基于综合生物信息学和单细胞测序方法揭示红花-丹参治疗冠心病的生物学和免疫学机制

doi:10.5246/jcps.2023.10.065

中国药学(英文版) ›› 2023, Vol. 32 ›› Issue (10): 796-812.DOI: 10.5246/jcps.2023.10.065

基于综合生物信息学和单细胞测序方法揭示红花-丹参治疗冠心病的生物学和免疫学机制

魏东升¹^,³, 刘孝生¹, 李路珍¹, 齐佳杰¹, 王雨轩¹, 张哲¹^,²^,³^,⁴^,^*()

1. 辽宁中医药大学, 辽宁沈阳 110847
2. 辽宁中医药大学中医药创新工程技术中心, 辽宁沈阳 110847
3. 辽宁中医药大学中医脏象理论及应用教育部重点实验室, 辽宁沈阳 110847
4. 辽宁中医药大学附属医院, 辽宁沈阳 110033

收稿日期:2023-04-25 修回日期:2023-05-18 接受日期:2023-06-05 出版日期:2023-11-04 发布日期:2023-11-04
通讯作者: 张哲
作者简介:
+ Tel.: +86-18102459155, E-mail: pedtrainzhzh7676@163.com
基金资助:
National Administration of Traditional Chinese Medicine Supporting Project for Young Qi Huang Scholars (Grant No. 20201A2180).

Unraveling the biological and immunological mechanisms of safflower-danshen in the treatment of coronary atherosclerotic heart disease: a comprehensive bioinformatics and single-cell sequencing approach

Dongsheng Wei¹^,³, Xiaosheng Liu¹, Luzhen Li¹, Jiajie Qi¹, Yuxuan Wang¹, Zhe Zhang¹^,²^,³^,⁴^,^*()

1 Graduate School of Liaoning University of Traditional Chinese Medicine, Shenyang 110847, Liaoning, China
2 Innovation Engineering Technology Center of Traditional Chinese Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, Liaoning, China
3 Key Laboratory of the Theory and Application of Viscera in Chinese Medicine, Ministry of Education, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, Liaoning, China
4 Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110033, Liaoning, China

Received:2023-04-25 Revised:2023-05-18 Accepted:2023-06-05 Online:2023-11-04 Published:2023-11-04
Contact: Zhe Zhang

摘要/Abstract

摘要：

本文基于生物信息学、网络药理学与单细胞测序探讨红花-丹参治疗冠心病(CAD)的机制。采用Comparative Toxicogenomics Database、Swiss target prediction、Binding Database Home、TargetNet四个数据库筛选出羟基红花黄色素、丹参酮、丹酚酸、丹参素的靶向基因。基于GEO数据库所筛选的3个CAD数据集, 通过差异分析及加权基因共表达网络(WGCNA)筛选与CAD相关的基因。融合药物与疾病基因, 通过cytoscape中mcode插件筛选出药物-疾病调控网络核心亚群。使用ssGSEA算法分析38种免疫细胞与mcode核心亚群基因的浸润情况。使用单细胞RNA测序明确mcode核心亚群所分布的细胞亚群。最后, 分别对药物靶点基因、疾病基因以及mcode核心亚群基因进行GO、KEGG富集分析。结果发现, 四个数据库共得到485个药物靶点基因。通过差异分析及WGCNA得到617个疾病基因。mcode插件获得1个药物-疾病的核心调控亚群, 包括99个基因。ssGSEA算法结果表明TGFβ家族成员、趋化因子、白介素受体可能是红花-丹参治疗CAD核心调控的免疫细胞。单细胞RNA测序结果表明巨噬细胞与单核细胞可能是核心细胞亚群。GO、KEGG富集分析结果表明内膜结合的细胞器与细胞核可能是核心细胞成分。红花-丹参可能通过调控TGFβ家族成员、趋化因子、白介素受体、巨噬细胞、单核细胞、内膜结合的细胞器与细胞核发挥治疗CAD的作用。

关键词: 红花-丹参, 冠状动脉粥样硬化性心脏病, 生物信息学, 单细胞测序, 网络药理学

Abstract:

In the present study, we investigated the mechanism of safflower-danshen treatment for coronary artery disease (CAD) through bioinformatics, network pharmacology, and single-cell sequencing. To identify target genes for hydroxy safflower yellow pigment, tanshinone, danshinolic acid, and tanshinol, we employed various databases, including Comparative Toxicogenomics Database, Swiss target prediction, Binding Database Home, and TargetNet. By analyzing three CAD datasets obtained from the GEO database, we identified CAD-associated genes using differential analysis and a weighted gene coexpression network analysis (WGCNA). We integrated drug-disease genes and utilized the mcode plugin in Cytoscape to identify core subgroups in the drug-disease regulatory networks. The infiltration of 38 immune cells/functions was assessed using the ssGSEA algorithm. Additionally, single-cell RNA sequencing (SCS) was employed to examine the distribution of cellular subpopulations within the mcode regulatory network. Finally, we performed GO and KEGG enrichment analyses for drug target genes, disease genes, and mcode core subpopulation genes. Our results revealed 485 drug target genes obtained from four databases. Through differential analysis and WGCNA, we identified 617 disease genes associated with CAD. The mcode plugin analysis yielded a drug-disease core regulatory subpopulation consisting of 99 genes. The ssGSEA algorithm indicated that members of the TGFβ family, chemokines, and interleukin receptors might play key roles in the immune regulation of safflower-danshen treatment for CAD. Furthermore, SCS results suggested that macrophages and monocytes might be core cell subpopulations within this context. GO and KEGG enrichment analyses highlighted endosome-bound organelles and nuclei as potentially important cellular components. In conclusion, safflower-danshen treatment for CAD might exert its therapeutic effects by modulating TGFβ family members, chemokines, interleukin receptors, macrophages, monocytes, endosome-bound organelles, and nuclei.

Key words: Safflower-danshen, Coronary atherosclerotic heart disease, Bioinformatics, Single-cell sequencing, Network pharmacology

Supporting:

魏东升, 刘孝生, 李路珍, 齐佳杰, 王雨轩, 张哲. 基于综合生物信息学和单细胞测序方法揭示红花-丹参治疗冠心病的生物学和免疫学机制[J]. 中国药学(英文版), 2023, 32(10): 796-812.

Dongsheng Wei, Xiaosheng Liu, Luzhen Li, Jiajie Qi, Yuxuan Wang, Zhe Zhang. Unraveling the biological and immunological mechanisms of safflower-danshen in the treatment of coronary atherosclerotic heart disease: a comprehensive bioinformatics and single-cell sequencing approach[J]. Journal of Chinese Pharmaceutical Sciences, 2023, 32(10): 796-812.

图/表 11

Figure 1. Flowchart of the current study.

Table 1. Descriptive statistics for the GEO dataset.

Figure 2. Plots of drug DO enrichment analysis results. (A) A bar graph of DO enrichment analysis results sorted by P-value; (B) A bubble graph of DO enrichment analysis results sorted by the number of enriched genes.

Figure 3. Differential gene volcano map and heat map. (A) Volcano plot for DEGs. Red dots indicate upregulated DEGs, and blue dots express downregulated DEGs; (B) Heatmap for DEGs. Each line symbolizes one DEG, and each row represents one specimen. The yellow and blue swatches represent upregulated and downregulated DEGs, respectively.

Figure 4. A plot of CAD plus cluster gene coexpression results. (A) Gene tree of the gene coexpression modules; (B) Heat map of the association of the modules with CAD. The blue module was significantly associated with CAD; numbers in and out of brackets indicate P-values and correlation coefficients, respectively; (C) Correlation plot of genes contained in the blue module MM (X-axis) versus GS (Y-axis); (D) Venn diagram of overlapping genes between DEGs and genes in the blue module.

Figure 5. Drug-disease mcode network map and keycluster gene differential box plot. (A) Mcode network of CGs fused to drug-targeted genes; purple represents disease genes, blue represents drug-targeted genes; (B) Core subgroups of the mcode network; purple represents disease genes, blue represents drug-targeted genes; (C) Variability of keycluster genes in normal and CAD samples, ns represents P > 0.05, *Represents P < 0.05, **Represents P < 0.01, ***Represents P < 0.001.

Figure 6. ssGSEA immune infiltration results. (A) Box plot of the difference in infiltration of 38 immune cells between CAD samples and normal samples; (B) Heat map of the correlation between keycluster genes and 38 immune cells; Yellow represents high correlation, blue represents correlation is low, white represents no correlation; ***Represents P < 0.001, **Represents P < 0.01, *Represents P < 0.05.

Table 2. Keycluster gene to immune cell correlation ratios, "–" represents negative correlation, "+" represents positive correlation. The number of correlations was limited to regions where the difference was significant (P < 0.05).

Figure 7. A plot of CAD SCS results. (A) 20 cell clusters labeled by Uniform Manifold Approximation and Projection (UMAP); (B) Six cell types clustered by 20 cell clusters; (C) Degree of aggregation of keycluster genes in cell subpopulations.

Figure 8. GO and KEGG enrichment analysis histogram, from top to bottom, drug targeted genes, disease genes, and keycluster genes. (A) BP enrichment histogram; (B) CC enrichment histogram; (C) MF enrichment histogram; (D) KEGG enrichment histogram.

Figure 9. RORA, FN1, and FYN levels in the control, model, and TCM groups. ###Represents P < 0.001 compared to the blank group; ***Represents P < 0.001 compared to the model group; nsRepresents P > 0.05 compared to the model group.

参考文献 39

[1]	Leigh, J.A.; Alvarez, M.; Rodriguez, C.J. Ethnic minorities and coronary heart disease: an update and future directions. Curr. Atheroscler. Rep. 2016, 18, 9.
[2]	Kanu, J.S.; Gu, Y.L.; Zhi, S.; Yu, M.X.; Lu, Y.P.; Cong, Y.T.; Liu, Y.K.; Li, Y.; Yu, Y.Q.; Cheng, Y.; Liu, Y.W. Single nucleotide polymorphism rs3774261 in the AdipoQ gene is associated with the risk of coronary heart disease (CHD) in Northeast Han Chinese population: a case-control study. Lipids Health Dis. 2016, 15, 6.
[3]	Fidler, M.M.; Gupta, S.; Soerjomataram, I.; Ferlay, J.; Steliarova-Foucher, E.; Bray, F. Cancer incidence and mortality among young adults aged 20-39 years worldwide in 2012: a population-based study. Lancet Oncol. 2017, 18, 1579–1589.
[4]	Zhao, Y.L.; Wang, Y.Y.; Zhao, J.; Zhang, Z.H.; Jin, M.P.; Zhou, F.; Jin, C.; Zhang, J.; Xing, J.L.; Wang, N.; He, X.L.; Ren, T.T. PDE2 inhibits PKA-mediated phosphorylation of TFAM to promote mitochondrial Ca²⁺-induced colorectal cancer growth. Front. Oncol. 2021, 11, 663778.
[5]	Marija, V.; Sasko, K. Effects of high intensity statin therapy in the treatment of diabetic dyslipidemia in patients with coronary artery disease. Curr. Pharm. Des. 2018, 24, 427–441.
[6]	Sun, M.H.; Zhang, Y.; Xiao, Y.; Zhuang, Y. Relationship between different types of coronary heart disease and pathogenesis and syndrome differentiation of traditional Chinese medicine. Chin. J. Integr. Med. Cardio Cerebrovasc. Dis. 2017, 15, 507–508.
[7]	Liao, H.; Li, Y.P.; Zhai, X.R.; Zheng, B.; Banbury, L.; Zhao, X.Y.; Li, R.S. Comparison of inhibitory effects of safflower decoction and safflower injection on protein and mRNA expressions of iNOS and IL-1 β in LPS-activated RAW_264.7 cells. J. Immunol. Res. 2019, 2019, 1018274.
[8]	Kim, J.H.; Park, G.Y.; Bang, S.Y.; Park, S.Y.; Bae, S.K.; Kim, Y. Crocin suppresses LPS-stimulated expression of inducible nitric oxide synthase by upregulation of heme oxygenase-1 via calcium/calmodulin-dependent protein kinase 4. Med. Inflamm. 2014, 2014, 728709.
[9]	Ma, Z.Y.; Li, C.X.; Qiao, Y.; Lu, C.T.; Li, J.K.; Song, W.; Sun, J.; Zhai, X.H.; Niu, J.; Ren, Q.; Wen, A.D. Safflower yellow B suppresses HepG2 cell injury induced by oxidative stress through the AKT/Nrf2 pathway. Int. J. Mol. Med. 2016, 37, 603–612.
[10]	Pu, Z.J.; Yue, S.J.; Zhou, G.S.; Yan, H.; Shi, X.Q.; Zhu, Z.H.; Huang, S.L.; Peng, G.P.; Chen, Y.Y.; Bai, J.Q.; Wang, X.P.; Su, S.L.; Tang, Y.P.; Duan, J.A. The comprehensive evaluation of safflowers in different producing areas by combined analysis of color, chemical compounds, and biological activity. Molecules. 2019, 24, 3381.
[11]	Wang, C.; Liu, L.M.; Song, Z.Q.; Dong, Y.Z.; Du, Z.Y.; Ning, Z.C.; Liu, Y.Y.; Liu, Z.L. Survey of active components in commonly-used Chinese materia medica injections and related Chinese materia medica for cardiovascular disease. Chin. Tradit. Herb. Drugs. 2015, 46, 2315–2328.
[12]	Nyberg, A.G.; Stricklin, D.; Sellström, Å. Mass casualties and health care following the release of toxic chemicals or radioactive material: contribution of modern biotechnology. Int. J. Environ. Res. Public Health. 2011, 8, 4521–4549.
[13]	Ye, F.; Huang, W.T.; Guo, G.J. Studying hematopoiesis using single-cell technologies. J. Hematol. Oncol. 2017, 10, 27.
[14]	Treppner, M.; Salas-Bastos, A.; Hess, M.; Lenz, S.; Vogel, T.; Binder, H. Synthetic single cell RNA sequencing data from small pilot studies using deep generative models. Sci. Rep. 2021, 11, 9403.
[15]	Pan, B.Y.; Wang, Y.; Wu, C.N.; Jia, J.R.; Huang, C.; Fang, S.B.; Liu, L.R. A mechanism of action study on Danggui Sini Decoction to discover its therapeutic effect on gastric cancer. Front. Pharmacol. 2021, 11, 592903.
[16]	Omidi, F.; Hosseini, S.A.; Ahmadi, A.; Hassanzadeh, K.; Rajaei, S.; Cesaire, H.M.; Hosseini, V. Discovering the signature of a lupus-related microRNA profile in the Gene Expression Omnibus repository. Lupus. 2020, 29, 1321–1335.
[17]	Hao, Y.C.; Liu, J.; Liu, J.; Yang, N.; Smith, S.C.; Jr, Huo, Y.; Fonarow, G.C.; Ge, J.B.; Taubert, K.A.; Morgan, L.; Zhou, M.G.; Xing, Y.Y.; Ma, C.S.; Han, Y.L.; Zhao, D. Sex differences in In-hospital management and outcomes of patients with acute coronary syndrome. Circulation. 2019, 139, 1776–1785.
[18]	Zhang, Z.Y.; Chen, J.W.; Tang, W.T.; Feng, Q.Y.; Xu, J.M.; Ren, L. Comprehensive analysis reveals the potential regulatory mechanism between ub–proteasome system and cell cycle in colorectal cancer. Front. Cell Dev. Biol. 2021, 9, 653528.
[19]	Chen, R.X.; Xiao, Y.; Chen, M.H.; He, J.Y.; Huang, M.T.; Hong, X.T.; Liu, X.; Fu, T.R.; Zhang, J.Z.; Chen, L.G. A traditional Chinese medicine therapy for coronary heart disease after percutaneous coronary intervention: a meta-analysis of randomized, double-blind, placebo-controlled trials. Biosci. Rep. 2018, 38, BSR20180973.
[20]	Biemmi, V.; Milano, G.; Ciullo, A.; Cervio, E.; Burrello, J.; Dei Cas, M.; Paroni, R.; Tallone, T.; Moccetti, T.; Pedrazzini, G.; Longnus, S.; Vassalli, G.; Barile, L. Inflammatory extracellular vesicles prompt heart dysfunction via TRL4-dependent NF-κB activation. Theranostics. 2020, 10, 2773–2790.
[21]	Fang, X.; Ni, N.; Gao, Y.; Vincent, D.F.; Bartholin, L.; Li, Q.L. A novel mouse model of testicular granulosa cell tumors. Mol. Hum. Reprod. 2018, 24, 343–356.
[22]	Cosselman, K.E.; Navas-Acien, A.; Kaufman, J.D. Environmental factors in cardiovascular disease. Nat. Rev. Cardiol. 2015, 12, 627–642.
[23]	Hu, C.X.; Huang, S.; Wu, F.F.; Ding, H. MicroRNA‑219‑5p participates in cyanotic congenital heart disease progression by regulating cardiomyocyte apoptosis. Exp. Ther. Med. 2020, 21, 36.
[24]	Wang, B.Y.; Wu, W.J.; Lu, H.J.; Wang, Z.; Xin, H.L. Enhanced anti-tumor of pep-1 modified superparamagnetic iron oxide/PTX loaded polymer nanoparticles. Front. Pharmacol. 2019, 9, 1556.
[25]	Ofengeim, D.; Ito, Y.; Najafov, A.; Zhang, Y.Y.; Shan, B.; DeWitt, J.P.; Ye, J.Y.; Zhang, X.M.; Chang, A.S.; Vakifahmetoglu-Norberg, H.; Geng, J.F.; Py, B.; Zhou, W.; Amin, P.; Lima, J.B.; Qi, C.T.; Yu, Q.; Trapp, B.; Yuan, J.Y. Activation of necroptosis in multiple sclerosis. Cell Rep. 2015, 10, 1836–1849.
[26]	Chien, Y.; Chien, C.S.; Chiang, H.C.; Huang, W.L.; Chou, S.J.; Chang, W.C.; Chang, Y.L.; Leu, H.B.; Chen, K.H.; Wang, K.L.; Lai, Y.H.; Liu, Y.Y.; Lu, K.H.; Li, H.Y.; Sung, Y.J.; Jong, Y.J.; Chen, Y.J.; Chen, C.H.; Yu, W.C. Interleukin-18 deteriorates Fabry cardiomyopathy and contributes to the development of left ventricular hypertrophy in Fabry patients with GLA IVS4⁺919 G>A mutation. Oncotarget. 2016, 7, 87161–87179.
[27]	Li, W.Z.; Chen, S.W.; Zhou, G.Q.; Li, H.L.; Zhong, L.; Liu, S.W. Potential role of cyanidin 3-glucoside (C3G) in diabetic cardiomyopathy in diabetic rats: an in vivo approach. Saudi J. Biol. Sci. 2018, 25, 500–506.
[28]	Lu, J.; Cong, T.X.; Wen, X.; Li, X.X.; Du, D.; He, G.; Jiang, X. Salicylic acid treats acne vulgaris by suppressing AMPK/SREBP1 pathway in sebocytes. Exp. Dermatol. 2019, 28, 786–794.
[29]	Suárez, A.L.; Feramisco, J.D.; Koo, J.; Steinhoff, M. Psychoneuroimmunology of psychological stress and atopic dermatitis: pathophysiologic and therapeutic updates. Acta Derm. Venereol. 2012, 92, 7–15.
[30]	Kashem, S.W.; Haniffa, M.; Kaplan, D.H. Antigen-presenting cells in the skin. Annu. Rev. Immunol. 2017, 35, 469–499.
[31]	Scott, C.L.; Henri, S.; Guilliams, M. Mononuclear phagocytes of the intestine, the skin, and the lung. Immunol. Rev. 2014, 262, 9–24.
[32]	Malissen, B.; Tamoutounour, S.; Henri, S. The origins and functions of dendritic cells and macrophages in the skin. Nat. Rev. Immunol. 2014, 14, 417–428.
[33]	Kayama, Y.; Raaz, U.; Jagger, A.; Adam, M.; Schellinger, I.N.; Sakamoto, M.; Suzuki, H.; Toyama, K.; Spin, J.M.; Tsao, P.S. Diabetic cardiovascular disease induced by oxidative stress. Int. J. Mol. Sci. 2015, 16, 25234–25263.
[34]	Jiang, Z.; Hu, Z.P.; Zeng, L.W.; Lu, W.; Zhang, H.N.; Li, T.; Xiao, H. The role of the Golgi apparatus in oxidative stress: is this organelle less significant than mitochondria? Free. Radic. Biol. Med. 2011, 50, 907–917.
[35]	Wang, J.H.; Lu, T.J.; Kung, M.L.; Yang, Y.F.; Yeh, C.Y.; Tu, Y.T.; Chen, W.S.; Tsai, K.W. The long noncoding RNA LOC441461 (STX17-AS1) modulates colorectal cancer cell growth and motility. Cancers. 2020, 12, 3171.
[36]	Mao,Q.; Liang,X.L.; Wu,Y.F.; Lu,Y.X. Resveratrol Attenuates Cardiomyocyte Apoptosis in Rats Induced by Coronary Microembolization Through SIRT1-Mediated Deacetylation of p53. J. Cardiovasc. Pharmacol. Ther. 2019, 24, 551–558.
[37]	Liu, L.L.; Lin, L.R.; Lu, C.X.; Fu, J.G.; Chao, P.L.; Jin, H.W.; Zhang, Z.Y.; Yang, T.C. Expression of inflammatory and apoptosis factors following coronary stent implantation in coronary heart disease patients. Int. Immunopharmacol. 2011, 11, 1850–1854.
[38]	Silwal, P.; Kim, J.K.; Kim, Y.J.; Jo, E.K. Mitochondrial reactive oxygen species: double-edged weapon in host defense and pathological inflammation during infection. Front. Immunol. 2020, 11, 1649.
[39]	Correa, B.L.; El Harane, N.; Gomez, I.; Hocine, H.R.; Vilar, J.; Desgres, M.; Bellamy, V.; Keirththana, K.; Guillas, C.; Perotto, M.; Pidial, L.; Alayrac, P.; Tran, T.; Tan, S.; Hamada, T.; Charron, D.; Brisson, A.; Renault, N.; Al-Daccak, R.; Menasché, P.; Silvestre, J. Extracellular vesicles from human cardiovascular progenitors trigger a reparative immune response in infarcted hearts. Cardiovasc. Res. 2021, 117, 292–307.

基于综合生物信息学和单细胞测序方法揭示红花-丹参治疗冠心病的生物学和免疫学机制

Unraveling the biological and immunological mechanisms of safflower-danshen in the treatment of coronary atherosclerotic heart disease: a comprehensive bioinformatics and single-cell sequencing approach

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