法医学杂志 ›› 2023, Vol. 39 ›› Issue (6): 549-556.DOI: 10.12116/j.issn.1004-5619.2022.521204
黄傲1,2(), 温姝博1,2, 孔倩倩2, 赵珍敏2(
), 刘希玲2(
)
收稿日期:
2022-12-30
发布日期:
2024-01-17
出版日期:
2023-12-25
通讯作者:
赵珍敏,刘希玲
作者简介:
黄傲(1997—),女,硕士研究生,主要从事法医遗传学研究;E-mail:aohuang0407@163.com
基金资助:
Ao HUANG1,2(), Shu-bo WEN1,2, Qian-qian KONG2, Zhen-min ZHAO2(
), Xi-ling LIU2(
)
Received:
2022-12-30
Online:
2024-01-17
Published:
2023-12-25
Contact:
Zhen-min ZHAO, Xi-ling LIU
摘要:
目的 研究外周血形成干燥血斑后蛋白质水平的变化。 方法 应用液相色谱-串联质谱法(LC-MS/MS)和非标记定量(label-free quantification,LFQ)策略进行全血和血斑的蛋白质检测,分别采用R 4.2.1软件limma包和edgeR包筛选差异蛋白质,再进行生物学功能、信号通路和亚细胞定位分析。 结果 全血和血斑中分别检测到623个和596个蛋白质,其中31个蛋白质的定量差异具有统计学意义,包括10个丰度在血斑中上调和21个丰度在血斑中下调的蛋白质。 结论 全血和血斑中的蛋白质丰度高度相关,两者间蛋白质丰度的改变可能与细胞内源性蛋白和结构蛋白的变化相关。应用蛋白质组学技术可以辅助蛋白质生物标志物的筛选和鉴定,为法医学研究引入新型生物标志物。
中图分类号:
黄傲, 温姝博, 孔倩倩, 赵珍敏, 刘希玲. 全血和血斑蛋白质组学差异分析[J]. 法医学杂志, 2023, 39(6): 549-556.
Ao HUANG, Shu-bo WEN, Qian-qian KONG, Zhen-min ZHAO, Xi-ling LIU. Proteomic Difference Analysis of Whole Blood and Bloodstains[J]. Journal of Forensic Medicine, 2023, 39(6): 549-556.
组别 | 样本 | 年龄/岁 | 平均年龄/岁 | 性别 | 血型 | 全血 | 血斑 |
---|---|---|---|---|---|---|---|
P1 | P1-1,P1-2,P1-3,P1-4,P1-5 | 25,25,25,26,28 | 26 | 女 | O型 | 全血-1 | 血斑-1 |
P2 | P2-1,P2-2,P2-3,P2-4,P2-5 | 25,26,27,29,47 | 30 | 男 | O型 | 全血-2 | 血斑-2 |
P3 | P3-1,P3-2,P3-3,P3-4,P3-5 | 25,26,26,26,28 | 26 | 男 | A型 | 全血-3 | 血斑-3 |
表1 样本分组信息
Tab. 1 Sample grouping information
组别 | 样本 | 年龄/岁 | 平均年龄/岁 | 性别 | 血型 | 全血 | 血斑 |
---|---|---|---|---|---|---|---|
P1 | P1-1,P1-2,P1-3,P1-4,P1-5 | 25,25,25,26,28 | 26 | 女 | O型 | 全血-1 | 血斑-1 |
P2 | P2-1,P2-2,P2-3,P2-4,P2-5 | 25,26,27,29,47 | 30 | 男 | O型 | 全血-2 | 血斑-2 |
P3 | P3-1,P3-2,P3-3,P3-4,P3-5 | 25,26,26,26,28 | 26 | 男 | A型 | 全血-3 | 血斑-3 |
图1 全血和血斑蛋白质丰度的相关性分析结果A:全血和血斑蛋白质丰度的相关性[数字表示两样本之间的Pearson相关系数(r)];B:全血和血斑间蛋白质定量均值的相关性。
Fig. 1 Correlation analysis results of protein abundances between the whole blood and bloodstains
图2 全血和血斑中丰度最高和最低的20个蛋白质的比较A:全血和血斑中丰度最高和最低的20个蛋白质的丰度变化(灰色虚线代表检测到的所有蛋白质丰度的平均值,“H”表示丰度最高的蛋白质,“L”表示丰度最低的蛋白质);B:全血和血斑中丰度最高的20个蛋白质。
Fig. 2 Comparison of the 20 proteins with the highest and the lowest abundance between the whole blood and bloodstains
编号 | 蛋白质 | Uniprot ID | 基因 | 亚细胞定位 | log2FC | -log10P | 变化趋势 |
---|---|---|---|---|---|---|---|
1 | 载脂蛋白C3 | P02656 | APOC3 | 分泌型 | 0.70 | 0.62 | ↑ |
2 | 核受体结合蛋白 | Q9UHY1 | NRBP1 | 胞质 | 0.72 | 1.52 | ↑ |
3 | 免疫球蛋白λ变量9-49 | A0A0B4J1Y8 | IGLV9-49 | 分泌型 | 26.00 | 10.15 | ↑ |
4 | 补体因子D | P00746 | CFD | 分泌型 | 23.57 | 11.08 | ↑ |
5 | 半胱氨酸蛋白酶抑制剂C | P01034 | CST3 | 分泌型 | 23.62 | 10.01 | ↑ |
6 | 核糖核苷二磷酸还原酶大亚基 | P23921 | RRM1 | 胞质 | 22.95 | 10.90 | ↑ |
7 | 根蛋白 | P35241 | RDX | 细胞膜 | 23.86 | 11.08 | ↑ |
8 | 肝细胞生长因子激活剂 | Q04756 | HGFAC | 分泌型 | 22.96 | 9.24 | ↑ |
9 | 自噬相关蛋白3 | Q9NT62 | ATG3 | 胞质 | 23.03 | 11.08 | ↑ |
10 | SEC14样蛋白4 | Q9UDX3 | SEC14L4 | 胞质 | 25.70 | 11.08 | ↑ |
11 | 铵转运体Rh A型 | Q02094 | RHAG | 浆膜 | -0.75 | 1.65 | ↓ |
12 | 胞质乙酰辅酶A硫脂水解酶 | O00154 | ACOT7 | 胞质 | -23.63 | 10.64 | ↓ |
13 | 26S蛋白酶体非ATP酶调节亚基3 | O43242 | PSMD3 | 细胞质基质 | -28.65 | 11.08 | ↓ |
14 | α-辅肌动蛋白-4 | O43707 | ACTN4 | 细胞核 | -24.56 | 8.25 | ↓ |
15 | 微管蛋白β链 | P07437 | TUBB | 胞质 | -23.18 | 10.93 | ↓ |
16 | 热激蛋白60 | P10809 | HSPD1 | 线粒体基质 | -24.89 | 6.97 | ↓ |
17 | 断裂点丛集区蛋白 | P11274 | BCR | 突触后致密区 | -26.77 | 8.89 | ↓ |
18 | 延伸因子2 | P13639 | EEF2 | 胞质 | -23.37 | 9.23 | ↓ |
19 | 连接桥粒斑珠蛋白 | P14923 | JUP | 细胞连接处 | -22.95 | 6.37 | ↓ |
20 | 异质性细胞核核糖蛋白A2/B1 | P22626 | HNRNPA2B1 | 细胞核 | -23.07 | 8.36 | ↓ |
21 | 甘露聚糖结合凝集素丝氨酸蛋白酶1 | P48740 | MASP1 | 分泌型 | -24.09 | 10.15 | ↓ |
22 | X连锁Kx血型抗原 | P51811 | XK | 内质网膜 | -24.34 | 11.08 | ↓ |
23 | Epiplakin | P58107 | EPPK1 | 细胞骨架 | -23.05 | 6.85 | ↓ |
24 | 转化蛋白RhoA | P61586 | RHOA | 细胞膜 | -25.39 | 11.08 | ↓ |
25 | 免疫球蛋白结合蛋白1 | P78318 | IGBP1 | 胞质 | -23.11 | 10.38 | ↓ |
26 | 1,4-α-葡聚糖分支酶 | Q04446 | GBE1 | 胞质 | -22.60 | 11.08 | ↓ |
27 | 细胞间黏附分子4 | Q14773 | ICAM4 | 细胞膜 | -24.15 | 11.08 | ↓ |
28 | 多聚C结合蛋白1 | Q15365 | PCBP1 | 细胞核 | -24.62 | 10.22 | ↓ |
29 | 含NudC结构域的蛋白质2 | Q8WVJ2 | NUDCD2 | 染色体 | -24.24 | 11.08 | ↓ |
30 | 羧肽酶B2 | Q96IY4 | CPB2 | 分泌型 | -24.54 | 10.64 | ↓ |
31 | Rabankyrin-5 | Q9P2R3 | ANKFY1 | 细胞膜 | -22.81 | 11.08 | ↓ |
表2 全血和血斑中的差异蛋白质及其对应的亚细胞定位信息
Tab. 2 Differential proteins in the whole blood and bloodstains andthe corresponding information of subcellular localization
编号 | 蛋白质 | Uniprot ID | 基因 | 亚细胞定位 | log2FC | -log10P | 变化趋势 |
---|---|---|---|---|---|---|---|
1 | 载脂蛋白C3 | P02656 | APOC3 | 分泌型 | 0.70 | 0.62 | ↑ |
2 | 核受体结合蛋白 | Q9UHY1 | NRBP1 | 胞质 | 0.72 | 1.52 | ↑ |
3 | 免疫球蛋白λ变量9-49 | A0A0B4J1Y8 | IGLV9-49 | 分泌型 | 26.00 | 10.15 | ↑ |
4 | 补体因子D | P00746 | CFD | 分泌型 | 23.57 | 11.08 | ↑ |
5 | 半胱氨酸蛋白酶抑制剂C | P01034 | CST3 | 分泌型 | 23.62 | 10.01 | ↑ |
6 | 核糖核苷二磷酸还原酶大亚基 | P23921 | RRM1 | 胞质 | 22.95 | 10.90 | ↑ |
7 | 根蛋白 | P35241 | RDX | 细胞膜 | 23.86 | 11.08 | ↑ |
8 | 肝细胞生长因子激活剂 | Q04756 | HGFAC | 分泌型 | 22.96 | 9.24 | ↑ |
9 | 自噬相关蛋白3 | Q9NT62 | ATG3 | 胞质 | 23.03 | 11.08 | ↑ |
10 | SEC14样蛋白4 | Q9UDX3 | SEC14L4 | 胞质 | 25.70 | 11.08 | ↑ |
11 | 铵转运体Rh A型 | Q02094 | RHAG | 浆膜 | -0.75 | 1.65 | ↓ |
12 | 胞质乙酰辅酶A硫脂水解酶 | O00154 | ACOT7 | 胞质 | -23.63 | 10.64 | ↓ |
13 | 26S蛋白酶体非ATP酶调节亚基3 | O43242 | PSMD3 | 细胞质基质 | -28.65 | 11.08 | ↓ |
14 | α-辅肌动蛋白-4 | O43707 | ACTN4 | 细胞核 | -24.56 | 8.25 | ↓ |
15 | 微管蛋白β链 | P07437 | TUBB | 胞质 | -23.18 | 10.93 | ↓ |
16 | 热激蛋白60 | P10809 | HSPD1 | 线粒体基质 | -24.89 | 6.97 | ↓ |
17 | 断裂点丛集区蛋白 | P11274 | BCR | 突触后致密区 | -26.77 | 8.89 | ↓ |
18 | 延伸因子2 | P13639 | EEF2 | 胞质 | -23.37 | 9.23 | ↓ |
19 | 连接桥粒斑珠蛋白 | P14923 | JUP | 细胞连接处 | -22.95 | 6.37 | ↓ |
20 | 异质性细胞核核糖蛋白A2/B1 | P22626 | HNRNPA2B1 | 细胞核 | -23.07 | 8.36 | ↓ |
21 | 甘露聚糖结合凝集素丝氨酸蛋白酶1 | P48740 | MASP1 | 分泌型 | -24.09 | 10.15 | ↓ |
22 | X连锁Kx血型抗原 | P51811 | XK | 内质网膜 | -24.34 | 11.08 | ↓ |
23 | Epiplakin | P58107 | EPPK1 | 细胞骨架 | -23.05 | 6.85 | ↓ |
24 | 转化蛋白RhoA | P61586 | RHOA | 细胞膜 | -25.39 | 11.08 | ↓ |
25 | 免疫球蛋白结合蛋白1 | P78318 | IGBP1 | 胞质 | -23.11 | 10.38 | ↓ |
26 | 1,4-α-葡聚糖分支酶 | Q04446 | GBE1 | 胞质 | -22.60 | 11.08 | ↓ |
27 | 细胞间黏附分子4 | Q14773 | ICAM4 | 细胞膜 | -24.15 | 11.08 | ↓ |
28 | 多聚C结合蛋白1 | Q15365 | PCBP1 | 细胞核 | -24.62 | 10.22 | ↓ |
29 | 含NudC结构域的蛋白质2 | Q8WVJ2 | NUDCD2 | 染色体 | -24.24 | 11.08 | ↓ |
30 | 羧肽酶B2 | Q96IY4 | CPB2 | 分泌型 | -24.54 | 10.64 | ↓ |
31 | Rabankyrin-5 | Q9P2R3 | ANKFY1 | 细胞膜 | -22.81 | 11.08 | ↓ |
1 | MÜLLER J B, GEYER P E, COLAÇO A R, et al. The proteome landscape of the kingdoms of life[J]. Nature,2020,582(7813):592-596. doi:10.1038/s41586-020-2402-x . |
2 | UHLÉN M, BJÖRLING E, AGATON C, et al. A human protein atlas for normal and cancer tissues based on antibody proteomics[J]. Mol Cell Proteomics,2005,4(12):1920-1932. doi:10.1074/mcp.M500279-MCP200 . |
3 | OMENN G S, LANE L, OVERALL C M, et al. Progress on identifying and characterizing the human proteome: 2018 metrics from the HUPO human proteome project[J]. J Proteome Res,2018,17(12):4031-4041. doi:10.1021/acs.jproteome.8b00441 . |
4 | SUHRE K, MCCARTHY M I, SCHWENK J M. Genetics meets proteomics: Perspectives for large population-based studies[J]. Nat Rev Genet,2021, 22(1):19-37. doi:10.1038/s41576-020-0268-2 . |
5 | ZHONG W, EDFORS F, GUMMESSON A, et al. Next generation plasma proteome profiling to monitor health and disease[J]. Nat Commun,2021,12(1):2493. doi:10.1038/s41467-021-22767-z . |
6 | LEGG K M, POWELL R, REISDORPH N, et al. Verification of protein biomarker specificity for the identification of biological stains by quadrupole time-of-flight mass spectrometry[J]. Electrophoresis,2017,38(6):833-845. doi:10.1002/elps.201600352 . |
7 | LEHALLIER B, GATE D, SCHAUM N, et al. Undulating changes in human plasma proteome profiles across the lifespan[J]. Nat Med,2019,25(12):1843-1850. doi:10.1038/s41591-019-0673-2 . |
8 | PALAGUMMI S, HARBISON S, FLEMING R. A time-course analysis of mRNA expression during injury healing in human dermal injuries[J]. Int J Legal Med,2014,128(3):403-414. doi:10.1007/s00414-013-0941-5 . |
9 | 韩刘君,徐红梅,陈龙. 蛋白质组学及其在法医病理学中的应用[J].法医学杂志,2019,35(1):78-83. doi:10.12116/j.issn.1004-5619.2019.01.015 . |
HAN L J, XU H M, CHEN L. Proteomics and its application in forensic pathology[J]. Fayixue Zazhi,2019,35(1):78-83. | |
10 | 张旭东,姜垚如,梁芯瑞,等. 蛋白质芯片检测技术结合多维统计方法推断死亡时间[J].法医学杂志,2023,39(2):115-120,128. doi:10.12116/j.issn.1004-5619.2022.420407 . |
ZHANG X D, JIANG Y R, LIANG X R, et al. Postmortem interval estimation using protein chip techno-logy combined with multivariate analysis methods[J]. Fayixue Zazhi,2023,39(2):115-120,128. | |
11 | DÍAZ MARTÍN R D, CAMACHO-MARTÍNEZ Z, AMBROSIO HERNÁNDEZ J R, et al. Proteomics as a new tool in forensic sciences[J]. Span J Leg Med,2019,45(3):114-122. doi:10.1016/j.remle.2019.08.001 . |
12 | DOTY K C, MURO C K, LEDNEV I K. Predicting the time of the crime: Bloodstain aging estimation for up to two years[J]. Forensic Chem,2017,5:1-7. doi:10.1016/j.forc.2017.05.002 . |
13 | BJÖRKESTEN J, ENROTH S, SHEN Q, et al. Stability of proteins in dried blood spot biobanks[J]. Mol Cell Proteomics,2017,16(7):1286-1296. doi:10 . |
1074/mcp.RA117.000015. | |
14 | KIM J Y, PARK J H, KIM M I, et al. Identification of female-specific blood stains using a 17β- estradiol-targeted aptamer-based sensor[J]. Int J Legal Med,2018,132(1):91-98. doi:10.1007/s00414-017-1718-z . |
15 | JACKSON S, FREY B S, BATES M N, et al. Direct differentiation of whole blood for forensic serology analysis by thread spray mass spectrometry[J]. Analyst,2020,145(16):5615-5623. doi:10.1039/d0an00857e . |
16 | ESHGHI A, PISTAWKA A J, LIU J, et al. Concentration determination of >200 proteins in dried blood spots for biomarker discovery and validation[J]. Mol Cell Proteomics,2020,19(3):540-553. doi:10 . |
1074/mcp.TIR119.001820. | |
17 | WIŚNIEWSKI J R. Filter-aided sample preparation for proteome analysis[J]. Methods Mol Biol,2018,1841:3-10. doi:10.1007/978-1-4939-8695-8_1 . |
18 | ZHU W, SMITH J W, HUANG C M. Mass spec-trometry-based label-free quantitative proteomics[J]. J Biomed Biotechnol,2010,2010:840518. doi:10.1155/2010/840518 . |
19 | SHERMAN B T, HAO M, QIU J, et al. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update)[J]. Nucleic Acids Res,2022,50(W1):W216-W221. doi:10.1093/nar/gkac194 . |
20 | CONSORTIUM U. UniProt: The universal protein knowledgebase in 2021[J]. Nucleic Acids Res,2021,49(D1):D480-D489. doi:10.1093/nar/gkaa1100 . |
21 | VAN WIJK K J, LEPPERT T, SUN Q, et al. The Arabidopsis PeptideAtlas: Harnessing worldwide proteomics data to create a comprehensive community proteomics resource[J]. Plant Cell,2021,33(11):3421-3453. doi:10.1093/plcell/koab211 . |
22 | DEUTSCH E W, OMENN G S, SUN Z, et al. Advances and utility of the human plasma proteome[J]. J Proteome Res,2021,20(12):5241-5263. doi:10 . |
1021/acs.jproteome.1c00657. | |
23 | OSTAN R, MONTI D, GUERESI P, et al. Gender, aging and longevity in humans: An update of an intriguing/neglected scenario paving the way to a gender-specific medicine[J]. Clin Sci (Lond),2016,130(19):1711-1725. doi:10.1042/CS20160004 . |
24 | ARSENE C G, OHLENDORF R, BURKITT W, et al. Protein quantification by isotope dilution mass spectrometry of proteolytic fragments: Cleavage rate and accuracy[J]. Anal Chem,2008,80(11):4154-4160. doi:10.1021/ac7024738 . |
25 | BROWNRIDGE P, BEYNON R J. The importance of the digest: Proteolysis and absolute quantification in proteomics[J]. Methods,2011,54(4):351-360. doi:10.1016/j.ymeth.2011.05.005 . |
26 | CHAMBERS A G, PERCY A J, YANG J, et al. Multiplexed quantitation of endogenous proteins in dried blood spots by multiple reaction monitoring-mass spectrometry[J]. Mol Cell Proteomics,2013,12(3):781-791. doi:10.1074/mcp.M112.022442 . |
27 | ROSTING C, GJELSTAD A, HALVORSEN T G. Water-soluble dried blood spot in protein analysis: A proof-of-concept study[J]. Anal Chem,2015,87(15):7918-7924. doi:10.1021/acs.analchem.5b01735 . |
28 | THOLEY A, BECKER A. Top-down proteomics for the analysis of proteolytic events -- Methods, applications and perspectives[J]. Biochim Biophys Acta Mol Cell Res,2017,1864(11 Pt B):2191-2199. doi:10.1016/j.bbamcr.2017.07.002 . |
29 | CORADIN M, KARCH K R, GARCIA B A. Monitoring proteolytic processing events by quantitative mass spectrometry[J]. Expert Rev Proteomics,2017,14(5):409-418. doi:10.1080/14789450.2017.1316977 . |
30 | CIZDZIEL J V. Determination of lead in blood by laser ablation ICP-TOF-MS analysis of blood spotted and dried on filter paper: A feasibility study[J]. Anal Bioanal Chem,2007,388(3):603-611. doi:10.1007/s00216-007-1242-y . |
31 | COWANS N J, SUONPAA M, KOURU H, et al. Evaluation of a dried blood spot assay to measure prenatal screening markers pregnancy-associated plas-ma protein a and free β-subunit of human chorionic gonadotropin[J]. Clin Chem,2013,59(6):968-975. doi: |
32 | 1373/clinchem.2012.194894. |
33 | TSUJITA K, TAKENAWA T, ITOH T. Feedback regulation between plasma membrane tension and membrane-bending proteins organizes cell polarity during leading edge formation[J]. Nat Cell Biol,2015,17(6):749-758. doi:10.1038/ncb3162 . |
34 | ANDERSON N L, ANDERSON N G. The human plasma proteome: History, character, and diagnostic prospects[J]. Mol Cell Proteomics,2002,1(11):845-867. doi:10.1074/mcp.r200007-mcp200 . |
35 | SCHILLING B, RARDIN M J, MACLEAN B X, et al. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: Application to protein acetylation and phosphorylation[J]. Mol Cell Proteomics,2012,11(5):202-214. doi:10.1074/mcp.M112.017707 . |
36 | ZHAO L, CONG X, ZHAI L, et al. Comparative evaluation of label-free quantification strategies[J]. J Proteomics,2020,215:103669. doi:10.1016/j.jprot.2020.103669 . |
37 | LI Z, CHEN D, WANG Q, et al. mRNA and micro-RNA stability validation of blood samples under different environmental conditions[J]. Forensic Sci Int Genet,2021,55:102567. doi:10.1016/j.fsigen.2021.102567 . |
38 | ALBANI P P, FLEMING R. Novel messenger RNAs for body fluid identification[J]. Sci Justice,2018,58(2):145-152. doi:10.1016/j.scijus.2017.09.002 . |
39 | SONG F, LUO H, HOU Y. Developed and evaluated a multiplex mRNA profiling system for body fluid identification in Chinese Han population[J]. J Forensic Leg Med,2015,35:73-80. doi:10.1016/j.jflm.2015.08.006 . |
40 | HEO T M, GWON S Y, YANG J H, et al. Hemoglobin subunit beta protein as a novel marker for time since deposition of bloodstains at crime scenes[J]. Forensic Sci Int,2022,336:111348. doi:10.1016/j.forsciint.2022.111348 . |
41 | HANSON E, INGOLD S, HAAS C, et al. Messenger RNA biomarker signatures for forensic body fluid identification revealed by targeted RNA sequencing[J]. Forensic Sci Int Genet,2018,34:206-221. doi:10.1016/j.fsigen.2018.02.020 . |
42 | ZUBAKOV D, LIU F, KOKMEIJER I, et al. Human age estimation from blood using mRNA, DNA methy-lation, DNA rearrangement, and telomere length[J]. Forensic Sci Int Genet,2016,24:33-43. doi:10.1016/j.fsigen.2016.05.014 . |
43 | WIKLUND F E, BENNET A M, MAGNUSSON P K E, et al. Macrophage inhibitory cytokine-1 (MIC-1/GDF15): A new marker of all-cause mortality[J]. Aging Cell,2010,9(6):1057-1064. doi:10.1111/j.1474- |
9726.2010.00629.x. | |
44 | COHEN E, DILLIN A. The insulin paradox: Aging, proteotoxicity and neurodegeneration[J]. Nat Rev Neurosci,2008,9(10):759-767. doi:10.1038/nrn2474 . |
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