics, the enzymes employed in middle-down proteomics produced larger peptides, promoting to a wider application[55]. For top-down proteomics, the intact proteins were directly analyzed without any enzyme digestion, providing more accurate and ample biological information[56–58]. Because a variety of species were involved in NEG, AGEs exhibited heterogeneities. The utilization of middle-down and top-down techniques can effectively identify the proteoforms in AGEs[59]. Liu et al[60] found that middle-down proteomics could not only offer the rapid identification of proteins containing FC fragments, but also effectively improve the identification of various PTMs in heterogeneous proteins, such as glycosylation, glycation, and oxidation. More importantly, it could be used to obtain the dynamic changes of glycation during the development and application of clinical antibody drugs. As has been known that human apolipoprotein A-I (ApoA-I), a mediator of high-density-lipoprotein cholesterol efflux (HDL-E), is inversely associated with coronary heart disease risk. But proteoforms of ApoA-I, and the relationship between each proteoform and the level of HDL-E remain to be studied. Seckler et al[61] studied the ApoA-I using top-down proteomics and found that ApoA-I contained 18 proteoforms and 11 PTMs, including glycosylation, phosphorylation, carboxymethyl, and palmitoylation. Six proteoforms were showed significantly (p < 0.0005) higher intensity in high
HDL-E individuals. 3.2.2
Application of ESI-MS/MS in analysis of glycated Hb
In 2002, LC-ESI/MS was recognized as the standard method for the determination of GHb as approved by IFCC Working Group. The peptide VHLTPE at the N-terminal of β-Hb chain was first isolated by the HPLC and then determined by ESI-MS[62]. Priego-Capote et al[63] provided the quantitative information of glycated human hemolysate associated to the glucose level by glycation isotopic labeling. The results showed that the glycation level of erythrocyte proteins exhibited positively correlation with the GHb value[63]. Wang et al[12] used trypsin, chymotrypsin and endoproteinase Glu-C to fully identify the glycation sites in Hb. By utilizing the complementary of the three enzymes, in-depth glycation sites were obtained. Each glycated site exhibited significant difference in normal and diabetes. As the main component of red blood cells, Hb is affected not only by glucose, but also by other metabolites in vivo and vitro. Therefore, it is clinically significant to obtain other PTMs to further understand the mechanism of diabetes. For example, the concentration of MG modified Hb of diabetes is higher than those of the controls[64,65]. It is reported that the increased MG in human always causes insulin resistance and typical metabolic disorder in diabetes, indicating that MG might be the reason for diabetes and its complications[66]. In addition, Kulkarni et al[67] targeted the PTMs (glycation, CEL, and CML) in diabetes with different phenotypes using PRM. They found that the combination of the three glycation modifications could effectively reflect the status of diabetes. Using the Nano-LC-MS/MS system, Chen et al[68–71] studied various PTMs in Hb in diabetes with smoking or non-smoking. The MS results showed that the influence of smoking on diabetes could be reflected by PTMs in Hb, providing theoretical foundation for studying on the correlation between smoking and DM.
4
Conclusion and perspective
Nowadays, mass spectrometry has been widely used in the analysis AGEs. Global glycation level of proteins could be achieved by MALDI-TOF MS, while multiple PTMs in proteins can simultaneously be obtained via bottom-up proteomic. However, the dynamic glycation and variable abundance of the various PTMs pose a great challenge for analyzing both the high and low abundance PTMs simultaneously. In addition, bottom-up proteomics provide the protein information at peptide level, losing the attributive information between peptides and proteins, which makes it difficult to analyze the interaction between different PTMs. Given the advantages of middle-down and top-down
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proteomics, it is a tendency to study NEG proteins for gaining biological information including structure and function. It is still a problem for the quantitation of proteins with high speed, good stability, and reproducibility. The integrated sample preparation technology combined with DIA can offer more accurate and stable protein quantitative information with the good reproducibility and high throughput. The newly developed BoxCar strategy allows the signal-to-noise ratio of low abundance peptides to increase with an order of magnitude in the case of no separation enrichment. These results can be obtained by dividing the precursor window into multiple narrow mass-to-charge segments, improving ion injection time, and averaging signal for both the high abundance and low abundance peptides, which is another tendency for the quantitation of NEG proteins. These new strategies will not only meet the needs of automated analysis in clinical field, but also will be in line with the developing needs in the medical big data era[72–75].
[15] Liu Z,Schey K L. J. Am. Soc. Mass Spectrom., 2005, 16(4): 482–490 [16] Lapolla A, Fedele D, Garbeglio M, Martano L, Tonani R, Seraglia R, Favretto D, Fedrigo M A, Traldi P. J. Am. Soc. Mass Spectrom., 2000, 11(2): 153–159 [17] Lapolla A, Brancia F L, Bereszczak J, Fedele D, Baccarin L, Seraglia R, Traldi P. Mol. Nutr. Food Res., 2007, 51(4): 456–461 [18] Barnaby O S, Cerny R L, Clarke W, Hage D S. Clin. Chim. Acta, 2011, 412(17): 1606–1615 [19] Barnaby O S, Cerny R L, Clarke W, Hage D S. Clin. Chim. Acta, 2011, 412(3): 277–285 [20] Wa C, Cerny R L, Clarke W A, Hage D S. Clin. Chim. Acta, 2007, 385(1): 48–60 [21] Barnaby O S, Wa C, Cerny R L, Clarke W, Hage D S. Clin. Chim. Acta, 2010, 411(15): 1102–1110 [22] Liu H, Ponniah G, Neill A, Patel R, Andrien B. J. Chromatogr. B, 2014, 958: 90–95 [23] Tong Q-H, Yan T Y, Tao T, Zhang L, Xie L Q, Lu H J. Anal.
References
Chem., 2018, 90(6): 3752–3758 [24] Lipska K J, Krumholz H M. JAMA, 2017, 317(10): 1017–1018
[1]
Lapolla A, Fedele D, Traldi P. Mass Spectrom. Rev., 2000, 19(5): 279–304
[2]
Lapolla A, Fedele D, Traldi P. Diabetes-Metab. Res., 2001, 17(2): 99–112
[3]
Gusev S I, Borovkova M A, Strepitov M A, Khodzitsky M K. Chinese Optics, 2018, 11(2): 182–189
[4]
Johnson R N, Metcalf P A, Baker J R. Clin. Chim. Acta, 1983, 127(1): 87–95
[5]
Association A D. Diabetes Care, 2014, 37(Supplement 1): S81–S90
[6]
[27] Lapolla A, Tubaro M, Fedele D, Reitano R, AricòN C, Ragazzi E, Seraglia R, Vogliardi S, Traldi P. Rapid Commun. Mass Spectrom., 2005, 19(2): 162–168 [28] Duncan M W, Nedelkov D, Walsh R, Hattan S J. Clin. Chem., 2016, 62(1): 134 [29] Biroccio A, Urbani A, Massoud R, di Ilio C, Sacchetta P, Bernardini S, Cortese C, Federici G. Anal. Biochem., 2005, 336(2): 279–288
You Y, Liu F, Lin Y W, Du K J, Liu J H, Wen G B. Chem. Life, 2013, 33(06): 627–632 Khan M S, Tabrez S, Rabbani N, Shah A. J. Fluoresc., 2015, 25(6): 1721–1726
[9]
Chem., 1995, 67(6): 1034–1041
Ragone R, Zambruno G, Carbone V, Ribatti D, Peschle C, 2806–2818
[8]
9(1): 1–7 [26] Gusev A I, Wilkinson W R, Proctor A, Hercules D M. Anal.
Facchiano F, D’Arcangelo D, Russo K, Fogliano V, Mennella C, Capogrossi M C, Facchiano A. Mol. Endocrinol., 2006, 20(11):
[7]
[25] Niu S, Zhang W, Chait B T. J. Am. Soc. Mass Spectrom., 1998,
Shuvaev V V, Laffont I, Serot J-M, Fujii J, Taniguchi N, Siest G. Neurobiol. Aging., 2001, 22(3): 397–402
[30] Hattan S J, Parker K C, Vestal M L, Yang J Y, Herold D A, Duncan M W. J. Am. Soc. Mass Spectrom., 2016, 27(3): 532–541 [31] Li L, Zang W, Zhang X. Anal. BioanaI. Chem., 2016, 408(5): 1507–1513 [32] Zehl M,Allmaier G. Rapid Commun. Mass Spectrom., 2004, 18(17): 1932–1938 [33] Nakanishi T, Miyazaki A, Kishikawa M, Yasuda M, Tokuchi Y,
[10] Acosta J, Hettinga J, Flückiger R, Krumrei N, Goldfine A,
Kanada Y, Shimizu A. J. Mass Spectrom., 1997, 32(7): 773–778
Angarita L, Halperin J. P. Natl. Acad. Sci. USA, 2000, 97(10):
[34] Lapolla A, Fedele D, Reitano R, AricòN C, Seraglia R, Traldi P,
5450 [11] Johansen M B, Kiemer L, Brunak S. Glycobiology, 2006, 16(9): 844–853 [12] Wang S H, Wang T F, Wu C H, Chen S H. J. Am. Soc. Mass Spectrom., 2014, 25(5): 758–766 [13] Lapolla A, Gerhardinger C, Baldo L, Fedele D, Keane A, Seraglia R, Catinella S, Traldi P. BBA-Mol. Basis Dis., 1993, 1225(1): 33–38 [14] Jørgensen T J D, Bojesen G, Rahbek-Nielsen H. Eur. Mass Spectrom., 1998, 4(1): 39–45
Marotta E, Tonani R. J. Am. Soc. Mass Spectrom., 2004, 15(4): 496–509 [35] Zhang Q, Tang N, Brock J W C, Mottaz H M, Ames J M, Baynes J W, Smith R D, Metz T O. J. Proteome Res., 2007, 6(6): 2323–2330 [36] Frolov A, Hoffmann R. Ann. N.Y. Acad. Sci., 2008, 1126(1): 253–256 [37] Zhang Q, Tang N, Schepmoes A A, Phillips L S, Smith R D, Metz T O. J. Proteome Res., 2008, 7(5): 2025–2032 [38] Zhang Q, Monroe M E, Schepmoes A A, Clauss T R W,
LI Wei-Feng et al. / Chinese Journal of Analytical Chemistry, 2019, 47(11): 1732–1741
Gritsenko M A, Meng D, Petyuk V A, Smith R D, Metz T O. J. Proteome Res., 2011, 10(7): 3076–3088 [39] Priego-Capote F, Scherl A, Müller M, Waridel P, Lisacek F, Sanchez J C. Mol. Cell. Proteomics, 2010, 9(3): 579–592 [40] Priego-Capote F, Ramírez-Boo M, Finamore F, Gluck F, Sanchez J C. J. Proteome Res., 2014, 13(2): 336–347 [41] Zhang M, Xu W, Deng Y L. Diabetes, 2013, 62(11): 3936 [42] Keilhauer E C, Geyer P E, Mann M. J. Proteome Res., 2016, 15(8): 2881–2890 [43] Frolov A, Blüher M, Hoffmann R. Anal. Bioanal. Chem., 2014, 406(24): 5755–5763 [44] Spiller S, Frolov A, Hoffmann R. Protein Pept. Lett., 2017, 24(10): 887–896 [45] Spiller S, Li Y, Blüher M, Welch L, Hoffmann R. Clin. Proteom., 2017, 14(1): 10 [46] Spiller S, Li Y, Blüher M, Welch L, Hoffmann R. Pharmaceuticals, 2018, 11(2): 38, doi: 10.3390/ph11020038 [47] Bollineni R C, Fedorova M, Blüher M, Hoffmann R. J. Proteome Res., 2014, 13(11): 5081–5093 [48] Ahmed N, Thornalley P J. Ann. N.Y. Acad. Sci., 2005, 1043(1): 260–266 [49] Greifenhagen U, Nguyen V D, Moschner J, Giannis A, Frolov A, Hoffmann R. J. Proteome Res., 2015, 14(2): 768–777 [50] Zhang L, Liu C W, Zhang Q. Anal. Chem., 2018, 90(2): 1081–1086 [51] Jeon J, Yang J, Park J M, Han N Y, Lee Y B, Lee H. J. Chromatogr. B, 2018, 1092: 88–94 [52] Korwar A M, Vannuruswamy G, Jagadeeshaprasad M G, Jayaramaiah R H, Bhat S, Regin B S, Ramaswamy S, Giri A P, Mohan V, Balasubramanyam M, Kulkarni M J. Mol. Cell Proteomics, 2015, 14(8): 2150–2159 [53] Bruderer R, Muntel J, Müller S, Bernhardt O M, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris W H M, Hager J, Valsesia A, Dayon L, Reiter L. Mol. Cell Proteomics, 2019, 18(6): 1242 [54] Sun R X, Luo L, Chi H, Liu C, He S M. Prog. Biochem. Biophys., 2015, 42(02): 101–114 [55] Fornelli L, Ayoub D, Aizikov K, Beck A, Tsybin Y O. Anal. Chem., 2014, 86(6): 3005–3012 [56] Li H, Nguyen H H, Ogorzalek Loo R R, Campuzano I D G, Loo J A. Nat. Chem., 2018, 10: 139–148 [57] Li H, Sheng Y, McGee W, Cammarata M, Holden D, Loo J A. Anal. Chem., 2017, 89(5): 2731–2738
[58] Li H, Wolff J J, van Orden S L, Loo J A. Anal. Chem., 2014, 86(1): 317–320 [59] Yang Y, Wang G, Song T, Lebrilla C B, Heck A J R. mAbs, 2017, 9(4): 638–645 [60] Liu T, Guo H, Zhu L, Zheng Y, Xu J, Guo Q, Zhang D, Qian W, Dai J, Guo Y, Hou S, Wang H. Chromatographia, 2016, 79(21): 1491–1505 [61] Seckler H d S, Fornelli L, Mutharasan R K, Thaxton C S, Fellers R, Daviglus M, Sniderman A, Rader D, Kelleher N L, Lloyd-Jones D M, Compton P D, Wilkins J T. J. Proteome Res., 2018, 17(6): 2156–2164 [62] Jeppsson J O, Kobold U, Barr J, Finke A, Hoelzel W, Hoshino T, Miedema K, Mosca A, Mauri P, Paroni R, Thienpont L, Umemoto M, Weykamp C. Clin. Chem. Lab. Med., 2002, 40(1): 78–79 [63] Priego-Capote F, Ramirez-Boo M, Hoogland C, Scherl A, Mueller M, Lisacek F, Sanchez J C. Anal. Chem., 2011, 83(14): 5673–5680 [64] Shimada S, Tanaka Y, Ohmura C, Tamura Y, Shimizu T, Uchino H, Watada H, Hirose T, Nakaniwa T, Miwa S, Kawamori R. Diabetes Res. Clin. PR, 2005, 69(3): 272–278 [65] Chen H J C, Chen Y C, Hsiao C F, Chen P F. Chem. Res. Toxicol., 2015, 28(12): 2377–2389 [66] Moraru A, Wiederstein J, Pfaff D, Fleming T, Miller A K, Nawroth P, Teleman A A. Cell Metab., 2018, 27(4): 1–9 [67] Jagadeeshaprasad M G, Batkulwar K B, Meshram N N, Tiwari S, Korwar A M, Unnikrishnan A G, Kulkarni M J. Clin. Proteom., 2016, 13(1): 7 [68] Chen H J C, Lin W P, Chiu S D, Fan C H. Chem. Res. Toxicol., 2014, 27(5): 864–872 [69] Chen H J C, Yang Y F, Lai P Y, Chen P F. Anal. Chem., 2016, 88(18): 9276–9284 [70] Chen H J C, Ip S W, Lin F D. Chem. Res. Toxicol., 2017, 30(11): 2074–2083 [71] Chen H J C, Chen Y C. Anal. Chem., 2012, 84(18): 7881–7890 [72] Geyer Philipp E, Kulak Nils A, Pichler G, Holdt Lesca M, Teupser D, Mann M. Cell Syst., 2016, 2(3): 185–195 [73] Lin L, Yu Q, Zheng J, Cai Z, Tian R. Clin. Proteom., 2018, 15(1): 42 [74] Humphrey S J, Karayel O, James D E, Mann M. Nat. Protoc., 2018, 13(9): 1897–1916 [75] Meier F, Geyer P E, Virreira Winter S, Cox J, Mann M. Nat. Methods, 2018, 15(6): 440–448