张丽翠^,, 马川, 冯毛, 李建科^,中国农业科学院蜜蜂研究所,北京 100093

Evaluation and Optimization of Metabolite Extraction Protocols for Royal Jelly by High Resolution Mass Spectrometry and Metabolomics

ZHANG LiCui^,, MA Chuan, FENG Mao, LI JianKe^,Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093

通讯作者: 李建科,Tel：010-82106448;E-mail： apislijk@126.com

责任编辑: 岳梅
收稿日期:2020-01-18接受日期:2020-02-25网络出版日期:2020-09-16

基金资助:

国家现代农业产业技术体系.蜜蜂CARS-44 中国农业科学院科技创新工程.CAAS-ASTIP-2015-IAR

Received:2020-01-18Accepted:2020-02-25Online:2020-09-16
作者简介 About authors
张丽翠,E-mail： 604011320@qq.com。






摘要
【目的】蜂王浆是具有医疗保健功效的天然产品,含有丰富的小分子活性成分,目前蜂王浆代谢物的提取尚缺乏系统研究。通过蜂王浆代谢轮廓分析,比较不同溶剂对蜂王浆小分子化合物的提取效果,优化蜂王浆代谢物提取方法,鉴定蜂王浆中的代谢物。【方法】分别使用6种溶剂（50%和80%的甲醇、乙醇和乙腈）提取蜂王浆代谢物,运用反相液相色谱（reverse phase liquid chromatography,RPLC）和亲水相互作用色谱（hydrophilic interaction liquid chromatography,HILIC）分别联合四级杆-静电场轨道阱高分辨质谱技术（Q-exactive orbitrap HRMS）进行检测。比较不同溶剂组的代谢特征离子数量和相对标准偏差（relative standard deviation,RSD）,并进行主成分分析（principal component analysis,PCA）。利用质谱数据库定性代谢物,并经标准品验证,比较其RSD差异,进行聚类热图（clustering heatmap）分析。通过正交偏最小二乘判别分析（orthogonal partial least squares-discriminant analysis,OPLS-DA）、单变量统计分析（student’s t-test）和倍数变化（fold change,FC）筛选溶剂组间具有显著差异的代谢物。【结果】强极性化合物,包括葡萄糖与果糖等同分异构体,在HILIC条件下分离良好,而脂类等中低极性化合物在RPLC条件下得到较好的分离。两种色谱分离方法的应用实现了对不同极性代谢物的检测,在蜂王浆中共鉴定到70种高丰度化合物,涵盖了糖、氨基酸、脂类、维生素等,其丰度差异高达8 340倍,其中有17种化合物为本研究首次报道。乙腈溶剂得到的代谢特征离子数量最少,80%乙腈比50%乙腈的提取效果更差;对甲醇和乙醇而言,高浓度时的代谢特征离子数量更多。所有溶剂组得到的RSD值集中分布在20%范围内,但80%乙腈组在10%内的占比最低,已鉴定的70种代谢物的RSD值进一步证明80%乙腈的重复性较差。PCA结果表明,来自同一提取溶剂的蜂王浆代谢谱高度相似,不同溶剂提取的样品间存在差异,其中,80%乙腈组与其他5组差异最大。聚类热图等分析结果表明,中低极性物质在50%溶剂组丰度较低,强极性物质特别是果糖、葡萄糖、蔗糖、赖氨酸、腺苷、胆碱、磷酸胆碱和葡萄糖酸在80%乙腈组丰度最低。【结论】RPLC和HILIC分别联合高分辨质谱技术能够较全面准确地检测蜂王浆中的小分子化合物,80%甲醇或80%乙醇是提取蜂王浆代谢物的最佳溶剂。
关键词： 蜂王浆;代谢组学;样品提取;高分辨质谱

Abstract
【Objective】Royal jelly, a natural product with health-promoting effect, is rich in small-molecule bioactive compounds. The systematic analysis of the metabolite extraction from royal jelly is still lacking. This study performed metabolic profiling of royal jelly with an aim to compare the metabolite extraction efficiency using different solvents, optimize the metabolite extraction protocol, and globally identify the small-molecule compounds in royal jelly. 【Method】Metabolites in royal jelly were extracted separately using six different solvents including 50% and 80% methanol, ethanol, and acetonitrile. They were analyzed by reverse phase liquid chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) each combined with Quadrupole-Exactive Orbitrap high resolution mass spectrometry (HRMS). The amount and relative standard deviation (RSD) values of metabolite features among the solvent groups were compared. Principal component analysis (PCA) was conducted on the basis of these metabolite features. The compound identification was based on mass spectrometry databases and was validated with standards. Both the RSD value comparison and clustering heatmap analysis were performed for the identified compounds. Significantly differential metabolites among the solvent groups were screened out by orthogonal partial least squares-discriminant analysis (OPLS-DA), univariate analysis (Student’s t-test) and fold change (FC). 【Result】Highly polar metabolites, including two isomeric sugars (glucose and fructose), and medium and weakly polar metabolites such as lipids were sufficiently separated using the HILIC and PRLC approach, respectively. The combination of the two separation methods enabled comprehensive detection of metabolites with different polarity in royal jelly. A total of 70 high-abundance compounds were identified, including carbohydrates, amino acids, lipids, and vitamins, with the highest difference of 8 340 times in abundance. Among them, 17 compounds were reported for the first time. The lowest number of metabolite features was obtained with acetonitrile, and a worse coverage was obtained with 80% than 50% acetonitrile. For methanol and ethanol, the number of metabolite features was higher at higher solvent concentrations. The RSD values of most metabolite features were lower than 20% in all solvent groups, and the 80% acetonitrile group had the lowest proportion within 10% RSD values compared with other solvents. The RSD values of the 70 identified compounds provided further evidence for the poor repeatability of 80% acetonitrile. The PCA score plots indicated a similar metabolic profiling from the same solvent. Differences in metabolic profiling were observed among the solvents, and the largest difference existed between 80% acetonitrile and the five other solvents. The clustering heatmap showed a lower abundance of medium and weakly polar metabolites obtained from 50% solvents and a lower abundance of highly polar metabolites including, in particular, fructose, glucose, sucrose, lysine, adenosine, choline, phosphorylcholine, and gluconic acid from 80% acetonitrile. 【Conclusion】The combination of RPLC-HRMS and HILIC-HRMS enables comprehensive and accurate detection of small-molecule compounds in royal jelly. 80% methanol or 80% ethanol is an optimal solvent for metabolite extraction from royal jelly.
Keywords：royal jelly;metabolomics;sample extraction;high resolution mass spectrometry (HRMS)


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本文引用格式
张丽翠, 马川, 冯毛, 李建科. 基于高分辨质谱和代谢组学技术评估和优化蜂王浆代谢物提取方法[J]. 中国农业科学, 2020, 53(18): 3833-3845 doi:10.3864/j.issn.0578-1752.2020.18.017
ZHANG LiCui, MA Chuan, FENG Mao, LI JianKe. Evaluation and Optimization of Metabolite Extraction Protocols for Royal Jelly by High Resolution Mass Spectrometry and Metabolomics[J]. Scientia Acricultura Sinica, 2020, 53(18): 3833-3845 doi:10.3864/j.issn.0578-1752.2020.18.017

0 引言

【研究意义】蜂王浆是由工蜂上颚腺和咽下腺等腺体共同分泌的乳白色或淡黄色的浆状物质^[1],是工蜂用来饲喂1—3日龄蜜蜂幼虫和饲喂蜂王终生的食物。蜂王浆是一种对人类健康具有重要作用的天然功能食品,具有调节血压、增强免疫力、抗菌消炎、抗肿瘤等药理功效^[2,3],其营养价值越来越受到人们的关注。蜂王浆成分复杂,主要包括水分、蛋白质、脂类、氨基酸、糖类、维生素等^[2,4],其丰富的生物活性成分是蜂王浆具有医疗保健功效的物质基础。代谢组学是对生物体、器官、组织或细胞中的代谢物进行全面系统鉴定和分析的技术^[5],该技术在食品领域的应用,可对食品中的小分子组分进行整体定性和定量分析,为食品质量安全提供技术支撑^[6]。代谢物的提取是代谢组学研究中至关重要的步骤^[7,8],直接影响了可检测的代谢物范围^[9]以及代谢物提取的数量^[10]。因此,建立简单易行且高效的蜂王浆代谢物的提取方法,获得更加全面的代谢物种类和数量,对蜂王浆质量评价和功能开发利用具有重要意义。【前人研究进展】目前对蜂王浆小分子化合物的鉴定分析已有大量报道,ISIDOROV等^[11]检测了蜂王浆中的挥发性成分和有机溶剂萃取成分,PINA、VIRGILIOU等^[12,13]靶向测定了蜂王浆中的亲水性化合物,但大多数研究只针对蜂王浆中的某一类化合物,如氨基酸^[14,15,16]、糖^[16,17]、有机酸^[18]、维生素^[19,20]及核酸^[21,22]。从分析方法来看,以上研究主要基于气相色谱-质谱联用技术（GC-MS）、气相色谱火焰-离子化检测器（GC-FID）、高效液相色谱-荧光检测技术（HPLC-FLD）等。气相色谱相关技术检测范围小,只能检测挥发性化合物和衍生化后形成的挥发性化合物^[23]。超高效液相色谱-高分辨质谱技术（UPLC-HRMS）具有更高的分辨率和灵敏度,通过选择不同的分离色谱柱可以实现从非极性到极性代谢物的全面检测,其中反相液相色谱（reverse phase liquid chromatography,RPLC）能够很好地分离非极性和中低极性化合物,但对强极性化合物的保留和分离能力较弱,而亲水相互作用色谱（hydrophilic interaction liquid chromatography,HILIC）对强极性化合物有较好的分离和保留,具有与RPLC互补的选择性^{[24,25,26,27]}。因此,RPLC和HILIC相结合可以扩大代谢物的分离检测范围,与质谱联用后可获得更全面的代谢物种类及含量的信息。但UPLC-HRMS技术在蜂王浆代谢物研究中的应用非常有限,而且只进行了靶向分析^[12,13],非靶向的RPLC-HRMS和HILIC-HRMS以及二者同时应用到蜂王浆中的研究还未有报道。代谢组学研究中,溶剂提取法是广泛使用的代谢物提取方法,目前常用的样品提取溶剂有甲醇、乙醇、乙腈以及与水组成的混合溶剂提取体系,不同溶剂对代谢物的提取会产生不同的影响^[28,29,30]。蜂王浆代谢成分复杂,化学性质和丰度差异大,提取溶剂的选择至关重要,目前常用的提取溶剂有90%乙醇^[14]、50%甲醇^[12,13]、100%甲醇^[16]及50%乙腈^[31]等,但缺乏系统的比较研究。【本研究切入点】分别基于RPLC和HILIC联合Q-Exactive Orbitrap HRMS以及代谢组学分析技术对蜂王浆中的小分子化合物进行全面分析,通过多元统计分析比较不同浓度（50%和80%）的甲醇、乙醇和乙腈对蜂王浆代谢物的提取效果,优化蜂王浆代谢物的提取方法。【拟解决的关键问题】建立并优化蜂王浆小分子化合物成分的高效提取方法,对蜂王浆中的小分子化合物进行全面解析,为后续蜂王浆代谢组学研究提供技术支持。

1 材料与方法

试验于2019年在中国农业科学院蜜蜂研究所完成。

1.1 化学试剂

质谱级乙腈（A955-4）、质谱级甲醇（A456-4）、质谱级乙醇（24102）、色谱级甲酸（50144）和甲酸铵（A115-50）购自Fisher Scientific,超纯水由Milli-Q纯水机制备,化合物标准品购自Sigma。

1.2 仪器设备

质谱仪（Q-Exactive）,美国Thermo Fisher Scientific公司;超高效液相色谱系统（Ultimate 3000）,美国Thermo Fisher Scientific公司;台式低温离心机（Microfuge 22R Centrifuge）,美国BeckMAN CoulTER公司;电子分析天平（PL203,0.1 mg）,德国METTLER TOLEDO公司;超低温冰箱（MDF-U3286S）,日本SANYO公司;非接触超声波细胞粉碎机（Scientz08- III）,宁波SCIENTZ公司;快速低温冷却循环机（DLK-2007）,宁波SCIENTZ公司。

1.3 样品采集

以饲养于中国农业科学院蜜蜂研究所养蜂基地的蜂王浆高产蜜蜂作为试验蜂群,蜂王购买于浙江省。参考常用的蜂王浆生产方法^[32,33,34]获得蜂王浆样品：准备并清理含有63个塑料王台的产浆条,移取1日龄小幼虫至王台中,将该产浆条固定在产浆框后放至所选取的蜂群继箱中,72 h后取出产浆框,割除蜡盖并移走王台中的幼虫后,收集蜂王浆于1.5 mL离心管,保存于-80℃冰箱中备用。

1.4 代谢物提取

利用6种不同有机溶剂（50%和80%甲醇、50%和80%乙醇、50%和80%乙腈）分别提取蜂王浆中的代谢物,每种提取方法6个平行样。将冻存的蜂王浆样品置于冰上充分解冻,均质化后,准确称取0.1 g至4 mL棕色进样瓶中,加入4 mL溶剂,超声40 min,使样品充分溶解,取1.4 mL样品混合液转移到1.5 mL离心管中,4℃条件下12 000 r/min离心20 min,取上清液用0.22 μm有机相滤膜过滤,收集到2 mL棕色进样瓶中供UPLC-MS分析。每个样品分别取30 μL,混合到2 mL棕色进样瓶中,作为质量控制（QC）样品。空白对照只加入6种有机溶剂的混合液,其他操作流程与样品处理完全一致。

1.5 UPLC-MS分析

RPLC方法：ZORBAX SB-Aq C18反相色谱柱（Agilent,100 mm×2.1 mm,1.8 μm）;进样量3 μL;柱温40℃;流动相A为0.1%甲酸-水溶液,流动相B为0.1%甲酸-乙腈;流速0.3 mL·min^-1。梯度洗脱：0—2 min,95%—70% A;2—8 min,70%—15% A;8—9 min,15%—15% A;9—9.5 min,15%—95% A;9.5—13 min,95%—95% A。

HILIC方法：ACQUITY BEH Amide色谱柱（Waters,150 mm×2.1 mm,1.7 μm）;进样量3 μL;柱温50℃;流动相A为30%乙腈溶液（含0.1%甲酸和10 mmol甲酸铵）,流动相B为95%乙腈溶液（含0.1%甲酸和10 mmol甲酸铵）;流速0.3 mL·min^-1。梯度洗脱：0—2 min,0—0 A;2—8 min,0—80% A;8—9 min,80%—80% A;9—9.5 min,80%—0 A;9.5—13.5 min,0—0 A。

质谱条件：采用HESI离子源,在正、负离子切换模式下采集,参数设置如下：离子源温度320℃,喷雾电压3.5 kV（ESI+）和3.0 kV（ESI-）,鞘气流速40 arb,辅助气流速5 arb,S-lens射频电压60%,母离子扫描分辨率70 000,扫描范围70—1 000 m/z,自动增益控制目标离子数1×10⁶,最大离子注入时间50 ms,扫描速率1 scan/s。为了鉴定化合物,需要进行数据依赖型二级扫描,参数设置如下：扫描分辨率17 500,自动增益控制目标离子数1×10⁵,最大离子注入时间50 ms,TopN为10,信号强度阈值2×10⁶,动态排除8 s,顶点激发2—7 s,归一化碰撞能量（NCE）为15%、40%和60%。先进10针QC样品,以平衡系统,此数据不用于后续分析。所有的蜂王浆样品顺序随机打乱,以减小系统误差。为了检测系统的稳定性及校正数据,每9个样品之间进1针QC样品。通过Xcalibur软件（Thermo Fisher Scientific）收集MS和MS/MS数据并保存为Raw格式文件。

1.6 数据处理与分析

将Raw文件导入Compound Discoverer 2.1软件（Thermo Fisher Scientific）进行离子峰识别、峰对齐、峰面积归一化处理及数据库搜索。参数设置如下：最大允许偏移时间0.2 min,质量允许偏差5 ppm,信噪比阈值3,峰响应强度最小值1×10⁶,样品与空白比值5,基于QC峰面积进行校正,QC覆盖范围>50%且QC峰面积相对标准偏差<30%。根据精准质量数及MS/MS质谱图的离子碎片信息,使用HMDB（http://www.hmdb.ca/）、mzCloud（https://www.mzcloud.org/）、ChemSpider（http://www.chemspider.com/）、LIPID MAPS（http://lipidmaps.org/）和KEGG（http://www.genome.jp/kegg/）数据库进行化合物鉴定,确定可能的化合物,购买相应的标准品并进行二级质谱分析,进一步验证。采用SIMCA 14.0软件,进行多变量统计分析：经Pareto-scaling处理和log转换后进行PCA分析,可直观上反应各组样本的空间分布,从总体上反映样本之间的代谢谱差异;经Pareto- scaling处理后,采用正交偏最小二乘判别分析（orthogonal partial least squares-discriminant analysis,OPLS-DA）,在建模过程中对模型数据进行置换检验并计算变量投影重要度（variable importance in the projection,VIP）。VIP>1.0且单变量统计分析（Student’s t-test）中P<0.05和倍数变化（fold change,FC）>1.2的代谢物作为具有显著差异的代谢物。使用Cluster和Treeview软件进行已定性化合物的聚类热图分析。计算代谢特征离子和已定性化合物在同一溶剂6个技术重复的峰面积的RSD,以检测不同溶剂提取过程的稳定性。

2 结果

2.1 分析系统的稳定性

将RPLC和HILIC条件下QC样品的总离子流图分别进行叠加,结果显示色谱峰的保留时间及信号响应强度高度重叠（图1）。在RPLC和HILIC条件下检测到的代谢特征离子的数量分别是955和1 113个,基于这些代谢特征离子的PCA分析表明,所有QC样品聚集紧密（图2）。

图1

新窗口打开|下载原图ZIP|生成PPT
图1QC样品的总离子流叠加图

Fig. 1Overlapping of total ion chromatography (TIC) of QCs

图2

新窗口打开|下载原图ZIP|生成PPT
图2基于代谢特征离子的主成分分析得分图

Fig. 2The PCA score plots based on metabolite features

2.2 化合物鉴定

通过数据库代谢物谱图信息比对及标准品验证,在RPLC和HILIC条件下分别鉴定到50和51种高丰度化合物,两者共有的化合物有31种。脂类等中低极性化合物,包括两组同分异构体（3-羟基癸酸与10-羟基癸酸,11-羟基十二烷酸与12-羟基十二烷酸）,在RPLC条件下得到较好的分离;而氨基酸、糖类等强极性化合物,包括葡萄糖与果糖等同分异构体,在HILIC条件下分离良好。在蜂王浆样品中共鉴定到70种化合物（53种经过标准品验证）,涵盖了糖类、氨基酸、脂类、维生素等,其丰度差异高达8 340倍,其中有17种化合物为本研究首次报道（表1）。

Table 1
表1
表1在蜂王浆中鉴定到的化合物
Table 1Tentatively identified compounds in royal jelly

编号 No.	化合物 Compound	分子式 Molecular formula	保留时间Retention time (min)	分子量测量值Measured mass	分子量理论值Theoretical mass	质量偏差 Mass error (×10-6)	匹配度得分Match score	平均峰面积 Average peak area	色谱柱 Column
1	马尿酸Hippuric acidab	C9H9NO3	3.34	179.05835	179.05824	0.61	<50.0	1.93E+05	RPLC
2	咖啡碱Caffeic acida	C9H8O4	3.65	180.04238	180.04226	0.67	85.0	3.28E+05	RPLC
3	3,10-二羟基癸酸3,10-Dihydroxydecanoic acid	C10H20O4	3.94	204.13618	204.13616	0.10	<50.0	2.24E+08	RPLC
4	辛二酸Suberic acida	C8H14O4	3.95	174.08918	174.08921	-0.17	97.1	1.03E+06	RPLC
5	8-羟基辛酸8-Hydroxyoctanoic acida	C8H16O3	4.05	160.10992	160.10994	-0.12	<50.0	2.26E+06	RPLC
6	壬酸Nonanoic acid	C9H18O2	4.67	158.13068	158.13068	0.00	88.1	4.79E+06	RPLC
7	2-癸二酸2-Decenedioic acid	C10H16O4	4.80	200.10483	200.10486	-0.15	97.0	2.42E+08	RPLC
8	癸二酸Decenedioic acida	C10H18O4	4.95	202.12048	202.12051	-0.15	85.2	3.83E+08	RPLC
9	10-羟基二癸烯酸10-Hydroxy-2-decenoic acida	C10H18O3	4.96	186.12558	186.12559	-0.05	97.0	5.35E+07	RPLC
10	10-羟基癸酸10-Hydroxydecanoic acida	C10H20O3	5.12	188.14120	188.14124	-0.21	89.0	1.90E+07	RPLC
11	十四烷二酸Tetradecanedioic acidb	C14H26O4	5.43	258.18337	258.18311	1.01	52.0	9.89E+05	RPLC
12	愈伤酸Traumatic acida	C12H20O4	5.72	228.13631	228.13616	0.66	84.7	2.22E+07	RPLC
13	3-羟基癸酸3-Hydroxydecanoic acida	C10H20O3	5.74	188.14121	188.14124	-0.16	89.7	8.69E+07	RPLC
14	十二烷二酸Dodecanedioic acida	C12H22O4	5.85	230.15179	230.15181	-0.09	87.2	3.73E+07	RPLC
15	脂肪酸（12:1）FA (12:1)	C12H22O2	5.91	198.16194	198.16198	-0.20	50.8	1.37E+07	RPLC
16	11-羟基十二烷酸 11-Hydroxydodecanoic acida	C12H24O3	5.92	216.17257	216.17254	0.14	78.3	1.24E+06	RPLC
17	12-羟基十二烷酸 12-Hydroxydodecanoic acida	C12H24O3	6.05	216.17256	216.17254	0.09	66.8	1.48E+06	RPLC
18	柯因Chrysina	C15H10O4	6.51	254.05777	254.05791	-0.55	97.1	2.12E+06	RPLC
19	脂肪酸（14:2）FA (14:2)	C14H24O2	6.61	224.17759	224.17763	-0.18	89.4	2.39E+06	RPLC
20	13-羟基十四烷酸13-Hydroxytetradecanoic acid	C14H28O3	6.74	244.20393	244.20384	0.37	95.2	7.39E+06	RPLC
21	脂肪酸（16:1）FA (16:1)	C16H30O2	7.48	254.22445	254.22458	-0.51	95.2	5.32E+05	RPLC
22	烟酰胺Nicotinamidea	C6H6N2O	1.92	122.04801	122.04801	0.00	81.7	1.34E+06	HILIC
23	吡哆醛Pyridoxal	C8H9NO3	2.25	167.05826	167.05824	0.12	88.5	3.04E+06	HILIC
24	泛酸Pantothenic acida	C9H17NO5	3.26	219.11061	219.11067	-0.27	80.7	5.03E+07	HILIC
25	乙酰胆碱Acetylcholinea	C7H15NO2	3.28	145.11018	145.11028	-0.69	95.8	1.61E+09	HILIC
26	琥珀酸Succinic acida	C4H6O4	3.39	118.02665	118.02661	0.34	83.7	4.34E+07	HILIC
27	烟酸Nicotinic acida	C6H5NO2	3.98	123.03209	123.03203	0.49	64.7	8.27E+05	HILIC
28	2′-脱氧腺苷2′-Deoxyadenosineab	C10H13N5O3	4.01	251.10167	251.10184	-0.68	<50.0	9.55E+05	HILIC
29	腺嘌呤Adeninea	C5H5N5	4.87	135.05452	135.05450	0.15	90.8	2.03E+08	HILIC
30	尿苷Uridinea	C9H12N2O6	5.42	244.06970	244.06954	0.66	84.7	2.53E+07	HILIC
31	腺苷Adenosinea	C10H13N5O4	5.54	267.09670	267.09675	-0.19	97.4	2.74E+08	HILIC
32	肌酐Creatininea	C4H7N3O	5.58	113.05896	113.05891	0.44	<50.0	4.75E+05	HILIC
33	胞嘧啶Cytosinea	C4H5N3O	5.94	111.04327	111.04326	0.09	95.5	7.44E+05	HILIC
34	胆碱Cholinea	C5H13NO	6.09	103.09963	103.09971	-0.78	91.3	2.32E+08	HILIC
35	溶血磷脂酰胆碱（18:3）LysoPC (18:3)b	C26H48NO7P	6.32	517.31677	517.31684	-0.14	78.6	2.04E+06	HILIC
36	N,N-二乙基乙醇胺N,N-Diethylethanolamineb	C6H15NO	6.33	117.11532	117.11536	-0.34	75.7	4.21E+07	HILIC
37	N-乙酰基组胺N-Acetylhistamineab	C7H11N3O	6.60	153.09019	153.09021	-0.13	91.4	4.34E+06	HILIC
38	肌苷Inosinea	C10H12N4O5	6.76	268.08081	268.08077	0.15	90.0	5.05E+06	HILIC
39	次黄嘌呤Hypoxanthinea	C5H4N4O	6.76	136.03854	136.03851	0.22	92.8	4.34E+06	HILIC
40	鸟嘌呤Guaninea	C5H5N5O	6.93	151.04944	151.04941	0.20	93.0	2.70E+05	HILIC
41	甜菜碱Betainea	C5H11NO2	7.26	117.07893	117.07898	-0.43	96.0	2.18E+07	HILIC
42	鸟苷Guanosinea	C10H13N5O5	7.27	283.09147	283.09167	-0.71	98.9	1.32E+06	HILIC
43	果糖Fructosea	C6H12O6	7.35	180.06350	180.06339	0.61	92.4	4.07E+08	HILIC
44	胡芦巴碱Trigonellinea	C7H7NO2	7.39	137.04766	137.04768	-0.15	93.8	3.81E+07	HILIC
45	胆碱Prolinea	C5H9NO2	7.61	115.06330	115.06333	-0.26	97.4	6.82E+07	HILIC
46	肉碱Carnitineab	C7H15NO3	7.66	161.10514	161.10519	-0.31	82.4	1.26E+06	HILIC
47	γ-氨基丁酸γ-Aminobutyric acida	C4H9NO2	7.66	103.06332	103.06333	-0.10	92.1	2.88E+06	HILIC
48	牛磺酸Taurinea	C2H7NO3S	7.66	125.01462	125.01466	-0.32	82.2	1.53E+06	HILIC
49	葡萄糖Glucosea	C6H12O6	7.67	180.06344	180.06339	0.28	97.8	1.30E+08	HILIC
50	吡哆胺Pyridoxamine	C8H12N2O2	7.81	168.08986	168.08988	-0.12	87.4	4.25E+06	HILIC
51	β-丙氨酸β-Alaninea	C3H7NO2	7.88	89.04772	89.04768	0.45	88.9	5.48E+06	HILIC
52	苏氨酸Threonic acida	C4H8O5	7.99	136.03725	136.03717	0.59	55.0	7.53E+05	HILIC
53	蔗糖Sucrosea	C12H22O11	8.08	342.11652	342.11621	0.91	93.6	1.71E+08	HILIC
54	葡萄糖酸Gluconic acida	C6H12O7	8.24	196.05822	196.05830	-0.41	95.9	4.08E+08	HILIC
55	谷氨酸Glutamic acida	C5H9NO4	8.26	147.05315	147.05316	-0.07	95.2	7.14E+06	HILIC
56	甘油3-磷酸乙醇胺 Glycerol 3-phosphoethanolamineb	C5H14NO6P	8.29	215.05582	215.05587	-0.23	<50.0	2.47E+06	HILIC
57	N3,N4-二甲基-L-精氨酸N3,N4-Dimethyl-L-arginineb	C8H18N4O2	8.37	202.14296	202.14298	-0.10	90.5	8.42E+06	HILIC
58	5′-单磷酸腺苷 Adenosine 5′-monophosphatea	C10H14N5O7P	8.45	347.06309	347.06308	0.03	84.3	1.36E+07	HILIC
59	尿苷一磷酸Uridine monophosphateab	C9H13N2O9P	8.52	324.03609	324.03587	0.68	86.2	8.36E+05	HILIC
60	N6,N6,N6-三甲基-L-赖氨酸N6,N6,N6-Trimethyl-L-lysineb	C9H20N2O2	8.53	188.15221	188.15248	-1.44	56.5	5.95E+06	HILIC
61	天冬氨酸Aspartic acida	C4H7NO4	8.54	133.03753	133.03751	0.15	90.5	1.72E+06	HILIC
62	N6-甲基-L-赖氨酸N6-Methyl-L-lysineb	C7H16N2O2	8.59	160.12116	160.12118	-0.12	87.6	1.44E+06	HILIC
63	1-甲基组氨酸1-Methylhistidineab	C7H11N3O2	8.71	169.08506	169.08513	-0.41	91.2	2.11E+06	HILIC
64	组氨酸Histidinea	C6H9N3O2	8.73	155.06952	155.06948	0.26	98.3	1.36E+07	HILIC
65	赖氨酸Lysinea	C6H14N2O2	8.81	146.10548	146.10553	-0.34	90.2	7.32E+07	HILIC
66	鸟氨酸Ornithinea	C5H12N2O2	8.86	132.08986	132.08988	-0.15	92.8	8.47E+05	HILIC
67	鸟苷单磷酸Guanosine monophosphateab	C10H14N5O8P	8.87	363.05817	363.05800	0.47	78.1	2.64E+06	HILIC
68	烟酰胺腺嘌呤二核苷酸 Nicotinamide adenine dinucleotideab	C21H27N7O14P2	8.94	663.10950	663.10912	0.57	94.2	4.80E+05	HILIC
69	UDP-N-乙酰氨基葡萄糖UDP-N-Acetylglucosamineb	C17H27N3O17P2	9.08	607.08223	607.08157	1.09	91.0	1.29E+06	HILIC
70	磷酸胆碱Phosphorylcholineab	C5H14NO4P	9.26	183.06600	183.06604	-0.22	97.5	7.18E+07	HILIC

^a：经过标准品验证Verified by standards;^b：本研究首次鉴定First identified in this study

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2.3 不同溶剂的提取效果

首先,比较了不同溶剂提取蜂王浆得到的代谢特征离子的数量和RSD值。使用50%和80%乙腈、50%和80%乙醇、50%和80%甲醇,在RPLC条件下分别鉴定到817、808、878、889、879和932个代谢特征离子,在HILIC条件下分别鉴定到799、752、857、869、805和865个代谢特征离子。可以看出,利用乙腈溶剂得到的代谢特征离子数量最少,与50%乙腈相比,80%乙腈提取效果更差。对甲醇和乙醇而言,高浓度时的代谢特征离子数量更多,浓度变化对甲醇的影响更大。所有溶剂组的特征离子RSD值具有相似的分布模式,集中分布在20%范围内,但80%乙腈组在10%内的占比最低（图3）。

图3

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图3代谢特征离子的相对标准偏差

Fig. 3The relative standard deviation of metabolite features



其次,通过计算70种代谢物峰面积的RSD值,进一步检测溶剂提取过程的稳定性（图4）。80%乙腈组的RSD值主要集中在5%—10%范围内,其次为0—5%,有11种代谢物的RSD值>15%。其他5种溶剂组的RSD值分布模式相似,主要集中在0—5%和5%—10%范围内。

图4

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图4已鉴定化合物的相对标准偏差

Fig. 4The relative standard deviation of identified compounds

2.4 主成分分析

基于代谢特征离子进行PCA分析,分别考察在RPLC和HILIC条件下不同溶剂对蜂王浆代谢物的提取效果。在RPLC条件下第1和第2主成分分别解释了总变异的49.9%和13.8%,在HILIC条件下第1和第2主成分分别为37.0%和20.9%。根据每个样本在PCA得分图上的分布规律可以发现,来自同一提取溶剂的样品分布比较集中,不同溶剂提取的样品间存在差异,其中,80%乙腈组与其他5组差异最大,甲醇和乙醇的浓度变化对蜂王浆代谢物提取的影响较小（图2）。

2.5 差异代谢物的筛选

为了筛查在PCA分析中将80%乙腈组和其他5组明显区分开的主要化合物,对鉴定到的70种化合物进行OPLS-DA分析。OPLS-DA分析能够过滤掉无关组分,凸显组间差异。该模型包含3个主成分,其拟合参数为R²X=0.913,R²Y=0.983,Q²=0.977,即用91.3%的变量解释了98.3%的组间差异,预测能力为97.7%,表明该模型的可靠性和预测率较高。为了避免有监督模型在建模过程中发生拟合,采用置换检验对模型进行检验,参数为R²=0.194,Q²=-0.447,说明此模型未发生过拟合,结果可靠（图5-A）。得到相应的S-plot图（图5-B）,一个点代表一个变量,越是在图的两端代表该化合物在该组的含量越高。VIP用来衡量各代谢物的表达模式对各组样本分类判别的影响强度和解释能力,以及辅助标志代谢物的筛选,VIP值>1.0的变量被认为是对分类具有重要意义的变量。将VIP>1.0且单变量统计分析P<0.05和FC>1.2的代谢物作为具有显著差异的代谢物,共筛选出8种差异化合物（赖氨酸、磷酸胆碱、葡萄糖、蔗糖、果糖、腺苷、葡萄糖酸和胆碱）,其含量在80%乙腈组显著低于其他5组（图5-B）。

图5

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图580%乙腈组与其他5组间差异代谢物

A：正交偏最小二乘判别分析模型过拟合验证Overfitting validation of OPLS-DA model。B：S-plot图S-plot figure。赖氨酸：Lysine;磷酸胆碱：Phosphorylcholine;葡萄糖：Glucose;蔗糖：Sucrose;果糖：Fructose;腺苷：Adenosine;葡萄糖酸：Gluconic acid;胆碱：Choline
Fig. 5Differential metabolites between the 80% acetonitrile and the five other solvents

2.6 代谢物热图分析

聚类热图分析结果表明,已鉴定的70种化合物总体上分为两个分支。分支1包括22种化合物,主要为中低极性物质,在50%溶剂组丰度较低。分支2主要为强极性物质,在80%乙腈组丰度最低,在其他5组的差异不明显（图6）。

图6

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图6已鉴定化合物的聚类热图

Fig. 6The clustering heatmap of identified compounds

3 讨论

本研究分别基于RPLC和HILIC联合高分辨质谱技术对蜂王浆小分子化合物进行代谢轮廓分析,基于代谢组学方法评估和优化蜂王浆代谢物的提取方法。在蜂王浆样品中共鉴定了70种高丰度化合物,发现80%甲醇和80%乙醇具有更高的提取效率,80%乙腈提取蜂王浆代谢物的重复性差且强极性物质的丰度最低。

3.1 数据质量评估

仪器分析系统的稳定性是获得稳定重现的代谢组学数据的前提,是代谢组学研究成功的关键,QC样品在实验分析中的重复性是评价系统稳定性的常用指标之一^[35]。本研究将蜂王浆提取物等体积混合后作为QC样品,采用与实际样品同样的进样方法,每分析9个样品穿插1个QC样品。由总离子流图的叠加图（图1）可知,QC样品的峰形重现性良好,表明仪器带来的偏差很小。在PCA得分图（图2）中,QC样品紧密聚集,进一步表明分析系统具有较好的稳定性和重复性。

3.2 蜂王浆小分子化合物鉴定

代谢组学的研究对象是生物样品中的代谢物,如氨基酸、糖、核苷酸、脂类等极性及非极性化合物^[36]。UPLC-HRMS是代谢组学分析的有力平台,高效的色谱分离能有效区分同分异构体,减少共流出化合物导致的离子抑制效应,从而有助于增加质谱检测化合物的种类和数量,并提高测定结果的准确度^[23,37]。目前常用的分离色谱柱主要有RPLC和HILIC色谱柱,前者对于非极性和中低极性化合物的分离发挥重要作用,对强极性化合物的分离和保留能力较弱^[38],而HILIC色谱柱在分离强极性化合物方面表现出更大的优势^[24]。

本研究比较了RPLC和HILIC两种色谱柱在分析蜂王浆代谢物种类上的互补性。在RPLC和HILIC条件下分别鉴定了50和51种高丰度代谢物,共有代谢物有31种。脂类等中低极性化合物,包括两组同分异构体（3-羟基癸酸与10-羟基癸酸,11-羟基十二烷酸与12-羟基十二烷酸）,在RPLC条件下得到较好的分离;而氨基酸、糖类等强极性化合物,包括葡萄糖与果糖等同分异构体,在HILIC条件下分离良好。以上结果证明了两种色谱柱的互补性,以及在蜂王浆代谢组学研究中的必要性。在蜂王浆样品中共鉴定了70种化合物（表1）,其中大部分化合物已在蜂王浆中报道^[11,12],17种化合物为本研究首次发现。受二级质谱数据库谱图数量的限制,本研究中还有大量代谢特征离子未能定性。此外,蜂王浆成分受蜂种、地理环境、气候条件、天气因素、蜜粉源状况、蜂群饲养管理以及生产方式等多种因素的影响^[39,40]。因此,完善二级质谱数据库,增加蜂王浆的种类,使用以上代谢组学方法,将鉴定出更多的蜂王浆小分子化合物。

3.3 不同溶剂对代谢物提取的影响

代谢物的提取是代谢组学的核心组成部分,合适的提取方法是获得较高提取效率的保证,不同的提取溶剂适用于不同的代谢产物,因此提取溶剂的选择至关重要。蜂王浆中所含代谢物种类繁多,化学性质差异较大。本研究以两种不同浓度的乙腈、乙醇和甲醇为研究对象,在基于UPLC-HRMS代谢组学分析中表明,80%乙腈对蜂王浆强极性化合物（特别是赖氨酸等8种化合物）的提取效率最低（图5、图6）,且重复性较差（图3、图4）。PINA等^[12]在提取蜂王浆极性代谢物时发现,与50%甲醇相比,80%乙腈的提取效率低,与本研究结果一致。在其他生物样品的代谢组学研究中,也有类似报道。例如,通过比较不同溶剂（甲醇、乙醇、乙腈及其水溶液）对白芦笋代谢物的提取效果,发现随着乙腈比例的增加,代谢特征离子数目及峰面积明显减少^[41];在血清代谢组学研究中,利用乙腈提取检测到的代谢特征离子数目最少,而且RSD值最大^[42]。本研究发现,与50%甲醇和50%乙醇相比,80%甲醇和80%乙醇提取的蜂王浆代谢特征离子数量更多,中低极性物质的丰度更高（图6）。因此,80%甲醇和80%乙醇是蜂王浆代谢组学研究的最佳提取溶剂。以往研究表明,80%乙醇能有效沉淀蜂王浆中的蛋白,并用于提取蜂王浆中的腺苷^[43]。80%甲醇和80%乙醇也广泛用于其他生物样品的非靶向代谢组学研究^{[44,45,46,47,48,49]}。

4 结论

建立了基于RPLC和HILIC四级杆-静电场轨道阱高分辨串联质谱技术的蜂王浆代谢轮廓分析方法,共鉴定了70种高丰度化合物,实现了糖、脂类、维生素和氨基酸等小分子化合物的检测,发现80%甲醇或80%乙醇是提取蜂王浆代谢物的最佳溶剂,为后续蜂王浆代谢组学研究提供了技术支持。

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DOI:10.1021/pr300700eURLPMID:23157659 [本文引用: 1]
To identify candidate royal jelly (RJ) proteins that might affect the physiologic status of honeybee colony members, we used shotgun proteomics to comprehensively identify the RJ proteome as well as proteomes of the hypopharyngeal gland (HpG), postcerebral gland (PcG), and thoracic gland (TG), from which RJ proteins are assumed to be derived. We identified a total of 38 nonredundant RJ proteins, including 22 putative secretory proteins and Insulin-like growth factor-binding protein complex acid labile subunit. Among them, 9 proteins were newly identified from RJ. Comparison of the RJ proteome with the HpG, PcG, and TG proteomes revealed that 17 of the 22 putative secretory RJ proteins were derived from some of these glands, suggesting that the RJ proteome is a cocktail of proteins from these three glands. Furthermore, pathway analysis suggested that the HpG proteome represents the molecular basis of the extremely high protein-synthesizing ability, whereas the PcG proteome suggests that the PcG functions as a reservoir for the volatile compounds and a primer pheromone. Finally, to further characterize the possible total RJ proteome, we identified putative secretory proteins in the proteomes of these three glands. This will be useful for predicting novel RJ protein components in future studies.

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Royal jelly (RJ) is an important functional food item that possess several health promoting properties. It has been widely used in commercial medical products, healthy foods and cosmetics in many countries. RJ has been demonstrated to possess numerous functional properties such as antibacterial activity, anti-inflammatory activity, vasodilative and hypotensive activities, disinfectant action, antioxidant activity, antihypercholesterolemic activity and antitumor activity. Biological activities of RJ are mainly attributed to the bioactive fatty acids, proteins and phenolic compounds. In consideration of potential utilisation, detailed knowledge on the composition of RJ is of major importance. The diversity of applications to which RJ can be put gives this novel food great industrial importance. This review summarises the composition, nutritional value and functional properties of RJ. (C) 2011 Elsevier Ltd.

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Abstract

A rapid ultra-performance liquid chromatography (UPLC) method was developed for feasible separation and quantification of 26 amino acids in royal jelly. The analysis was performed on Acquity UPLC system with Acquity UPLC AccQ·Tag Ultra Column within 8 min. The correlation coefficient values (r² > 0.9978) indicated good correlations between the investigated compounds’ concentrations and their peak areas within the test ranges. The limits of quantitation and detection of 26 amino acids were 42.7–235.1 ng/mL and 12.9–69.3 ng/mL, respectively. The recoveries ranged from 90.1% to 100.9% and the overall relative standard deviations for intra- and inter-day were lower than 2.8%. The results showed that UPLC was a powerful tool for the analysis of amino acids in royal jelly. The method was also applied to quantitatively determine free amino acid (FAA) and total amino acid (TAA) profiles in RJ samples stored at different temperatures (−18 °C, 4 °C and 25 °C) for different time intervals (1, 3, 6 and 10 months). Results showed that the average contents of FAA and TAA in fresh royal jelly were 9.21 mg/g and 111.27 mg/g, respectively; the major FAAs were Pro, Gln, Lys, Glu, and the most abundant TAAs were Asp, Glu, Lys and Leu. Although the concentration of most FAAs and TAAs showed no significant difference during storage, contents of total Met and free Gln decreased significantly and continuously, and might be a parameter to predict the quality of royal jelly.

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Ethnopharmacological relevance: Royal Jelly (RJ) is a bee-derived product that has been traditionally used in the European and Asian systems of medicine for longevity. RJ has various pharmacological activities that may prevent aging e.g., anti-inflammatory, anti-oxidative, anti-hypercholesterolemic and anti-hyperglycemic properties.
Aim of the study: To evaluate the behavioral and neurochemical effects of long-term oral, previously chemically analyzed, Greek RJ administration to aged rats.
Materials and methods: RJ powder was given to 18-month old male Wistar rats (50 and 100 mg of powder/kg b.w./day) by gastric gavage for 2 months. The spatial memory was assessed in the water maze and next the level of neurotransmitters, their metabolites and utilization in the selected brain regions were estimated.
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Although royal jelly constitutes one of the richest natural sources of pantothenic acid (vitamin B-5), a reliable and validated chromatographic method to determine this analyte in this matrix has yet to be described in the literature. In this work we present an original RP-HPLC procedure to measure the concentration of pantothenic acid in royal jelly. A sample pre-treatment is needed to prevent the interference of high protein concentration in the matrix. The method has been validated in terms of LoD, LoQ, linearity, precision (repeatability and reproducibility) and bias. Finally, the whole procedure was tested on a number of samples of royal jelly from different origins, providing concentration values ranging from 120 +/- 30 to 565 +/- 40 mg kg(-1) of pantothenic acid.

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Nucleotides, nucleosides and nucleobases play a greater role in the physiological activity of organisms which are highly present in royal jelly (RJ). The objective of the present study is to develop a HPLC method to simultaneous determine nucleotides, nucleosides and nucleobases in RJ and access them in fresh and commercial RJ samples. The LOD and LOQ were 12.2-99.6 mug/L and 40.8-289.4 mug/L, respectively with nearly 100.9% recoveries. Except uric acid, all other compounds were found in RJ samples. Significant difference in the average content of compounds in fresh (2682.93 mg/kg) and commercial samples (3152.78 mg/kg) were observed. AMP, adenosine and adenine were found predominant in all the samples. Significant higher levels of ATP, ADP and AMP was seen in fresh RJ samples, and IMP, uridine, guanosine, and thymidine was seen in commercial RJ samples. The investigated compounds can be used as indexes for assessment RJ freshness and quality.

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Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. (c) 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

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Metabolome sampling is one of the most important factors that determine the quality of metabolomics data. The main steps in metabolite sample preparation include quenching and metabolite extraction. Quenching with 60% (v/v) cold methanol at -40 degrees C has been most commonly used for Saccharomyces cerevisiae, and this method was recently modified as "leakage-free cold methanol quenching" using pure methanol at -40 degrees C. Boiling ethanol (75%, v/v) and cold pure methanol are the most widely used extraction solvents for S. cerevisiae. In the present study, metabolome sampling protocols, including the above methods, were evaluated by analyzing 110 identified intracellular metabolites of S. cerevisiae using gas chromatography/time-of-flight mass spectrometry. According to our results, fast filtration followed by washing with an appropriate volume of water can minimize the metabolite loss due to cell leakage as well as the contamination by extracellular metabolites, For metabolite extraction, acetonitrile/water mixture (1:1, v/v) at -20 degrees C was the most effective. These results imply that the systematic evaluation of existing methods and the development of customized methods for each microorganism are critical for metabolome sample preparation to facilitate the reliable and accurate analysis of metabolome.

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Analytical Chemistry, 2005,77(24):8086-8094.
DOI:10.1021/ac051211vURLPMID:16351159 [本文引用: 1]
Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of approximately 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 microM. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.

[31] 夏广辉, 沈伟健, 余可垚, 吴斌, 张睿, 沈崇钰, 赵增运, 卞筱泓, 许激扬. 气相色谱-负化学源质谱法测定蜂蜜和王浆中4种杀虫剂的残留
色谱, 2014,32(7):741-745.
DOI:10.3724/SP.J.1123.2014.01043URL [本文引用: 1]
建立了气相色谱-负化学源质谱（GC-NCI/MS）测定蜂蜜和王浆中4种杀虫剂残留量的方法。蜂蜜样品由乙酸乙酯提取、乙二胺-N-丙基硅烷（PSA）净化，而王浆样品经乙腈-水（1：1，v/v）提取、C18固相萃取柱净化，采用GC-NCI/MS测定，外标法定量。结果表明：在50~500 μg/L范围内4种农药的线性良好；所有农药的LOD在0.12~5.0 μg/kg之间，LOQ在0.40~16.5 μg/kg之间；在10、15、20 μg/kg 3个添加水平下，4种农药的平均回收率在78.2%~110.0%之间，且RSD均小于14%。所有农药的测定均没有出现干扰峰。该方法简单、快速，准确度、精密度和选择性高，抗干扰能力强，可用于蜂蜜和王浆中这4种农药的快速检测。
XIA G H, SHEN W J, YU K Y, WU B, ZHANG R, SHEN C Y, ZHAO Z Y, BIAN X H, XU J Y. Determination of four insecticide residues in honey and royal jelly by gas chromatography-negative chemical ionization mass spectrometry
Chinese Journal of Chromatographyi, 2014,32(7):741-745. (in Chinese)
DOI:10.3724/SP.J.1123.2014.01043URL [本文引用: 1]
建立了气相色谱-负化学源质谱（GC-NCI/MS）测定蜂蜜和王浆中4种杀虫剂残留量的方法。蜂蜜样品由乙酸乙酯提取、乙二胺-N-丙基硅烷（PSA）净化，而王浆样品经乙腈-水（1：1，v/v）提取、C18固相萃取柱净化，采用GC-NCI/MS测定，外标法定量。结果表明：在50~500 μg/L范围内4种农药的线性良好；所有农药的LOD在0.12~5.0 μg/kg之间，LOQ在0.40~16.5 μg/kg之间；在10、15、20 μg/kg 3个添加水平下，4种农药的平均回收率在78.2%~110.0%之间，且RSD均小于14%。所有农药的测定均没有出现干扰峰。该方法简单、快速，准确度、精密度和选择性高，抗干扰能力强，可用于蜂蜜和王浆中这4种农药的快速检测。

[32] ALTAYE S Z, MENG L F, LI J K. Molecular insights into the enhanced performance of royal jelly secretion by a stock of honeybee ( Apis mellifera ligustica) selected for increasing royal jelly production
Apidologie, 2019,50(4):436-453.
DOI:10.1007/s13592-019-00656-1URL [本文引用: 1]

[33] CAO L F, ZHENG H Q, PIRK C W W, HU F L, XU Z W. High royal jelly-producing honeybees (Apis mellifera ligustica) (Hymenoptera: Apidae) in China
Journal of Economic Entomology, 2016,109(2):510-514.
DOI:10.1093/jee/tow013URLPMID:26921226 [本文引用: 1]
China is the largest producer and exporter of royal jelly (RJ) in the world, supplying >90% of the global market. The high production of RJ in China is principally owing to the high RJ-producing lineage of honeybees (Apis mellifera ligustica Spinola, 1806) established by beekeepers in the 1980s. We describe the development of high royal jelly-producing honeybees and the management of this lineage today. Previous research and recent advances in the genetic characterization of this lineage, and the molecular markers and mechanisms associated with high RJ production are summarized. The gaps in our knowledge and prospects for future research are also highlighted.

[34] HU H, BEZABIH G, FENG M, WEI Q H, ZHANG X F, WU F, MENG L F, FANG Y, HAN B, MA C, LI J K. In-depth proteome of the hypopharyngeal glands of honeybee workers reveals highly activated protein and energy metabolism in priming the secretion of royal jelly
Molecular and Cellular Proteomics, 2019,18(4):606-621.
DOI:10.1074/mcp.RA118.001257URLPMID:30617159 [本文引用: 1]
Royal jelly (RJ) is a secretion of the hypopharyngeal glands (HGs) of honeybee workers. High royal jelly producing bees (RJBs), a stock of honeybees selected from Italian bees (ITBs), have developed a stronger ability to produce RJ than ITBs. However, the mechanism underpinning the high RJ-producing performance in RJBs is still poorly understood. We have comprehensively characterized and compared the proteome across the life span of worker bees between the ITBs and RJBs. Our data uncover distinct molecular landscapes that regulate the gland ontogeny and activity corresponding with age-specific tasks. Nurse bees (NBs) have a well-developed acini morphology and cytoskeleton of secretory cells in HGs to prime the gland activities of RJ secretion. In RJB NBs, pathways involved in protein synthesis and energy metabolism are functionally induced to cement the enhanced RJ secretion compared with ITBs. In behavior-manipulated RJB NBs, the strongly expressed proteins implicated in protein synthesis and energy metabolism further demonstrate their critical roles in the regulation of RJ secretion. Our findings provide a novel understanding of the mechanism consolidating the high RJ-output in RJBs.

[35] GIKA H G, THEODORIDIS G A, WINGATE J E, WILSON I D. Within-day reproducibility of a HPLC-MS-based method for metabonomic analysis: Application to human urine
Journal of Proteome Research, 2007,6(8):3291-3303.
DOI:10.1021/pr070183pURLPMID:17625818 [本文引用: 1]
Self-evidently, research in areas supporting

[36] JOHANNINGSMEIER S D, HARRIS G K, KLEVORN C M. Metabolomic technologies for improving the quality of food: Practice and promise
Annual Review of Food Science and Technology, 2016,7:413-438.
DOI:10.1146/annurev-food-022814-015721URLPMID:26772413 [本文引用: 1]
It is now well documented that the diet has a significant impact on human health and well-being. However, the complete set of small molecule metabolites present in foods that make up the human diet and the role of food production systems in altering this food metabolome are still largely unknown. Metabolomic platforms that rely on nuclear magnetic resonance (NMR) and mass spectrometry (MS) analytical technologies are being employed to study the impact of agricultural practices, processing, and storage on the global chemical composition of food; to identify novel bioactive compounds; and for authentication and region-of-origin classifications. This review provides an overview of the current terminology, analytical methods, and compounds associated with metabolomic studies, and provides insight into the application of metabolomics to generate new knowledge that enables us to produce, preserve, and distribute high-quality foods for health promotion.

[37] WANT E J, WILSON I D, GIKA H, THEODORIDIS G, PLUMB R S, SHOCKCOR J, HOLMES E, NICHOLSON J K. Global metabolic profiling procedures for urine using UPLC-MS
Nature Protocols, 2010,5(6):1005-1018.
DOI:10.1038/nprot.2010.50URLPMID:20448546 [本文引用: 1]
The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.

[38] GIKA H G, THEODORIDIS G A, VRHOVSEK U, MATTIVI F. Quantitative profiling of polar primary metabolites using hydrophilic interaction ultrahigh performance liquid chromatography-tandem mass spectrometry
Journal of Chromatography A, 2012,1259:121-127.
URLPMID:24369998 [本文引用: 1]

[39] ZHAO Y Z, LI Z G, TIAN W L, FANG X M, SU S K, PENG W J. Differential volatile organic compounds in royal jelly associated with different nectar plants
Journal of Integrative Agriculture, 2016,15(5):1157-1165.
DOI:10.1016/S2095-3119(15)61274-6URL [本文引用: 1]

[40] ZHENG H Q, HU F L, DIETEMANN V. Changes in composition of royal jelly harvested at different times: Consequences for quality standards
Apidologie, 2011,42(1):39-47.
DOI:10.1051/apido/2010033URL [本文引用: 1]
Most of the studies on royal jelly (RJ) composition or properties as well as quality standards of commercially available royal jelly are based on RJ harvested three days (72 h) after grafting. In China, some beekeepers produce RJ harvested one (24 h) or two (48 h) days after grafting. There is a lack of knowledge about the quality of the royal jelly harvested earlier than 72 h. This study compared 32 colonies for their chemical compositions of RJ harvested at 24, 48 and 72 h after grafting, according to the proportion of moisture, protein, 10-HDA, total sugar and the value of acidity and superoxide dismutase activity. The analysis of RJ samples revealed that the composition varied significantly (for both fresh and dehydrated samples) and on some occasions above and below the range of present Chinese and Swiss standards. The results suggest that harvesting time should be considered when defining new quality standards of RJ.

[41] CREYDT M, ARNDT M, HUDZIK D, FISCHER M. Plant metabolomics: Evaluation of different extraction parameters for nontargeted UPLC-ESI-QTOF-mass spectrometry at the example of white Asparagus officinalis
Journal of Agricultural and Food Chemistry, 2018,66(48):12876-12887.
DOI:10.1021/acs.jafc.8b06037URLPMID:30411896 [本文引用: 1]
The extraction of metabolites turns out to be one of the most important key factors for nontargeted metabolomics approaches as this step can significantly affects the informative value of the successive measurements. Compared to metabolomics experiments of various matrices of bacterial or mammalian origins, there are only few studies, which focus on different extraction methods for plant metabolomics analyses. In this study, various solvent extraction compositions were compared and assessed using an UPLC-ESI-QTOF-MS strategy. Exemplary, white asparagus ( Asparagus officinalis) were employed as a low-fat-, low-protein-, high-water-content model commodity with the objective of designing an optimal nontargeted extraction protocol for polar and nonpolar metabolites. Furthermore, the influence of acid addition, mechanical cell disruption methods (ball mill, ultrasonic bath, vortex mixer), and extract stability have been systematically scrutinized too. The different extraction protocols were compared based on sum of features, sum of peak intensities, sum of peak areas, as well as by analyzing individual signals of as many different substance groups as possible to obtain a maximum overview.

[42] WANT E J O’MAILLE G, SMITH C A, BRANDON T R, URITBOONTHAI W, QIN C, TRAUGER S A, SIUZDAK G. Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry
Analytical Chemistry, 2006,78(3):743-752.
DOI:10.1021/ac051312tURLPMID:16448047 [本文引用: 1]
The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10,000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.

[43] XUE X F, ZHOU J H, WU L M, HU F L, ZHAO J. HPLC determination of adenosine in royal jelly
Food Chemistry, 2009,115(2):715-719.
DOI:10.1016/j.foodchem.2008.12.003URL [本文引用: 1]

[44] BONDIA-PONS I, SAVOLAINEN O, T?RR?NEN R, MARTINEZ J A, POUTANEN K, HANHINEVA K. Metabolic profiling of Goji berry extracts for discrimination of geographical origin by non- targeted liquid chromatography coupled to quadrupole time-of-flight mass spectrometry
Food Research International, 2014,63:132-138.
DOI:10.1016/j.foodres.2014.01.067URL [本文引用: 1]
The fruit of Lycium barbarum (Solanaceae), known as Goji berry, or wolfberry, has long been used in traditional Chinese medicine, and is increasingly becoming popular in Western diets due to its potential health benefits. The majority of commercially produced Goji berries come from certain regions in Asia. In this study we explored the discrimination of phytochemical content between four different geographic origins of Goji berries by applying non-targeted liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-qTOF-MS) metabolite profiling. Principal component analysis was able to clearly distinguish the berries by the geographic origin when applied to the non-targeted profiling data of Mongolian, Chinese and two Tibetan origin Goji berry extracts. Furthermore, partial least squares discriminant analysis (PLS-DA) provided indicative markers of discrimination between the different origins, and quality threshold cluster analysis classified the most discriminative compounds according to their occurrence between the different origins. The largest cluster included the most discriminative metabolites in the Mongolian variety, which was also seen as the most distant group in the PCA analysis as compared to the other countries of origin.
Mongolian Goji berries were mainly characterized by significantly higher levels of several flavonol glycosides, such as quercetin and kaempferol glycosides; isomers of dicaffeoylquinic acid and phenolic acids such as coumaric acid. In addition to the various phytochemical metabolites identified, a pesticide compound was found especially in the extracts of Goji berries from China.
The present non-targeted metabolite profiling proved to be a useful approach of the Foodomics field for assessment of geographical origin of berries. (C) 2014 Elsevier Ltd.

[45] HRBEK V, REKTORISOVA M, CHMELAROVA H, OVESNA J, HAJSLOVA J. Authenticity assessment of garlic using a metabolomic approach based on high resolution mass spectrometry
Journal of Food Composition and Analysis, 2018,67:19-28.
DOI:10.1016/j.jfca.2017.12.020URL [本文引用: 1]

[46] DIAZ R, POZO O J, SANCHO J V, HERNANDEZ F. Metabolomic approaches for orange origin discrimination by ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry
Food Chemistry, 2014,157:84-93.
DOI:10.1016/j.foodchem.2014.02.009URL [本文引用: 1]
In this work, hybrid quadrupole time-of-flight mass spectrometer (QTOF MS) coupled to ultra high performance liquid chromatography (UHPLC) has been used for biomarkers identification for correct authentication of Valencia (Spain) oranges. Differentiation from foreign Argentinean, Brazilian and South African oranges has been carried out using XCMS application and multivariate analysis to UHPLC-(Q)TOF MS data acquired in both, positive and negative ionisation modes. Several markers have been found and corroborated by analysing two seasons samples. A seasonal independent marker was found and its structure elucidated using accurate mass data and MSE fragmentation spectrum information. Empirical formula was searched in Reaxys database applying sub-structure filtering from the fragments obtained. Three possible structures were found and citrusin D, a compound present in sweet oranges, has been identified as the most plausible as it fits better with the product ion scan performed for this compound. As a result of data obtained in this work, citrusin D is suggested as a potential marker to distinguish the geographic origin of oranges. (C) 2014 Elsevier Ltd.

[47] YANSHOLE V V, SNYTNIKOVA O A, KIRYUTIN A S, YANSHOLE L V, SAGDEEV R Z, TSENTALOVICH Y P. Metabolomics of the rat lens: A combined LC-MS and NMR study
Experimental Eye Research, 2014,125:71-78.
DOI:10.1016/j.exer.2014.05.016URL [本文引用: 1]
This work is the first comprehensive report on the quantitative metabolomic composition of the rat lens. Quantitative metabolomic profiles of lenses were acquired with the combined use of high-frequency nuclear magnetic resonance (NMR) and high-performance liquid chromatography with highresolution mass-spectrometric detection (LC-MS) methods. More than forty low molecular weight compounds found in the lens have been reliably identified and quantified. The most abundant metabolites in the 3-month-old Wistar rat lens are taurine, hypotaurine, lactate, phosphocholine and reduced glutathione. The analysis of age-related changes in the lens metabolomic composition shows a gradual decrease of the content of most metabolites. This decrease is the most pronounced between 1 and 3 months, which probably corresponds to the completion of the lens maturation in one-month-old rats and to the high rate of the young lens growth. The enhanced levels of tryptophan, tyrosine, carnitine, glycerophosphate, GSH and GSSG were found in lenses of senescence-accelerated OXYS rats; for some metabolites, this effect may probably be attributed to the compensatory response to oxidative stress. (C) 2014 Elsevier Ltd.

[48] MEDIANI A, ABAS F, MAULIDIANI M, KHATIB A, TAN C P, ISMAIL I S, SHAARI K, ISMAIL A. Characterization of metabolite profile in Phyllanthus niruri and correlation with bioactivity elucidated by nuclear magnetic resonance based metabolomics
Molecules, 2017,22(6):902.
DOI:10.3390/molecules22060902URL [本文引用: 1]

[49] MEDIANI A, ABAS F, KHATIB A, TAN C P, ISMAIL I S, SHAARI K, ISMAIL A, LAJIS N H. Phytochemical and biological features of Phyllanthus niruri and Phyllanthus urinaria harvested at different growth stages revealed by ¹H NMR-based metabolomics
Industrial Crops and Products, 2015,77:602-613.
DOI:10.1016/j.indcrop.2015.09.036URL [本文引用: 1]