Omics in Quality Research of Chinese Medicines

1.

Z.T. Li, F.X. Zhang, C.L. Fan, M.N. Ye, W.W. Chen, Z.H. Yao, X.S. Yao, Y. Dai, Discovery of potential Q-marker of traditional Chinese medicine based on plant metabolomics and network pharmacology: Periplocae Cortex as an example. Phytomedicine 85, 153535 (2021). https://doi.org/10.1016/j.phymed.2021.153535CrossRefPubMed

2.

W.Z. Yang, Y.B. Zhang, W.Y. Wu, L.Q. Huang, D. Guo, C.X. Liu, Approaches to establish Q-markers for the quality standards of traditional Chinese medicines. Acta Pharma. Sin. B 7, 439–446 (2017). https://doi.org/10.1016/j.apsb.2017.04.012CrossRef

3.

Y.Q. Li, D.X. Kong, Y. Fu, M.R. Sussman, H. Wu, The effect of developmental and environmental factors on secondary metabolites in medicinal plants. Plant Physiol. Bioch. 148, 80–89 (2020). https://doi.org/10.1016/j.plaphy.2020.01.006CrossRef

4.

J.Y. Liu, A.L. Liu, F.Y. Mao, Y.S. Zhao, Z. Cao, N.N. Cen, S.Q. Li, L.F. Li, X.F. Ma, H. Sui, Determination of the active ingredients and biopotency in Polygala tenuifolia Willd. and the ecological factors that influence them. Ind. Crop. Prod. 134, 113–123 (2019). https://doi.org/10.1016/j.indcrop.2019.03.074

5.

Y.M. Wang, Y.B. Li, X.X. Zhang, Y.Y. Xu, H. Wang, Y.J. Zhang, Exploring processing adjuvants influence on traditional Chinese medicine compatibility of Aconiti Radix Cocta and Pinelliae rhizome using rapid resolution liquid chromatography-quadrupole time-of-flight mass spectrometry. Pharmacogn. Mag. 10, 483–490 (2014). https://doi.org/10.4103/0973-1296.141771CrossRefPubMedPubMedCentral

6.

S.P. Li, F.Q. Yang, K.W.K. Tsim, Quality control of Cordyceps sinensis, a valued traditional Chinese medicine. J. Pharmaceut. Biomed. Anal. 41, 1571–1584 (2006). https://doi.org/10.1016/j.jpba.2006.01.046CrossRef

7.

Y.Q. Mei, L.F. Wei, M.X. Tan, C.C. Wang, L.S. Zou, J.L. Chen, Z.C. Cai, S.X. Yin, F.R. Zhang, C.X. Shan, X.H. Liu, Qualitative and quantitative analysis of the major constituents in Spatholobi Caulis by UFLC-Triple TOF-MS/MS and UFLC-QTRAP-MS/MS. J. Pharmaceut. Biomed. Anal. 194, 113803 (2021). https://doi.org/10.1016/j.jpba.2020.113803CrossRef

8.

P.S. Xie, A.Y. Leung, Understanding the traditional aspect of Chinese medicine in order to achieve meaningful quality control of Chinese materia medica. J. Chromatogr. AChromatogr. A 1216, 1933–1940 (2009). https://doi.org/10.1016/j.chroma.2008.08.045CrossRef

9.

B. Yang, Y. Wang, L.L. Shan, J.T. Zou, Y.Y. Wu, F.F. Yang, Y.N. Zhang, Y.B. Li, Y.J. Zhang, A novel and practical chromatographic “Fingerprint-ROC-SVM” strategy applied to quality analysis of traditional Chinese medicine injections: using KuDieZi injection as a case study. Molecules 22, 1237 (2017). https://doi.org/10.3390/molecules22071237CrossRefPubMedPubMedCentral

10.

D.Z. Yang, T.Q. An, X.L. Jiang, D.Q. Tang, Y.Y. Gao, H.T. Zhao, X.W. Wu, Development of a novel method combining HPLC fingerprint and multi-ingredients quantitative analysis for quality evaluation of traditional Chinese medicine preparation. Talanta 85, 885–890 (2011). https://doi.org/10.1016/j.talanta.2011.04.059CrossRefPubMed

11.

H. Zhang, J.A. Wang, Y. Chen, X.W. Shen, H.J. Jiang, X.C. Gong, J.Z. Yan, Establishing the chromatographic fingerprint of traditional Chinese medicine standard decoction based on quality by design approach: a case study of Licorice. J. Sep. Sci. 42, 1144–1154 (2019). https://doi.org/10.1002/jssc.201800989CrossRefPubMed

12.

Y. Qin, J.B. Wang, Y.L. Zhao, L.M. Shan, B.C. Li, F. Fang, C. Jin, X.H. Xiao, Establishment of a bioassay for the toxicity evaluation and quality control of Aconitum herbs. J. Hazard. Mater. 199, 350–357 (2012). https://doi.org/10.1016/j.jhazmat.2011.11.029CrossRefPubMed

13.

Y. Han, H. Sun, A.H. Zhang, G.L. Yan, X.J. Wang, Chinmedomics, a new strategy for evaluating the therapeutic efficacy of herbal medicines. Pharmacol. Therapeut. 216, 107680 (2020). https://doi.org/10.1016/j.pharmthera.2020.107680CrossRef

14.

X.Y. Han, H. Xiao, J. Chen, W.Q. Xu, D.Q. Dou, The substance basis of Poria ameliorates hypothyroidism other than hyperthyroidism based on proteomics and metabolomics. FASEB J. 34, 11970–11982 (2020). https://doi.org/10.1096/fj.202000984RCrossRefPubMed

15.

T.F. Xu, Z.F. Pi, S. Liu, F.R. Song, Z.Q. Liu, Chemical profiling combined with “Omics” technologies (CP-Omics): a strategy to understand the compatibility mechanisms and simplify herb formulas in traditional Chinese medicines. Phytochem. Anal. 28, 381–391 (2017). https://doi.org/10.1002/pca.2685CrossRefPubMed

16.

L. Liu, H. Wang, The recent applications and developments of bioinformatics and Omics technologies in traditional Chinese medicine. Curr. Bioinform. 14, 200–210 (2019). https://doi.org/10.2174/1574893614666190102125403CrossRef

17.

T.Y. Xin, Y. Zhang, X.D. Pu, R.R. Gao, Z.C. Xu, J.Y. Song, Trends in herbgenomics. Sci. China Life Sci. 62, 288–308 (2019). https://doi.org/10.1007/s11427-018-9352-7CrossRefPubMed

18.

H.Y. Hu, X.F. Shen, B.S. Liao, L. Luo, J. Xu, S.L. Chen, Herbgenomics: a stepping stone for research into herbal medicine. Sci. China Life Sci. 62, 913–920 (2019). https://doi.org/10.1007/s11427-018-9472-yCrossRefPubMed

19.

T.C. Suo, H.X. Wang, Z. Li, Application of proteomics in research on traditional Chinese medicine. Expert Rev. Proteomics 13, 873–881 (2016). https://doi.org/10.1080/14789450.2016.1220837CrossRefPubMed

20.

R. Guo, X.L. Luo, J.J. Liu, L. Liu, X.J. Wang, H.T. Lu, Omics strategies decipher therapeutic discoveries of traditional Chinese medicine against different diseases at multiple layers molecular-level. Pharmacol. Res. 152, 104627 (2020). https://doi.org/10.1016/j.phrs.2020.104627CrossRefPubMed

21.

Y. Gu, G.S. Wu, H.K. Li, W.D. Zhang, Strategy of systems biology for visualizing the “Black box” of traditional Chinese medicine. World J. Tradit. Chin. Med. 6, 260–270 (2020). https://doi.org/10.4103/wjtcm.wjtcm_31_20CrossRef

22.

X.Q. Hu, S.B. Su, An overview of epigenetics in Chinese medicine researches. Chin. J. Integr. Med. 23, 714–720 (2017). https://doi.org/10.1007/s11655-016-2274-yCrossRefPubMed

23.

J. Li, P. Yang, Q.H. Yang, X.W. Gong, H.C. Ma, K. Dang, G.H. Chen, X.L. Gao, B.L. Feng, Analysis of flavonoid metabolites in buckwheat leaves using UPLC-ESI-MS/MS. Molecules 24, 1310 (2019). https://doi.org/10.3390/molecules24071310CrossRefPubMedPubMedCentral

24.

X.T. Lin, S.S. Liang, Q.H. Wang, W.C. Duanmu, H.X. Kuang, Metagenomics approach the intestinal microbiome structure and function in the anti-H1N1 of a traditional Chinese medicine acid polysaccharide. Microb. Pathogenesis 147, 104351 (2020). https://doi.org/10.1016/j.micpath.2020.104351CrossRef

25.

J. Qian, H.B. Xu, J.Y. Song, J. Xu, Y.J. Zhu, S.L. Chen, Genome-wide analysis of simple sequence repeats in the model medicinal mushroom Ganoderma lucidum. Gene 512, 331–336 (2013). https://doi.org/10.1016/j.gene.2012.09.127CrossRefPubMed

26.

D.X. Chen, Y. Pan, Y. Wang, Y.Z. Cui, Y.J. Zhang, R.Y. Mo, X.l. Wu, J. Tan, J. Zhang, L.A. Guo, X. Zhao, W.K. Jiang, T.L. Sun, X.D. Hu, L.Y. Li, The chromosome-level reference genome of Coptis chinensis provides insights into genomic evolution and berberine biosynthesis. Hortic. Res. 8, 121 (2021). https://doi.org/10.1038/s41438-021-00559-2

27.

B.S. Liao, H.Y. Hu, S.M. Xiao, G.R. Zhou, W. Sun, Y. Chu, X.X. Meng, J.H. Wei, H. Zhang, J. Xu, S.L. Chen, Global Pharmacopoeia Genome database is an integrated and mineable genomic database for traditional medicines derived from eight international pharmacopoeias. Sci. China Life Sci. (2021). https://doi.org/10.1007/s11427-021-1968-7CrossRefPubMedPubMedCentral

28.

Y.D. Yang, L.Y. Huang, C.Y. Xu, L. Qi, Z.Y. Wu, J. Li, H.X. Chen, Y. Wu, T. Fu, H. Zhu, M.A. Saand, J. Li, L.Y. Liu, H.K. Fan, H.Q. Zhou, W.Q. Qin, Chromosome-scale genome assembly of areca palm (Areca catechu). Mol. Ecol. Resour. 21, 2504–2519 (2021). https://doi.org/10.1111/1755-0998.13446CrossRefPubMed

29.

S.L. Chen, J. Xu, C. Liu, Y.J. Zhu, D.R. Nelson, S.G. Zhou, C.F. Li, L.Z. Wang, X. Guo, Y.Z. Sun, H.M. Luo, Y. Li, J.Y. Song, B. Henrissat, A. Levasseur, J. Qian, J.Q. Li, X. Luo, L.C. Shi, L. He, L. Xiang, X.L. Xu, Y.Y. Niu, Q.S. Li, M.V. Han, H.X. Yan, J. Zhang, H.M. Chen, A.P. Lv, Z. Wang, M.Z. Liu, D.C. Schwartz, C. Sun, Genome sequence of the model medicinal mushroom Ganoderma lucidum. Nat. Commun. 3, 913 (2012). https://doi.org/10.1038/ncomms1923CrossRefPubMed

30.

W. Chen, L. Kui, G.H. Zhang, S.S. Zhu, J. Zhang, X. Wang, M. Yang, H.C. Huang, Y.X. Liu, Y. Wang, Y.H. Li, L.P. Zeng, W. Wang, X.H. He, Y. Dong, S.C. Yang, Whole-genome sequencing and analysis of the Chinese Herbal Plant Panax notoginseng. Mol. Plant 10, 899–902 (2017). https://doi.org/10.1016/j.molp.2017.02.010CrossRefPubMed

31.

J. Qian, J.Y. Song, H.H. Gao, Y.J. Zhu, J. Xu, X.H. Pang, H. Yao, C. Sun, X.E. Li, C.Y. Li, J.A. Liu, H.B. Xu, S.L. Chen, The complete chloroplast genome sequence of the medicinal plant salvia miltiorrhiza. Plos One 8, e57607 (2013). https://doi.org/10.1371/journal.pone.0057607CrossRefPubMedPubMedCentral

32.

Q. Shen, L.D. Zhang, Z.H. Liao, S.Y. Wang, T.X. Yan, P. Shi, M. Liu, X.Q. Fu, Q.F. Pan, Y.L. Wang, Z.Y. Lv, X. Lu, F.Y. Zhang, W.M. Jiang, Y.N. Ma, M.H. Chen, X.L. Hao, L. Li, Y.L. Tang, G. Lv, Y. Zhou, X.F. Sun, P.E. Brodelius, J.K.C. Rose, K.X. Tang, The genome of artemisia annua provides insight into the evolution of asteraceae family and artemisinin biosynthesis. Mol. Plant 11, 776–788 (2018). https://doi.org/10.1016/j.molp.2018.03.015CrossRefPubMed

33.

C. Song, Y.F. Liu, A.P. Song, G.Q. Dong, H.B. Zhao, W. Sun, S. Ramakrishnan, Y. Wang, S.B. Wang, T.Z. Li, Y. Niu, J.F. Jiang, B. Dong, Y. Xia, S.M. Chen, Z.G. Hu, F.D. Chen, S.L. Chen, The chrysanthemum nankingense genome provides insights into the evolution and diversification of chrysanthemum flowers and medicinal traits. Mol. Plant 11, 1482–1491 (2018). https://doi.org/10.1016/j.molp.2018.10.003CrossRefPubMed

34.

Y.B. Zhao, J.L. Yin, H.Y. Guo, Y.Y. Zhang, W. Xiao, C. Sun, J.Y. Wu, X.B. Qu, J. Yu, X.M. Wang, J.F. Xiao, The complete chloroplast genome provides insight into the evolution and polymorphism of Panax ginseng. Front. Plant Sci. 5, 696 (2015). https://doi.org/10.3389/fpls.2014.00696CrossRefPubMedPubMedCentral

35.

T. Liu, L.L. Guo, Y.L. Pan, Q. Zhao, J.H. Wang, Z.Q. Song, Construction of the first high-density genetic linkage map of Salvia miltiorrhiza using specific length amplified fragment (SLAF) sequencing. Sci. Rep. 6, 24070 (2016). https://doi.org/10.1038/srep24070CrossRefPubMedPubMedCentral

36.

X.Y. Zhao, Q. Wang, F.Y. Wang, Y. Li, Characterization of the complete chloroplast genome of Euphorbia lathyris (Euphorbiaceae), a traditional Chinese medicine. Mitochondrial DNA B 5, 2129–2130 (2020). https://doi.org/10.1080/23802359.2020.1768169CrossRef

37.

Y.F. Liu, N. Ai, J. Liao, X.H. Fan, Transcriptomics: a sword to cut the Gordian knot of traditional Chinese medicine. Biomark. Med. 9, 1201–1213 (2015). https://doi.org/10.2217/bmm.15.91CrossRefPubMed

38.

H.B. Liu, J.H. Shi, M.K. Wu, D.L. Xu, The application and future prospect of RNA-Seq technology in Chinese medicinal plants. J. Appl. Res. Med. Aroma. Plants 24, 100318 (2021). https://doi.org/10.1016/j.jarmap.2021.100318CrossRef

39.

S.N. Wang, H.H. Chen, Y.F. Zheng, Z.Y. Li, B.P. Cui, P. Zhao, J.L. Zheng, R. Lu, N. Sun, Transcriptomics- and metabolomics-based integration analyses revealed the potential pharmacological effects and functional pattern of in vivo Radix Paeoniae Alba administration. Chin. Med. 15, 52 (2020). https://doi.org/10.1186/s13020-020-00330-0CrossRefPubMedPubMedCentral

40.

Y. Quan, Z.Y. Wang, M. Xiong, Z.T. Xiao, H.Y. Zhang, Dissecting traditional Chinese medicines by omics and bioinformatics. Nat. Prod. Commun. 9, 1391–1396 (2014). https://doi.org/10.1177/1934578X1400900942CrossRefPubMed

41.

S. Gao, Y.P. Ren, Y. Suna, Z.F. Wu, J.S. Ruan, B.J. He, T. Zhang, X. Yua, X.X. Tian, W.J. Bua, PacBio full-length transcriptome profiling of insect mitochondrial gene expression. RNA Biol. 13, 1323–1323 (2016). https://doi.org/10.1080/15476286.2016.1237750CrossRef

42.

X. Gao, F.X. Guo, Y. Chen, G. Bai, Y.X. Liu, J. Jin, Q. Wang, Full-length transcriptome analysis provides new insights into the early bolting occurrence in medicinal Angelica sinensis. Sci. Rep. 11, 13000 (2021). https://doi.org/10.1038/s41598-021-92494-4CrossRefPubMedPubMedCentral

43.

Q. Ji, F.S. Zhu, X. Liu, Q. Li, S.B. Su, Recent advance in applications of proteomics technologies on traditional Chinese medicine research. Evid. Based Compl. Alt. Med. 2015, 983139 (2015). https://doi.org/10.1155/2015/983139

44.

J.Y. Duan, W.Y. Dong, L.J. Xie, S.M. Fan, Y.Y. Xu, Y.B. Li, Integrative proteomics-metabolomics strategy reveals the mechanism of hepatotoxicity induced by Fructus Psoraleae. J. Proteomics 221, 103767 (2020). https://doi.org/10.1016/j.jprot.2020.103767CrossRefPubMed

45.

X.M. Tang, J.L. Guo, L. Chen, P.C.L. Ho, Application for proteomics analysis technology in studying animal-derived traditional Chinese medicine: a review. J. Pharmaceut. Biomed. Anal. 191, 113609 (2020). https://doi.org/10.1016/j.jpba.2020.113609CrossRef

46.

K.L. Yang, L.T. Zeng, A.N. Ge, X.P. Pan, T.T. Bao, Z.Y. Long, Q.Z. Tong, M.X. Yuan, X.F. Zhu, J.N. Ge, Z.D. Huang, Integrating systematic biological and proteomics strategies to explore the pharmacological mechanism of danshen yin modified on atherosclerosis. J. Cell. Mol. Med. 24, 13876–13898 (2020). https://doi.org/10.1111/jcmm.15979CrossRefPubMedPubMedCentral

47.

Y.Z. Lao, X.Y. Wang, N.H. Xu, H.M. Zhang, H.X. Xu, Application of proteomics to determine the mechanism of action of traditional Chinese medicine remedies. J. Ethnopharmacol. 155, 1–8 (2014). https://doi.org/10.1016/j.jep.2014.05.022CrossRefPubMed

48.

Y.Y. Yang, F.Q. Yang, J.L. Gao, Differential proteomics for studying action mechanisms of traditional Chinese medicines. Chin. Med. 14, 1 (2019). https://doi.org/10.1186/s13020-018-0223-8CrossRefPubMedPubMedCentral

49.

W. Ye, W.M. Zhang, T.M. Liu, M.Z. Zhu, S.N. Li, H.H. Li, Z.L. Huang, X.X. Gao, ITRAQ-based quantitative proteomic analysis of chemically induced Aquilaria sinensis provides insights into agarwood formation mechanism. Proteomics 18, 1800023 (2018). https://doi.org/10.1002/pmic.201800023CrossRef

50.

H. Du, Y.F. Rao, R.H. Liu, K.S. Deng, Y.M. Guan, D.W. Luo, Q.P. Mao, J.W. Yu, T. Bo, Z.Q. Fan, H. Ouyang, Y.L. Feng, W.F. Zhu, Proteomics and metabolomics analyses reveal the full spectrum of inflammatory and lipid metabolic abnormalities in dyslipidemia. Biomed. Chromatogr. 35, e5183 (2021). https://doi.org/10.1002/bmc.5183CrossRefPubMed

51.

P.C. Wang, Q.H. Wang, B.Y. Yang, S. Zhao, H.X. Kuang, The progress of metabolomics study in traditional Chinese medicine research. Am. J. Chin. Med. 43, 1281–1310 (2015). https://doi.org/10.1142/s0192415x15500731CrossRefPubMed

52.

M. Wang, L. Chen, D. Liu, H. Chen, D.D. Tang, Y.Y. Zhao, Metabolomics highlights pharmacological bioactivity and biochemical mechanism of traditional Chinese medicine. Chem. Biol. Interact. 273, 133–141 (2017). https://doi.org/10.1016/j.cbi.2017.06.011CrossRefPubMed

53.

G.S. Wu, W.D. Zhang, H.K. Li, Application of metabolomics for unveiling the therapeutic role of traditional Chinese medicine in metabolic diseases. J. Ethnopharmacol. 242, 112057 (2019). https://doi.org/10.1016/j.jep.2019.112057CrossRefPubMed

54.

Q. Song, A.H. Zhang, G.L. Yan, L. Liu, X.J. Wang, Technological advances in current metabolomics and its application in tradition Chinese medicine. RSC Adv. 7, 53516–53524 (2017). https://doi.org/10.1039/c7ra02056bCrossRef

55.

L. Xu, Y.X. Zhang, P. Zhang, X.M. Dai, Y.Q. Gao, Y.T. Lv, S.Y. Qin, F.G. Xu, Integrated metabolomics and network pharmacology strategy driven active traditional chinese medicine ingredients discovery for the alleviation of Cisplatin Nephrotoxicity. Chem. Res. Toxicol. 32, 2411–2421 (2019). https://doi.org/10.1021/acs.chemrestox.9b00180CrossRefPubMed

56.

T.Y. Wang, J.J. Liu, X.L. Luo, L.L. Hu, H.T. Lu, Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds. Pharmacol. Therapeut. 224, 107824 (2021). https://doi.org/10.1016/j.pharmthera.2021.107824CrossRef

57.

J.S. Tian, G.J. Peng, Y.F. Wu, J.J. Zhou, H. Xiang, X.X. Gao, Y.Z. Zhou, X.M. Qin, G.H. Du, A GC-MS urinary quantitative metabolomics analysis in depressed patients treated with TCM formula of Xiaoyaosan. J. Chromatogr. B 1026, 227–235 (2016). https://doi.org/10.1016/j.jchromb.2015.12.026CrossRef

58.

G.S. Shan, L.X. Zhang, Q.M. Zhao, H.B. Xiao, R.J. Zhuo, G. Xu, H. Jiang, X.M. You, T.Z. Jia, Metabolomic study of raw and processed Atractylodes macrocephala Koidz by LC-MS. J. Pharmaceut. Biomed. Anal. 98, 74–84 (2014). https://doi.org/10.1016/j.jpba.2014.05.010CrossRef

59.

R. Ramautar, O.A. Mayboroda, G.W. Somsen, G.J. de Jong, CE-MS for metabolomics: developments and applications in the period 2008–2010. Electrophoresis 32, 52–65 (2011). https://doi.org/10.1002/elps.201000378CrossRefPubMed

60.

X.J. Wang, H. Sun, A.H. Zhang, W.J. Sun, P. Wang, Z.G. Wang, Potential role of metabolomics approaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine. J. Pharmaceut. Biomed. Anal. 55, 859–868 (2011). https://doi.org/10.1016/j.jpba.2011.01.042CrossRef

61.

Q. Yang, A.H. Zhang, J.H. Miao, H. Sun, Y. Han, G.L. Yan, F.F. Wu, X.J. Wang, Metabolomics biotechnology, applications, and future trends: a systematic review. RSC Adv. 9, 37245–37257 (2019). https://doi.org/10.1039/c9ra06697gCrossRefPubMedPubMedCentral

62.

Y.B. Li, Y.M. Wang, L.N. Su, L.X. Li, Y.J. Zhang, Exploring potential chemical markers by metabolomics method for studying the processing mechanism of traditional Chinese medicine using RPLC-Q-TOF/MS: a case study of Radix Aconiti. Chem. Cent. J. 7, 36 (2013). https://doi.org/10.1186/1752-153x-7-36CrossRefPubMedPubMedCentral

63.

J. Yang, X. Han, H.Y. Wang, J. Yang, Y. Kuang, K.Y. Ji, Y. Yang, K. Pang, S.X. Yang, J.C. Qin, H.P. Wang, Comparison of metabolomics of Dendrobium officinale in different habitats by UPLC-Q-TOF-MS. Biochem. Syst. Ecol. 89, 104007 (2020). https://doi.org/10.1016/j.bse.2020.104007CrossRef

64.

Y. Li, T. Pang, J.L. Shi, X.P. Lu, Y.P. Li, Q. Lin, Sample-specific metabolites library with retention Neighbor: an improved identification and quantitation strategy for gas chromatography-mass spectrometry-based metabolomics. J. Anal. Chem. 76, 844–853 (2021). https://doi.org/10.1134/S1061934821070108CrossRef

65.

M. Sorokina, C. Steinbeck, Review on natural products databases: where to find data in 2020. J. Cheminformatics 12, 20 (2020). https://doi.org/10.1186/s13321-020-00424-9CrossRef

66.

W.X. Li, A.H. Zhang, X.H. Zhou, Y. Nan, Q. Liu, H. Sun, H. Fang, X.J. Wang, High-throughput liquid chromatography mass-spectrometry-driven lipidomics discover metabolic biomarkers and pathways as promising targets to reveal the therapeutic effects of the Shenqi pill. RSC Adv. 10, 2347–2358 (2020). https://doi.org/10.1039/c9ra07621bCrossRefPubMedPubMedCentral

67.

Y.L. Hua, Q. Ma, X.S. Zhang, Y.Q. Jia, X.T. Peng, W.L. Yao, P. Ji, J.J. Hu, Y.M. Wei, Pulsatilla decoction can treat the dampness-heat Diarrhea rat model by regulating Glycerinphospholipid metabolism based lipidomics approach. Front. Pharmacol. 11, 197 (2020). https://doi.org/10.3389/fphar.2020.00197CrossRefPubMedPubMedCentral

68.

A.P. Li, L. Yang, T. Cui, L.C. Zhang, Y.T. Liu, Y. Yan, K. Li, X.M. Qin, Uncovering the mechanism of Astragali Radix against nephrotic syndrome by intergrating lipidomics and network pharmacology. Phytomedicine 77, 153274 (2020). https://doi.org/10.1016/j.phymed.2020.153274CrossRefPubMed

69.

J.J. Wang, L.F. Ding, J. Zhou, H.Y. Ma, Y.Y. Wu, J.J. Wang, X. Lv, S.J. Liu, H.B. Wang, Y.Q. Yan, N.C. Luo, Q. Li, H.Q. Xu, L.Q. Di, Q.A. Wu, J.N. Duan, Target lipidomics approach to reveal the resolution of inflammation induced by Chinese medicine combination in Liu-Shen-Wan against realgar overexposure to rats. J. Ethnopharmacol. 249, 112171 (2020). https://doi.org/10.1016/j.jep.2019.112171CrossRefPubMed

70.

X.F. Wu, Y.T. Zhang, J.Q. Qiu, Y. Xu, J. Zhang, J. Huang, J.Q. Bai, Z.H. Huang, X.H. Qiu, W. Xu, Lipidomics analysis indicates disturbed hepatocellular lipid metabolism in Reynoutria multiflora-Induced Idiosyncratic Liver Injury. Front. Pharmacol. 11, 569144 (2020). https://doi.org/10.3389/fphar.2020.569144CrossRefPubMedPubMedCentral

71.

M.K. Liang, X.Q. Liang, J. Zhong, Y.T. Wei, Z.P. Lian, Z.K. Huang, J. Liang, Integrative analysis of epigenomics, transcriptomics, and proteomics to identify key targets and pathways of Weining granule for gastric cancer. J. Ethnopharmacol. 270, 113787 (2021). https://doi.org/10.1016/j.jep.2021.113787CrossRefPubMed

72.

D.Q. Li, F.J. Shao, S.F. Lu, Identification and characterization of mRNA-like noncoding RNAs in Salvia miltiorrhiza. Planta 241, 1131–1143 (2015). https://doi.org/10.1007/s00425-015-2246-zCrossRefPubMed

73.

W. Zhao, X.Z. Shi, J.N. Li, W. Guo, C.B. Liu, X. Chen, Genetic, epigenetic, and HPLC fingerprint differentiation between natural and Ex situ populations of Rhodiola sachalinensis from Changbai Mountain, China. Plos One 9, e112869 (2014). https://doi.org/10.1371/journal.pone.0112869

74.

L.K. Wu, J.Y. Wang, H.M. Wu, J. Chen, Z.G. Xiao, X.J. Qin, Z.Y. Zhang, W.X. Lin, Comparative metagenomic analysis of rhizosphere microbial community composition and functional potentials under Rehmannia glutinosa consecutive monoculture. Int. J. Mol. Sci. 19, 2394 (2018). https://doi.org/10.3390/ijms19082394CrossRefPubMedPubMedCentral

75.

C.E. Hong, J.U. Kim, J.W. Lee, K.H. Bang, I.H. Jo, Metagenomic analysis of bacterial endophyte community structure and functions in Panax ginseng at different ages. 3 Biotech 9, 300 (2019). https://doi.org/10.1007/s13205-019-1838-x

76.

M.Z. Li, Z.J. Chen, J. Qian, F.G. Wei, G.Z. Zhang, Y. Wang, G.F. Wei, Z.G. Hu, L.L. Dong, S.L. Chen, Composition and function of rhizosphere microbiome of Panax notoginseng with discrepant yields. Chin. Med. 15, 85 (2020). https://doi.org/10.1186/s13020-020-00364-4CrossRefPubMedPubMedCentral

77.

Z.Y. Yang, Z. Chao, K.K. Huo, H. Xie, Z.P. Tian, S.L. Pan, ITS sequence analysis used for molecular identification of the Bupleurum species from northwestern China. Phytomedicine 14, 416–422 (2007). https://doi.org/10.1016/j.phymed.2007.04.009CrossRefPubMed

78.

X.M. Qin, Y.T. Dai, N.Q. Liu, Z.Y. Li, X.J. Liu, J. Hu, Y.H. Choi, R. Verpoorte, Metabolic fingerprinting by (HNMR)-H-1 for discrimination of the two species used as Radix Bupleuri. Planta Med. 78, 926–933 (2012). https://doi.org/10.1055/s-0031-1298496CrossRefPubMed

79.

Z.D. Xia, X. Liu, L.G. Tong, H. Wang, M.L. Feng, X.H. Xi, P. He, X.M. Qin, Comparison of chemical constituents of Bupleurum marginatum var. Stenophyllum and Bupleurum Chinense DC using UHPLC-Q-TOF-MS based on a metabonomics approach. Biomed. Chromatogr 35, e5133 (2021). https://doi.org/10.1002/bmc.5133

80.

Y.M. Wang, L. Liu, Y.K. Ma, L.N. Guo, Y. Sun, Q. Liu, J.C. Liu, Chemical discrimination of Astragalus mongholicus and Astragalus membranaceus based on metabolomics using UHPLC-ESI-Q-TOF-MS/MS approach. Molecules 24, 4064 (2019). https://doi.org/10.3390/molecules24224064CrossRefPubMedPubMedCentral

81.

L.X. Duan, T.L. Chen, M. Li, M. Chen, Y.Q. Zhou, G.H. Cui, A.H. Zhao, W. Jia, L.Q. Huang, X.Q. Qi, Use of the metabolomics approach to characterize chinese medicinal material Huangqi. Mol. Plant 5, 376–386 (2012). https://doi.org/10.1093/mp/ssr093CrossRefPubMed

82.

J.L. Cui, Y. Gong, X.Z. Xue, Y.Y. Zhang, M.L. Wang, J.H. Wang, A phytochemical and pharmacological review on Cynomorium songaricum as functional and medicinal food. Nat. Prod. Commun. 13, 501–510 (2018). https://doi.org/10.1177/1934578X1801300428CrossRef

83.

Y. Zheng, X. Sun, Y.J. Miao, S.W. Qin, Y. Jiang, X. Zhang, L.F. Huang, A systematic study on the chemical diversity and efficacy of the inflorescence and succulent stem of Cynomorium songaricum. Food Func. 12, 7501–7513 (2021). https://doi.org/10.1039/d1fo01275dCrossRef

84.

H.M. Ahmed, Ethnomedicinal, phytochemical and pharmacological investigations of Perilla frutescens (L.) Britt. Molecules 24, 102 (2019). https://doi.org/10.3390/molecules24010102

85.

P.N. Zhou, M.J. Yin, S.L. Dai, K. Bao, C.L. Song, C.C. Liu, Q.A. Wu, Multi-omics analysis of the bioactive constituents biosynthesis of glandular trichome in Perilla frutescens. BMC Plant Biol. 21, 277 (2021). https://doi.org/10.1186/s12870-021-03069-4CrossRefPubMedPubMedCentral

86.

Z.Z. Zhao, P. Guo, E. Brand, The formation of daodi medicinal materials. J. Ethnopharmacol. 140, 476–481 (2012). https://doi.org/10.1016/j.jep.2012.01.048CrossRefPubMed

87.

C. Sarfo-Poku, O. Eshun, K.H. Lee, Medical application of scorpion venom to breast cancer: a mini-review. Toxicon 122, 109–112 (2016). https://doi.org/10.1016/j.toxicon.2016.09.005CrossRefPubMed

88.

S.Y. Gao, H.Y. Liang, Z.Y. Shou, Y.Z. Yao, Y. Lv, J. Shang, W. Lu, C.L. Jia, Q. Liu, H.Y. Zhang, L. Xiao, De novo transcriptomic and proteomic analysis and potential toxin screening of Mesobuthus martensii samples from four different provinces. J. Ethnopharmacol. 265, 113268 (2021). https://doi.org/10.1016/j.jep.2020.113268CrossRefPubMed

89.

W.W. Xie, Y.H. Ma, W.J. Sun, S. Guan, Y.R. Jin, Y.F. Du, An integrative method based on UHPLC-Q-TOF-MS/MS combined with metabolomics to authenticate Isodon rubescens. Anal. Biochem. 629, 114297 (2021). https://doi.org/10.1016/j.ab.2021.114297CrossRefPubMed

90.

G.Y. Lu, Z.T. Liu, X. Wang, C.L. Wang, Recent advances in Panax ginseng C.A. Meyer as a Herb for Anti-Fatigue: an effects and mechanisms review. Foods 10, 1030 (2021). https://doi.org/10.3390/foods10051030

91.

Y.G. Yang, Y.B. Yang, H. Qiu, Z.C. Ju, Y.C. Shi, Z.T. Wang, L. Yang, Localization of constituents for determining the age and parts of ginseng through ultraperfomance liquid chromatography quadrupole/time of flight-mass spectrometry combined with desorption electrospray ionization mass spectrometry imaging. J. Pharmaceut. Biomed. Anal. 193, 113722 (2021). https://doi.org/10.1016/j.jpba.2020.113722CrossRef

92.

J. Zhang, X.F. Wu, J.Q. Qu, L. Zhang, Y.T. Zhang, X.H. Qu, Z.H. Huang, W. Xu, Comprehensive comparison on the chemical profile of Guang Chen Pi at DIFFERENT Ripeness stages using untargeted and Pseudotargeted Metabolomics. J. Agr. Food Chem. 68, 8483–8495 (2020). https://doi.org/10.1021/acs.jafc.0c02904CrossRef

93.

J.W. Huang, Y.Q. Du, C.J. Li, J.Z. Yang, J. Ma, Y.D. Zang, N.H. Chen, D.M. Zhang, Neuroprotective triterpene saponins from the leaves of Panax notoginseng. Nat. Prod. Res. 35, 2388–2394 (2021). https://doi.org/10.1080/14786419.2019.1677657CrossRefPubMed

94.

L.-J. Ma, N. Ma, J.-L. Cao, J.-B. Wan, Characterizing the influence of different drying methods on chemical components of Panax notoginseng leaves by heart-cutting two-dimensional liquid chromatography coupled to orbitrap high-resolution mass spectrometry. Food Chem. 369, 130965 (2022). https://doi.org/10.1016/j.foodchem.2021.130965

95.

S.R. Wang, Z.X. Xue, X.H. Huang, W.J. Ma, D.Y. Yang, L.L. Zhao, H.Z. Ouyang, Y.X. Chang, J. He, Comparison of the chemical profile differences of Aster tataricus between raw and processed products by metabolomics coupled with chemometrics methods. J. Sep. Sci. 44, 3883–3897 (2021). https://doi.org/10.1002/jssc.202100315CrossRefPubMed

96.

W.D. Zhang, H.H. Jiang, J.X. Yang, G.S. Song, D. Wen, W.Q. Liu, M.M. Jin, Q. Wang, Y.F. Du, Q. Sun, L. Cao, H.J. Xu, A high-throughput metabolomics approach for the comprehensive differentiation of four Pulsatilla Adans herbs combined with a nontargeted bidirectional screen for rapid identification of triterpenoid saponins. Anal. Bioanal. Chem. 411, 2071–2088 (2019). https://doi.org/10.1007/s00216-019-01631-6CrossRefPubMed

97.

J.G. Zeng, Y.S. Liu, W. Liu, X.B. Liu, F.Q. Liu, P. Huang, P.C. Zhu, J.J. Chen, M.M. Shi, F. Guo, P. Cheng, J. Zeng, Y.F. Liao, J. Gong, H.M. Zhang, D.P. Wang, A.Y. Guo, X.Y. Xiong, Integration of transcriptome proteome and metabolism data reveals the Alkaloids Biosynthesis in Macleaya cordata and Macleaya microcarpa. Plos One 8, e53409 (2013). https://doi.org/10.1371/journal.pone.0053409CrossRefPubMedPubMedCentral

98.

F. Wang, L. Chen, S. Chen, H. Chen, Y. Liu, Characterization of two closely related citrus cultivars using UPLC-ESI-MS/MS-based widely targeted metabolomics. Plos One 16, e0254759 (2021). https://doi.org/10.1371/journal.pone.0254759CrossRefPubMedPubMedCentral

99.

F. Liu, Y.Y. Meng, K. He, F.J. Song, J.H. Cheng, H.X. Wang, Z. Huang, Z. Luo, X.Z. Yan, Comparative analysis of proteomic and metabolomic profiles of different species of Paris. J. Proteomics 200, 11–27 (2019). https://doi.org/10.1016/j.jprot.2019.02.003CrossRefPubMed

100.

S.S. Zhou, J. Xu, C.K. Tsang, K.M. Yip, W.P. Yeung, Z.Z. Zhao, S. Zhu, H. Fushimi, H.Y. Chang, H.B. Chen, Comprehensive quality evaluation and comparison of Angelica sinensis radix and Angelica acutiloba radix by integrated metabolomics and glycomics. J. Food Drug Anal. 26, 1122–1137 (2018). https://doi.org/10.1016/j.jfda.2018.01.015CrossRefPubMedPubMedCentral

101.

T. Jiang, M.D. Zhang, C.X. Wen, X.L. Xie, W. Tian, S.Q. Wen, R.K. Lu, L.D. Liu, Integrated metabolomic and transcriptomic analysis of the anthocyanin regulatory networks in Salvia miltiorrhiza Bge. flowers, BMC Plant Biol. 20, 349 (2020). https://doi.org/10.1186/s12870-020-02553-7

102.

Z.Q. Song, L.L. Guo, T. Liu, C.C. Lin, J.H. Wang, X.F. Li, Comparative RNA-sequence transcriptome analysis of phenolic acid metabolism in Salvia miltiorrhiza, a traditional Chinese medicine model plant. Int. J. Genom. 2017, 9364594 (2017). https://doi.org/10.1155/2017/9364594CrossRef

103.

X. Li, F. Shi, L.P. Gong, B.J. Hang, D.Y. Li, L.L. Chi, Species-specific identification of collagen components in Colla corii asini using a nano-liquid chromatography tandem mass spectrometry proteomics approach. Int. J. Nanomed. 12, 4443–4454 (2017). https://doi.org/10.2147/ijn.S136819CrossRef

104.

Z. Xiang, X.Q. Wang, X.J. Cai, S. Zeng, Metabolomics study on quality control and discrimination of three curcuma species based on gas chromatograph-mass spectrometry. Phytoche. Anal. 22, 411–418 (2011). https://doi.org/10.1002/pca.1296CrossRef

105.

X.J. Zhang, J.F. Qiu, L.P. Guo, Y. Wang, P. Li, F.Q. Yang, H.X. Su, J.B. Wan, Discrimination of multi-origin Chinese herbal medicines using gas chromatography-mass spectrometry-based fatty acid profiling. Molecules 18, 15329–15343 (2013). https://doi.org/10.3390/molecules181215329CrossRefPubMedPubMedCentral

106.

C.P. Wang, Y. Dong, L.Z. Zhu, L.B. Wang, L. Yan, M.Z. Wang, Q. Zhu, X.X. Nan, Y.H. Li, J. Li, Comparative transcriptome analysis of two contrasting wolfberry genotypes during fruit development and ripening and characterization of the LrMYB1 transcription factor that regulates flavonoid biosynthesis. BMC Genomics 21, 295 (2020). https://doi.org/10.1186/s12864-020-6663-4CrossRefPubMedPubMedCentral

107.

P. Wang, J. Zhang, Y.T. Zhang, H. Su, X.H. Qiu, L. Gong, J. Huang, J.Q. Bai, Z.H. Huang, W. Xu, Chemical and genetic discrimination of commercial Guangchenpi (Citrus reticulata ‘Chachi’) by using UPLC-QTOF-MS/MS based metabolomics and DNA barcoding approaches. RSC Adv. 9, 23373–23381 (2019). https://doi.org/10.1039/c9ra03740cCrossRefPubMedPubMedCentral

108.

R. Wang, C.X. Ren, S. Dong, C. Chen, B. Xian, Q.H. Wu, J. Wang, J. Pei, J. Chen, Integrated metabolomics and transcriptome analysis of flavonoid biosynthesis in safflower (Carthamus tinctorius L.) with different colors. Front. Plant Sci. 12, 712038 (2021). https://doi.org/10.3389/fpls.2021.712038

109.

X.H. Li, X.B. Wang, D.X. Hong, S.Y. Zeng, J.S. Su, G. Fan, Y. Zhang, Metabolic discrimination of different rhodiola species using H-1-NMR and GEP combinational chemometrics. Chem. Pharmaceut. Bull. 67, 81–87 (2019). https://doi.org/10.1248/cpb.c18-00509CrossRef

110.

G. Fan, L.H. Tao, Q.H. Yue, T.T. Kuang, C. Tang, Y.D. Yang, W.Z. Luo, X.D. Zhou, Y. Zhang, Metabolic discrimination of Rhizoma Coptidis from different species using H-1 NMR spectroscopy and principal component analysis. Planta Med. 78, 641–648 (2012). https://doi.org/10.1055/s-0031-1298240CrossRefPubMed

111.

Y.Y. Shan, X.Y. Pei, S.Y. Yong, J.L. Li, Q.L. Qin, S.Y. Zeng, J. Yu, Analysis of the complete chloroplast genomes of Scutellaria tsinyunensis and Scutellaria tuberifera (Lamiaceae). Mitochondrial DNA B 6, 2672–2680 (2021). https://doi.org/10.1080/23802359.2021.1920491CrossRef

112.

X. Wang, J.H. Bai, W. Wang, G.L. Zhang, Leaf metabolites profiling between red and green phenotypes of Suaeda salsa by widely targeted metabolomics. Funct. Plant Biol. 46, 845–856 (2019). https://doi.org/10.1071/fp18182CrossRefPubMed

113.

J.J. Li, C.L. Ye, C.F. Chang, Comparative transcriptomics analysis revealing flower trichome development during flower development in two Lonicera japonica Thunb. cultivars using RNA-seq. BMC Plant Biol. 20, 341 (2020). https://doi.org/10.1186/s12870-020-02546-6

114.

S. Wang, L.J. Hao, J.J. Zhu, Z.M. Wang, X. Zhang, X.M. Song, Comparative evaluation of chrysanthemum Flos from different origins by HPLC-DAD-MS (n) and relative response factors. Food Anal. Method. 8, 40–51 (2015). https://doi.org/10.1007/s12161-014-9864-0CrossRef

115.

Q.J. Zou, T. Wang, Q.S. Guo, F. Yang, J.M. Chen, W.Y. Zhang, Combined metabolomic and transcriptomic analysis reveals redirection of the phenylpropanoid metabolic flux in different colored medicinal Chrysanthemum morifolium. Ind. Crop Prod. 164, 113343 (2021). https://doi.org/10.1016/j.indcrop.2021.113343CrossRef

116.

G.L. Zhang, Y. Li, W.L. Wei, J.Y. Li, H.J. Li, Y. Huang, D.A. Guo, Metabolomics combined with multivariate statistical analysis for screening of chemical markers between Gentiana scabra and Gentiana rigescens. Molecules 25, 1228 (2020). https://doi.org/10.3390/molecules25051228CrossRefPubMedPubMedCentral

117.

Z.Q. Wang, C.J. Zhu, S.S. Liu, C.N. He, F.H. Chen, P.G. Xiao, Comprehensive metabolic profile analysis of the root bark of different species of tree peonies (Paeonia Sect. Moutan). Phytochemistry 163, 118–125 (2019). https://doi.org/10.1016/j.phytochem.2019.04.005

118.

C.X. Zhang, X.Y. Wang, Z.Z. Lin, H.D. Wang, Y.X. Qian, W.W. Li, W.Z. Yang, D.A. Guo, Highly selective monitoring of in-source fragmentation sapogenin product ions in positive mode enabling group-target ginsenosides profiling and simultaneous identification of seven Panax herbal medicines. J. Chromatogr. A 1618, 460850 (2020). https://doi.org/10.1016/j.chroma.2020.460850CrossRefPubMed

119.

L. Jia, T.T. Zuo, C.X. Zhang, W.W. Li, H.D. Wang, Y. Hu, X.Y. Wang, Y.X. Qian, W.Z. Yang, H.S. Yu, Simultaneous profiling and holistic comparison of the metabolomes among the flower buds of Panax ginseng, Panax quinquefolius, and Panax notoginseng by UHPLC/IM-QTOF-HDMSE-based metabolomics analysis. Molecules 24, 2188 (2019). https://doi.org/10.3390/molecules24112188CrossRefPubMedPubMedCentral

120.

Y. Liu, G. Fan, J. Zhang, Y. Zhang, J.J. Li, C. Xiong, Q. Zhang, X.D. Li, X.R. Lai, Metabolic discrimination of sea buckthorn from different Hippophae species by H-1 NMR based metabolomics. Sci. Rep. 7, 1585 (2017). https://doi.org/10.1038/s41598-017-01722-3CrossRefPubMedPubMedCentral

121.

Z. Cheng, H. Shu, S. Zhang, B.S. Luo, R.H. Gu, R.F. Zhang, Y.Y. Ji, F.F. Li, C.L. Long, From Folk taxonomy to species confirmation of Acorus (Acoraceae): evidences based on phylogenetic and metabolomic analyses. Front. Plant Sci. 11, 965 (2020). https://doi.org/10.3389/fpls.2020.00965CrossRefPubMedPubMedCentral

122.

C. Song, C.Y. Jiao, Q. Jin, C.W. Chen, Y.P. Cai, Y. Lin, Metabolomics analysis of nitrogen-containing metabolites between two Dendrobium plants. Physiol. Mol. Biol. Plants 26, 1425–1435 (2020). https://doi.org/10.1007/s12298-020-00822-1CrossRefPubMedPubMedCentral

123.

Y.J. Hua, S.N. Wang, Z.X. Liu, X.H. Liu, L.S. Zou, W. Gu, Y. Hou, Y. Ma, Y.Y. Luo, J.X. Liu, ITRAQ-based quantitative proteomic analysis of cultivated Pseudostellaria heterophylla and its wild-type. J. Proteomics 139, 13–25 (2016). https://doi.org/10.1016/j.jprot.2016.02.027CrossRefPubMed

124.

H.X. Tao, W. Xiong, G.D. Zhao, Y. Peng, Z.F. Zhong, L. Xu, R. Duan, K.W.K. Tsim, H. Yu, Y.T. Wang, Discrimination of three Siegesbeckiae Herba species using UPLC-QTOF/MS-based metabolomics approach. Food Chem. Toxicol. 119, 400–406 (2018). https://doi.org/10.1016/j.fct.2017.12.068CrossRefPubMed

125.

Z.J. Hu, Z.Y. Pan, L.J. Yang, K. Wang, P.Y. Yang, Z.Q. Xu, H.X. Yu, Metabolomics analysis provides new insights into the medicinal value of flavonoids in tobacco leaves. Mol. Omics 17, 620–629 (2021). https://doi.org/10.1039/d1mo00092fCrossRefPubMed

126.

D.D. Wang, L.X. Zhang, X.R. Huang, X. Wang, R.N. Yang, J. Mao, X.F. Wang, X.P. Wang, Q. Zhang, P.W. Li, Identification of nutritional components in black sesame determined by Widely targeted metabolomics and traditional Chinese medicines. Molecules 23, 1180 (2018). https://doi.org/10.3390/molecules23051180CrossRefPubMedPubMedCentral

127.

S.Y. Zhao, Z.L. Liu, Y.S. Shu, M.L. Wang, D. He, Z.Q. Song, H.L. Zeng, Z.C. Ning, C. Lu, A.P. Lu, Y.Y. Liu, Chemotaxonomic classification applied to the identification of two closely-related citrus TCMs using UPLC-Q-TOF-MS-based metabolomics. Molecules 22, 1721 (2017). https://doi.org/10.3390/molecules22101721CrossRefPubMedPubMedCentral

128.

M.L. Chen, W.Q. Chang, J.L. Zhou, Y.H. Yin, W.R. Xia, J.Q. Liu, L.F. Liu, G.Z. Xin, Comparison of three officinal species of Callicarpa based on a biochemome profiling strategy with UHPLC-IT-MS and chemometrics analysis. J. Pharmaceut. Biomed. Anal. 145, 666–674 (2017). https://doi.org/10.1016/j.jpba.2017.07.054CrossRef

129.

L.Z. Ling, S.D. Zhang, F. Zhao, J.L. Yang, W.H. Song, S.M. Guan, X.S. Li, Z.J. Huang, L. Cheng, Transcriptome-Wide Identification and Prediction of miRNAs and their targets in Paris polyphylla var. Yunnanensis by high-throughput sequencing analysis. Int. J. Mol. Sci. 18, 219 (2017). https://doi.org/10.3390/ijms18010219

130.

J. Yang, C. Zhang, W.H. Li, T.E. Zhang, G.Z. Fan, B.F. Guo, W.Y. Yao, D.H. Gong, Q.L. Peng, W.J. Ding, Comprehensive analysis of transcriptomics and metabolomics between the heads and tails of angelica sinensis: genes related to Phenylpro-panoid biosynthesis pathway. Comb. Chem. High T. Scr. 24, 1417–1427 (2021). https://doi.org/10.2174/1386207323999201103221952CrossRef

131.

G.H. Zhang, H.X. Wang, W.Y. Xie, Q. Wang, X. Wang, C.Y. Wang, Y.F. Du, C.H. Huo, Q. Wang, Comparison of triterpene compounds of four botanical parts from Poria cocos (Schw.) wolf using simultaneous qualitative and quantitative method and metabolomics approach. Food Res. Int. 121, 666–677 (2019). https://doi.org/10.1016/j.foodres.2018.12.036

132.

S. Zhao, P.A. Tuan, X. Li, Y.B. Kim, H.R. Kim, C.G. Park, J.L. Yang, C.H. Li, S.U. Park, Identification of phenylpropanoid biosynthetic genes and phenylpropanoid accumulation by transcriptome analysis of Lycium chinense. BMC Genomics 14, 802 (2013). https://doi.org/10.1186/1471-2164-14-802CrossRefPubMedPubMedCentral

133.

W. Rui, W.X. Xia, W. Zhao, B.L. Li, J. Li, Y.F. Feng, H.Y. Chen, S.J. Zhao, Differential constituents in roots, stems and leaves of polygonum multiflorum Thunb. screened by UPLC/ESI-Q-TOF-MS and multivariate statistical analysis. J. Chromatogr. Sci. 58, 136–143 (2020). https://doi.org/10.1093/chromsci/bmz086

134.

E.B. Wang, B.F. Jin, X. Li, R.L. Liu, X.R. Xie, W.F. Guo, H.X. Zheng, Z.B. Zhao, Comparative analysis between aerial parts and roots (Astragali Radix) of astragalus membranaceus by NMR-based metabolomics. Food Agr. Immunol. 28, 1126–1141 (2017). https://doi.org/10.1080/09540105.2017.1332007CrossRef

135.

J.X. Liu, J.Y. Hou, C. Jiang, G. Li, H. Lu, F.Y. Meng, L.C. Shi, Deep sequencing of the Scutellaria baicalensis Georgi transcriptome reveals flavonoid biosynthetic profiling and organ-specific gene expression. Plos One 10, e0136397 (2015). https://doi.org/10.1371/journal.pone.0136397CrossRefPubMedPubMedCentral

136.

Z.J. Wu, X.W. Wang, M. Chen, H.Y. Hu, J. Cao, T.Y. Chai, H. Wang, A study on tissue-specific metabolite variations in polygonum cuspidatum by high-resolution mass spectrometry-based metabolic profiling. Molecules 24, 1058 (2019). https://doi.org/10.3390/molecules24061058CrossRefPubMedPubMedCentral

137.

X.W. Chang, J. Li, M.Z. Ju, H.W. Yu, L.P. Zha, H.S. Peng, J.T. Wang, D.Y. Peng, S.Y. Gui, Untargeted metabolomics approach reveals the tissue-specific markers of balloon flower root (Platycodi Radix) using UPLC-Q-TOF/MS. Microchem. J. 168, 106447 (2021). https://doi.org/10.1016/j.microc.2021.106447CrossRef

138.

Z.C. Cai, H.Y. Liao, C.C. Wang, J.L. Chen, M.X. Tan, Y.Q. Mei, L.F. Wei, H. Chen, R. Yang, X.H. Liu, A comprehensive study of the aerial parts of Lonicera japonica Thunb. based on metabolite profiling coupled with PLS-DA. Phytochem. Anal. 31, 786–800 (2020). https://doi.org/10.1002/pca.2943

139.

Y.P. Li, Q. He, S.S. Du, S.S. Guo, Z.F. Geng, Z.W. Deng, Study of methanol extracts from different parts of Peganum harmala L. using H-1-NMR plant metabolomics. J. Anal. Methods Chem. 2018, 6532789 (2018). https://doi.org/10.1155/2018/6532789

140.

M.Y. Lv, J.B. Sun, M. Wang, W.Q. Huang, H.Y. Fan, F.G. Xu, Z.J. Zhang, GC-MS based metabolomics study of stems and roots of Ephedra sinica. J. Pharmaceut. Biomed. Anal. 114, 49–52 (2015). https://doi.org/10.1016/j.jpba.2015.04.035CrossRef

141.

M.Y. Lv, J.Q. Chen, Y.Q. Gao, J.B. Sun, Q.Q. Zhang, M.H. Zhang, F.G. Xu, Z.J. Zhang, Metabolomics based on liquid chromatography with mass spectrometry reveals the chemical difference in the stems and roots derived from Ephedra sinica. J. Sep. Sci. 38, 3331–3336 (2015). https://doi.org/10.1002/jssc.201500529CrossRefPubMed

142.

X. Sun, Y. Zheng, L.X. Tian, Y.J. Miao, T.X. Zeng, Y. Jiang, J. Pei, B. Ahmad, L.F. Huang, Metabolome profiling and molecular docking analysis revealed the metabolic differences and potential pharmacological mechanisms of the inflorescence and succulent stem of Cistanche deserticola. RSC Adv. 11, 27226–27245 (2021). https://doi.org/10.1039/d0ra07488hCrossRefPubMedPubMedCentral

143.

Y.D. Yuan, M.Y. Yu, Z.H. Jia, X.E. Song, Y.Q. Liang, J.C. Zhang, Analysis of Dendrobium huoshanense transcriptome unveils putative genes associated with active ingredients synthesis. BMC Genomics 19, 978 (2018). https://doi.org/10.1186/s12864-018-5305-6CrossRefPubMedPubMedCentral

144.

Y.D. Yuan, J.C. Zhang, J. Kallman, X. Liu, M.J. Meng, J. Lin, Polysaccharide biosynthetic pathway profiling and putative gene mining of Dendrobium moniliforme using RNA-Seq in different tissues. BMC Plant Biol. 19, 521 (2019). https://doi.org/10.1186/s12870-019-2138-7CrossRefPubMedPubMedCentral

145.

Z.J. Wang, W.M. Jiang, Y.Y. Liu, X.X. Meng, X.L. Su, M.Y. Cao, L.P. Wu, N.J. Yu, S.A. Xing, D.Y. Peng, Putative genes in alkaloid biosynthesis identified in Dendrobium officinale by correlating the contents of major bioactive metabolites with genes expression between Protocorm-like bodies and leaves. BMC Genomics 22, 579 (2021). https://doi.org/10.1186/s12864-021-07887-6CrossRefPubMedPubMedCentral

146.

L.L. Chen, Z.H. Wang, Z. Zhou, B.S. He, J.M. He, L.J. Huang, A. Nurbolat, G.Y. Liu, A.A. Haji, Z. Abliz, Development of plant metabolomics analytical approach based on liquid chromatography tandem mass spectrometry in Artemisia rupestris L. Chin. J. Anal. Chem. 46, 735–742 (2018). https://doi.org/10.11895/j.issn.0253.3820.171489

147.

J.C. Wang, K. Yang, L.R. Yao, Z.K. Ma, C.D. Li, E.J. Si, B.C. Li, Y.X. Meng, X.L. Ma, X.W. Shang, H.J. Wang, Metabolomics analyses provide insights into nutritional value and abiotic stress tolerance in halophyte Halogeton glomeratus. Front. Plant Sci. 12, 703255 (2021). https://doi.org/10.3389/fpls.2021.703255CrossRefPubMedPubMedCentral

148.

W.X. Liang, L. Ni, R. Carballar-Lejarazu, X.X. Zou, W.H. Sun, L.J. Wu, X.Y. Yuan, Y.L. Mao, W. Huang, S.Q. Zou, Comparative transcriptome among Euscaphis konishii Hayata tissues and analysis of genes involved in flavonoid biosynthesis and accumulation. BMC Genomics 20, 24 (2019). https://doi.org/10.1186/s12864-018-5354-xCrossRefPubMedPubMedCentral

149.

M.C. Cui, S.J. Chen, H.H. Wang, Z.H. Li, H.J. Chen, Y. Chen, H.B. Zhou, X. Li, J.W. Chen, Metabolic profiling investigation of Fritillaria thunbergii Miq. by gas chromatography-mass spectrometry. J. Food Drug Anal. 26, 337–347 (2018). https://doi.org/10.1016/j.jfda.2016.10.003

150.

S.S.K. Dossou, F.T. Xu, X.H. Cui, C. Sheng, R. Zhou, J. You, K. Tozo, L.H. Wang, Comparative metabolomics analysis of different sesame (Sesamum indicum L.) tissues reveals a tissue-specific accumulation of metabolites. BMC Plant Biol. 21, 352 (2021). https://doi.org/10.1186/s12870-021-03132-0

151.

R. Xu, J. Zhang, J.M. You, L.M. Gao, Y.C. Li, S.P. Zhang, W.J. Zhu, S.H. Shu, C. Xiong, H. Xiong, P. Chen, J. Guo, Z. Liu, Full-length transcriptome sequencing and modular organization analysis of oleanolic acid- and dammarane-type saponins related gene expression patterns in Panax japonicus. Genomics 112, 4137–4147 (2020). https://doi.org/10.1016/j.ygeno.2020.06.045CrossRefPubMed

152.

S.P. Zhang, G. Wang, T. Zuo, X.H. Zhang, R. Xu, W.J. Zhu, J.M. You, R.F. Wang, P. Chen, Comparative transcriptome analysis of rhizome nodes and internodes in Panax. japonicus var. major reveals candidate genes involved in the biosynthesis of triterpenoid saponins. Genomics 112, 1112–1119 (2020). https://doi.org/10.1016/j.ygeno.2019.06.025

153.

J.A. Um, Y.G. Choi, D.K. Lee, Y.S. Lee, C.J. Lim, Y.A. Youn, H.D. Lee, H.J. Cho, J.H. Park, Y.B. Seo, H.C. Kuo, J. Lim, T.J. Yang, S.W. Kwon, J. Lee, Discrimination between genetically identical peony roots from different regions of origin based on H-1-nuclear magnetic resonance spectroscopy-based metabolomics: determination of the geographical origins and estimation of the mixing proportions of blended samples. Anal. Bioanal. Chem. 405, 7523–7534 (2013). https://doi.org/10.1007/s00216-013-7182-9CrossRefPubMed

154.

Y.H. Zhu, G.S. Zhu, Q.S. Guo, Z.B. Zhu, C.L. Wang, Z.Y. Liu, A comparative proteomic analysis of Pinellia ternata leaves exposed to heat stress. Int. J. Mol. Sci. 14, 20614–20634 (2013). https://doi.org/10.3390/ijms141020614CrossRefPubMedPubMedCentral

155.

F. Xiong, X.Q. Nie, L.C. Yang, L.L. Wang, J.J. Li, G.Y. Zhou, Non-target metabolomics revealed the differences between Rh. tanguticum plants growing under canopy and open habitats. BMC Plant Biol. 21, 119 (2021). https://doi.org/10.1186/s12870-021-02897-8

156.

P.H. Chan, W.L. Zhang, C.H. Lau, C.Y. Cheung, H.C. Keun, K.W.K. Tsim, H. Lam, Metabonomic analysis of water extracts from different angelica roots by H-1-nuclear magnetic resonance spectroscopy. Molecules 19, 3460–3470 (2014). https://doi.org/10.3390/molecules19033460CrossRefPubMedPubMedCentral

157.

K.X. Zhang, M.L. Yan, S. Han, L.F. Cong, L.Y. Wang, L. Zhang, L.L. Sun, H.Y. Bai, G.H. Wei, H. Du, M. Jiang, G. Bai, Z.G. Yang, Identification of chemical markers for the discrimination of Radix Angelica sinensis grown in Geoherb and non-Geoherb regions using UHPLC-QTOF-MS/MS based metabolomics. Molecules 24, 3536 (2019). https://doi.org/10.3390/molecules24193536CrossRefPubMedPubMedCentral

158.

G.H. Cui, L.Q. Huang, X.J. Tang, J.X. Zhao, Candidate genes involved in tanshinone biosynthesis in hairy roots of Salvia miltiorrhiza revealed by cDNA microarray. Mol. Biol. Rep. 38, 2471–2478 (2011). https://doi.org/10.1007/s11033-010-0383-9CrossRefPubMed

159.

Y.W. Cao, R.J. Qu, X.Q. Tang, L.Q. Sun, Q.Q. Chen, Y.J. Miao, UPLC-Triple TOF-MS/MS based metabolomics approach to reveal the influence of nitrogen levels on Isatis Indigotica seedling leaf. Sci. Hortic. 266, 109280 (2020). https://doi.org/10.1016/j.scienta.2020.109280CrossRef

160.

J.S. Zhang, X. Zhong, S.S. Li, G.R. Zhang, X. Liu, Metabolic characterization of natural and cultured Ophicordyceps sinensis from different origins by H-1 NMR spectroscopy. J. Pharmaceut. Biomed. Anal. 115, 395–401 (2015). https://doi.org/10.1016/j.jpba.2015.07.035CrossRef

161.

B. Zhang, B. Li, X.H. Men, Z.W. Xu, H. Wu, X.T. Qin, F. Xu, Y. Teng, S.J. Yuan, L.Q. Jin, Z.Q. Liu, Y.G. Zheng, Proteome sequencing and analysis of Ophiocordyceps sinensis at different culture periods. BMC Genomics 21, 886 (2020). https://doi.org/10.1186/s12864-020-07298-zCrossRefPubMedPubMedCentral

162.

Z. Pan, G. Fan, R.P. Yang, W.Z. Luo, X.D. Zhou, Y. Zhang, Discriminating Lamiophlomis rotata according to geographical origin by H-1-NMR spectroscopy and multivariate analysis. Phytochem. Anal. 26, 247–252 (2015). https://doi.org/10.1002/pca.2557CrossRefPubMed

163.

J. Wang, Y.L. Gao, Y.L. Chen, Y.W. Chen, Y. Zhang, L. Xiang, Z. Pan, Lamiophlomis rotata identification via ITS2 barcode and quality evaluation by UPLC-QTOF-MS couple with multivariate analyses. Molecules 23, 3289 (2018). https://doi.org/10.3390/molecules23123289CrossRefPubMedPubMedCentral

164.

H.Y. Bai, F. Bao, X.R. Fan, S. Han, W.H. Zheng, L.L. Sun, N. Yan, H. Du, H.Y. Zhao, Z.G. Yang, Metabolomics study of different parts of licorice from different geographical origins and their anti-inflammatory activities. J. Sep. Sci. 43, 1593–1602 (2020). https://doi.org/10.1002/jssc.201901013CrossRefPubMed

165.

J.R. Li, X.Z. Chen, L.T. Zhong, X.B. Wang, X.X. Zhou, Y. Tang, Y.T. Liu, H. Zheng, R.T. Zhan, L.K. Chen, Comparative iTRAQ-based proteomic analysis provides insight into a complex regulatory network of Pogostemon cablin in response to exogenous MeJA and Ethrel. Ind. Crop Prod. 140, 111661 (2019). https://doi.org/10.1016/j.indcrop.2019.111661CrossRef

166.

T.T. Cao, J.S. Liu, X.Y. Zhang, X.P. Wei, Y.D. Qi, B.G. Zhang, H.T. Liu, P.G. Xiao, Metabolomics characterization of different geographical origins of Flos Carthami using UPLC-QTOF/MS and their correlation with climate factors. Anal. Methods 11, 2700–2711 (2019). https://doi.org/10.1039/c9ay00368aCrossRef

167.

J. Chen, J. Wang, R. Wang, B. Xian, C.X. Ren, Q.Q. Liu, Q.H. Wu, J. Pei, Integrated metabolomics and transcriptome analysis on flavonoid biosynthesis in safflower (Carthamus tinctorius L.) under MeJA treatment. BMC Plant Biol. 20, 353 (2020). https://doi.org/10.1186/s12870-020-02554-6

168.

A. Booker, L.X. Zhai, C. Gkouva, S.Y. Li, M. Heinrich, From traditional resource to global commodities: A comparison of rhodiola species using NMR spectroscopy—metabolomics and HPTLC. Front. Pharmacol. 7, 254 (2016). https://doi.org/10.3389/fphar.2016.00254CrossRefPubMedPubMedCentral

169.

Y. Jiang, J. Vaysse, V. Gilard, S. Balayssac, S. Dejean, M. Malet-Martino, B. David, C. Fiorini, Y. Barbin, Quality assessment of commercial Magnoliae Officinalis Cortex by 1H-NMR-based metabolomics and HPLC methods. Phytochem. Anal. 23, 387–395 (2012). https://doi.org/10.1002/pca.1369CrossRefPubMed

170.

A.P. Li, Z.Y. Li, H.F. Sun, K. Li, X.M. Qin, G.H. Du, Comparison of two different Astragali Radix by a H-1 NMR-based metabolomic approach. J. Proteome Res. 14, 2005–2016 (2015). https://doi.org/10.1021/pr501167uCrossRefPubMed

171.

Z.C. Cai, H. Chen, J.J. Chen, R. Yang, L.S. Zou, C.C. Wang, J.L. Chen, M.X. Tan, Y.Q. Mei, L.F. Wei, S.X. Yin, X.H. Liu, Metabolomics characterizes the metabolic changes of Lonicerae Japonicae Flos under different salt stresses. Plos One 15, e0243111 (2020). https://doi.org/10.1371/journal.pone.0243111CrossRefPubMedPubMedCentral

172.

S.Z. Zhou, P.M. Allard, C. Wolfrum, C.Q. Ke, C.P. Tang, Y. Ye, J.L. Wolfender, Identification of chemotypes in bitter melon by metabolomics: a plant with potential benefit for management of diabetes in traditional Chinese medicine. Metabolomics 15, 104 (2019). https://doi.org/10.1007/s11306-019-1565-7CrossRefPubMed

173.

C.H. Li, H.L. Zuo, C. Chen, Y.J. Hu, Z.M. Qian, W.J. Li, Z.N. Xia, F.Q. Yang, SDS-PAGE and 2-DE protein profiles of Ganoderma lucidum from different origins. Pak. J. Pharm. Sci. 31, 447–454 (2018)PubMed

174.

H.F. Sun, M.F. Song, Y. Zhang, Z.L. Zhang, Transcriptome profiling reveals candidate flavonoid-related genes during formation of dragons? blood from Dracaena cochinchinensis (Lour.) SCChen under conditions of wounding stress. J. Ethnopharmacol. 273, 113987 (2021). https://doi.org/10.1016/j.jep.2021.113987

175.

X. Jin, J.Q. Zhang, Y. Li, W.Y. Wu, H. Zhang, C.L. Yao, W.L. Wei, S. Yao, Y. Huang, H. Qu, D.A. Guo, Nontargeted metabolomic analysis and multiple criteria decision-making method induced robust quality markers screening for the authentication of herbal medicines from different origins by taking Ophiopogon japonicus L. f. Ker-Gawl. as a case study. J. Sep. Sci. 44, 1440–1451 (2021). https://doi.org/10.1002/jssc.202000655

176.

Y.L. Lin, T.N. Wen, S.T. Chang, F.H. Chu, Proteomic analysis of differently cultured endemic medicinal mushroom Antrodia cinnamomea T.T. Chang et W.N. Chou from Taiwan. Int. J. Med. Mushrooms 13, 473–481 (2011). https://doi.org/10.1615/IntJMedMushr.v13.i5.70

177.

S.B. Zhang, C. Li, W. Gu, R.L. Qiu, J.G. Chao, L.F. Pei, L.J. Ma, Y.F. Guo, R. Tian, Metabolomics analysis of dandelions from different geographical regions in China. Phytochem. Anal. 32, 899–906 (2021). https://doi.org/10.1002/pca.3033CrossRefPubMed

178.

M. Jayakodi, S.C. Lee, H.S. Park, W. Jang, Y.S. Lee, B.S. Choi, G.J. Nah, D.S. Kim, S. Natesan, C. Sun, T.J. Yang, Transcriptome profiling and comparative analysis of Panax ginseng adventitious roots. J. Ginseng Res. 38, 278–288 (2014). https://doi.org/10.1016/j.jgr.2014.05.008CrossRefPubMedPubMedCentral

179.

X.M. Luo, J.Q. Meng, X.F. Chen, L.K. Cheng, S.P. Yan, L.Y. Gao, M. Xue, Y.J. Yang, Metabolomics-based study reveals the effect of lead (Pb) in the culture environment on Whitmania pigra. Sci. Rep. 10, 4794 (2020). https://doi.org/10.1038/s41598-020-61745-1CrossRefPubMedPubMedCentral

180.

Y.J. Hua, C.C. Wang, S.N. Wang, Z.X. Liu, X.H. Liu, L.S. Zou, W. Gu, Y.Y. Luo, J.X. Liu, ITRAQ-based quantitative proteomic analysis of Pseudostellaria heterophylla from geo-authentic habitat and cultivated bases. Curr. Proteomics 16, 231–245 (2019). https://doi.org/10.2174/1570164616666181116124050CrossRef

181.

Y.J. Hua, Y. Hou, S.N. Wang, Y. Ma, Z.X. Liu, L.S. Zou, X.H. Liu, Y.Y. Luo, J.X. Liu, Comparison of chemical compositions in Pseudostellariae radix from different cultivated fields and germplasms by NMR-based metabolomics. Molecules 21, 1538 (2016). https://doi.org/10.3390/molecules21111538CrossRefPubMedPubMedCentral

182.

J.J. Hou, C.M. Cao, Y.W. Xu, S. Yao, L.Y. Cai, H.L. Long, Q.R. Bi, Y.Y. Zhen, W.Y. Wu, D.A. Guo, Exploring lipid markers of the quality of coix seeds with different geographical origins using supercritical fluid chromatography mass spectrometry and chemometrics. Phytomedicine 45, 1–7 (2018). https://doi.org/10.1016/j.phymed.2018.03.010CrossRefPubMed

183.

J. Wu, W.Z. Yang, H.Q. Pan, S. Yao, W.Y. Wu, D.A. Guo, Geographic impact evaluation of the quality of Alismatis Rhizoma by untargeted metabolomics and quantitative assay. J. Sep. Sci. 41, 839–846 (2018). https://doi.org/10.1002/jssc.201700902CrossRefPubMed

184.

R. Dong, Q.P. Tian, Y.P. Shi, S.J. Chen, Y.G. Zhang, Z.P. Deng, X.J. Wang, Q.Q. Yao, L.W. Han, An integrated strategy for rapid discovery and identification of quality markers in gardenia fructus using an omics discrimination-grey correlation-biological verification method. Front. Pharmacol. 12, 705498 (2021). https://doi.org/10.3389/fphar.2021.705498CrossRefPubMedPubMedCentral

185.

K.M. Yip, J. Xu, S.S. Zhou, Y.M. Lau, Q.L. Chen, Y.C. Tang, Z.J. Yang, Z.P. Yao, P. Ding, H.B. Chen, Z.Z. Zhao, Characterization of chemical component variations in different growth years and tissues of Morindae officinalis radix by integrating metabolomics and glycomics. J. Agr. Food Chem. 67, 7304–7314 (2019). https://doi.org/10.1021/acs.jafc.9b01910CrossRef

186.

Z. Geng, Y.F. Liu, Y. Gou, Q.M. Zhou, C.J. He, L. Guo, J. Zhou, L. Xiong, Metabolomics study of cultivated bulbus Fritillariae Cirrhosae at different growth stages using UHPLC-QTOF-MS coupled with multivariate data analysis. Phytochem. Anal. 29, 290–299 (2018). https://doi.org/10.1002/pca.2742CrossRefPubMed

187.

M.Y. Xu, Y.W. Wang, Q.B. Wang, S.L. Guo, Y. Liu, J. Liu, Z.H. Tang, Z.Y. Wang, Targeted development-dependent metabolomics profiling of bioactive compounds in Acanthopanax senticosus by UPLC-ESI-MS. Nat. Prod. Commun. 15, 1–11 (2020). https://doi.org/10.1177/1934578x20910553CrossRef

188.

J. Li, M.L. Li, T.T. Zhu, X.N. Zhang, M.F. Li, J.H. Wei, Integrated transcriptomics and metabolites at different growth stages reveals the regulation mechanism of bolting and flowering of Angelica sinensis. Plant Biol. 23, 574–582 (2021). https://doi.org/10.1111/plb.13249CrossRefPubMed

189.

Y.Y. Qian, Y.L. Wang, R.N. Sa, H. Yan, X.B. Pan, Y.W. Yang, Y.J. Sun, Metabolic fingerprinting of Angelica sinensis during growth using UPLC-TOFMS and chemometrics data analysis. Chem. Cent. J. 7, 42 (2013). https://doi.org/10.1186/1752-153x-7-42CrossRefPubMedPubMedCentral

190.

S.L. Liu, X.H. Wang, Y.G. Gao, Y. Zhao, A.H. Zhang, Y.H. Xu, L.X. Zhang, Transcriptomic analysis identifies differentially expressed genes (DEGs) associated with bolting and flowering in Saposhnikovia divaricata. Chin. J. Nat. Med. 16, 446–455 (2018). https://doi.org/10.1016/S1875-5364(18)30078-5CrossRefPubMed

191.

Y.Y. Wu, W.J. Zhu, W. Wei, X.L. Zhao, Q. Wang, W.Y. Zeng, Y.L. Zheng, P. Chen, S.P. Zhang, De novo assembly and transcriptome analysis of sclerotial development in Wolfiporia cocos. Gene 588, 149–155 (2016). https://doi.org/10.1016/j.gene.2016.05.020CrossRefPubMed

192.

H. Su, H. Zhang, X.H. Wei, D.A. Pan, L. Jing, D.Q. Zhao, Y. Zhao, B. Qi, Comparative proteomic analysis of Rana chensinensis Oviduct. Molecules 23, 1384 (2018). https://doi.org/10.3390/molecules23061384CrossRefPubMedPubMedCentral

193.

Y. Peng, H.Q. Ma, S.W. Chen, De novo characterization of the Lycium ruthenicum transcriptome and analysis of its digital gene expression profiles during fruit development and ripening. Arch. Biol. Sci. 69, 181–190 (2017). https://doi.org/10.2298/abs160123125pCrossRef

194.

J.P. Liang, W.N. Li, X.Y. Jia, Y. Zhang, J.P. Zhao, Transcriptome sequencing and characterization of Astragalus membranaceusvar. mongholicusroot reveals key genes involved in flavonoids biosynthesis. Genes Genom. 42, 901–914 (2020). https://doi.org/10.1007/s13258-020-00953-5

195.

D.X. Xing, G.W. Shen, Q.R. Li, Y. Xiao, Q. Yang, Q.Y. Xia, Quality formation mechanism of Stiff Silkworm, Bombyx batryticatus using UPLC-Q-TOF-MS-based metabolomics. Molecules 24, 3780 (2019). https://doi.org/10.3390/molecules24203780CrossRefPubMedPubMedCentral

196.

J. Li, Z.Z. Zhang, Z.H. Lei, X.M. Qin, Z.Y. Li, NMR based metabolomic comparison of the antitussive and expectorant effect of Farfarae Flos collected at different stages. J. Pharmaceut. Biomed. Anal. 150, 377–385 (2018). https://doi.org/10.1016/j.jpba.2017.12.028CrossRef

197.

J.P. Jia, F.S. Zhang, Z.Y. Li, X.M. Qin, L.W. Zhang, Comparison of fruits of forsythia suspensa at two different maturation stages by NMR-based metabolomics. Molecules 20, 10065–10081 (2015). https://doi.org/10.3390/molecules200610065CrossRefPubMedPubMedCentral

198.

J.L. Bao, R.B. Ding, Y.E. Liang, F. Liu, K. Wang, X.J. Jia, C. Zhang, M.W. Chen, P. Li, H.X. Su, J.B. Wan, Y.T. Wang, C.W. He, Differences in chemical component and anticancer activity of green and Ripe Forsythiae Fructus. Am. J. Chin. Med. 45, 1513–1536 (2017). https://doi.org/10.1142/s0192415x17500823CrossRefPubMed

199.

C.J. Zeng, W.J. Li, Q. Liao, T.T. Yan, K.S. Wang, Y. Hu, Y.M. Shen, M. Price, Z.X. Fan, X.Y. Zhang, B.S. Yue, F.N. Geng, L.J. Chen, Comparative transcriptomics reveals the expression differences between four developmental stages of American Cockroach (Periplaneta americana). DNA Cell Biol. 38, 1078–1087 (2019). https://doi.org/10.1089/dna.2018.4578CrossRefPubMed

200.

J. Liu, Q. Wang, M.Y. Sun, L.L. Zhu, M. Yang, Y. Zhao, Selection of reference genes for quantitative real-time PCR normalization in panax ginseng at different stages of growth and in different Organs. Plos One 9, e112177 (2014). https://doi.org/10.1371/journal.pone.0112177CrossRefPubMedPubMedCentral

201.

B.M. Huang, Q.L. Zha, T.B. Chen, S.Y. Xiao, Y. Xie, P. Luo, Y.P. Wang, L. Liu, H. Zhou, Discovery of markers for discriminating the age of cultivated ginseng by using UHPLC-QTOF/MS coupled with OPLS-DA. Phytomedicine 45, 8–17 (2018). https://doi.org/10.1016/j.phymed.2018.03.011CrossRefPubMed

202.

Z. Liu, C.Z. Wang, X.Y. Zhu, J.Y. Wan, J. Zhang, W. Li, C.C. Ruan, C.S. Yuan, Dynamic changes in neutral and acidic ginsenosides with different cultivation ages and harvest seasons: identification of chemical characteristics for panax ginseng quality control. Molecules 22, 734 (2017). https://doi.org/10.3390/molecules22050734CrossRefPubMedPubMedCentral

203.

Z.M. Zhang, B.H. Xia, Y.M. Li, Y. Lin, J.C. Xie, P. Wu, L.M. Lin, D.F. Liao, Comparative proteomic analysis of Prunella vulgaris L. spica ripening. J. Proteomics 232, 104028 (2021). https://doi.org/10.1016/j.jprot.2020.104028

204.

X. Zhang, Z.Q. Song, T. Liu, L.L. Guo, X.F. Li, De Novo assembly and comparative transcriptome analysis provide insight into Lysine Biosynthesis in Toona sinensis Roem. Int. J. Genomics 2016, 6735209 (2016). https://doi.org/10.1155/2016/6735209CrossRefPubMedPubMedCentral

205.

M. Kong, J. Xu, H.H. Liu, J.D. Xu, X.Y. Li, M. Lu, C.R. Wang, H.B. Chen, S.L. Li, Effects of boiling duration in processing of White Paeony Root on its overall quality evaluated by ultra-high performance liquid chromatography quadrupole/time-of-flight mass spectrometry based metabolomics analysis and high performance liquid chromatography quantification. Chin. J. Nat. Med. 15, 62–70 (2017). https://doi.org/10.1016/S1875-5364(17)30009-2CrossRef

206.

J.D. Zhang, G. Cao, Y.H. Xia, C.P. Wen, Y.S. Fan, Fast analysis of principal volatile compounds in crude and processed Atractylodes macrocephala by an automated static headspace gas chromatography-mass spectrometry. Pharmacogn. Mag. 10, 249–253 (2014). https://doi.org/10.4103/0973-1296.137364CrossRefPubMedPubMedCentral

207.

T.L. Lei, S.F. Chen, K. Wang, D.D. Zhang, L. Dong, C.N. Lv, J. Wang, J.C. Lu, Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis. Biomed. Chromatogr. 32, e4044 (2018). https://doi.org/10.1002/bmc.4044CrossRef

208.

C. Li, R. Wen, D.W. Liu, Q. Liu, L.P. Yan, J.X. Wu, Y.J. Guo, S.Y. Li, Q.F. Gong, H. Yu, Diuretic effect and metabolomics analysis of crude and salt-processed Plantaginis Semen. Front. Pharmacol. 11, 563157 (2020). https://doi.org/10.3389/fphar.2020.563157CrossRefPubMedPubMedCentral

209.

L.J. Zhong, Y.L. Hua, P. Ji, W.L. Yao, W.Q. Zhang, J. Li, Y.M. Wei, Evaluation of the anti-inflammatory effects of volatile oils from processed products of Angelica sinensis radix by GC MS-based metabolomics. J. Ethnopharmacol. 191, 195–205 (2016). https://doi.org/10.1016/j.jep.2016.06.027CrossRefPubMed

210.

F.B. Xia, C.H. Liu, J.B. Wan, Characterization of the cold and hot natures of raw and processed Rehmanniae Radix by integrated metabolomics and network pharmacology. Phytomedicine 74, 153071 (2020). https://doi.org/10.1016/j.phymed.2019.153071CrossRefPubMed

211.

X.A. Yu, A.H. Ge, L. Zhang, J. Li, M.R. An, J. Cao, J. He, X.M. Gao, Y.X. Chang, Influence of different processing times on the quality of Polygoni Multiflora Radix by metabolomics based on ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry. J. Sep. Sci. 40, 1928–1941 (2017). https://doi.org/10.1002/jssc.201600913CrossRefPubMed

212.

L.F. Han, P. Wang, Y.L. Wang, Q.Y. Zhao, F. Zheng, Z.Y. Dou, W.Z. Yang, L.M. Hu, C.X. Liu, Rapid Discovery of the Potential Toxic Compounds in Polygonum multiflorum by UHPLC/Q-Orbitrap-MS-Based Metabolomics and Correlation Analysis. Front. Pharmacol.Pharmacol. 10, 329 (2019). https://doi.org/10.3389/fphar.2019.00329CrossRef

213.

J.Q. Bai, H. Su, Y.L. Liang, X.H. Shi, J. Huang, W. Xu, J. Zhang, L. Gong, Z.H. Huang, X.H. Qiu, Screening of Quality Markers During the Processing of Reynoutria multiflora Based on the UHPLC-Q-Exactive Plus Orbitrap MS/MS Metabolomic Method. Front. Pharmacol. 12, 695560 (2021). https://doi.org/10.3389/fphar.2021.695560CrossRefPubMedPubMedCentral

214.

H. Zhao, Y. Yan, C.C. Wang, L.S. Zou, X.H. Liu, S.Y. Chen, J.J. Shi, Comparison of chemical constituents in Magnoliae officinalis cortex processed by “Sweating” and “Non Sweating” based on ultra fast liquid chromatography-triple quadrupole-time of flight mass spectrometry and gas chromatography-triple quadrupole mass spectrometry combined with multivariate statistical analysis. Nat. Prod. Commun. 13, 987–991 (2018). https://doi.org/10.1177/1934578X1801300816CrossRef

215.

P.P. Liu, G.S. Shan, F. Zhang, J.N. Chen, T.Z. Jia, Metabolomics analysis and rapid identification of changes in chemical ingredients in crude and processed Astragali Radix by UPLC-QTOF-MS combined with novel informatics UNIFI platform. Chin. J. Nat. Med. 16, 714–720 (2018). https://doi.org/10.1016/s1875-5364(18)30111-0CrossRefPubMed

216.

Y.T. Dai, Q. Li, J.Y. Tong, R. Verpoorte, S.J. Zhao, X.M. Qin, S.L. Chen, Quality marker identification based on standard decoction of differently processed materials of Ephedrae Herba. J. Ethnopharmacol. 237, 47–54 (2019). https://doi.org/10.1016/j.jep.2019.03.025CrossRefPubMed

217.

X.Y. Li, J.D. Xu, J. Xu, M. Kong, S.S. Zhou, Q. Mao, E. Brand, H.B. Chen, H.Q. Liu, S.L. Li, UPLC-QTOF-MS based metabolomics coupled with the diagnostic ion exploration strategy for rapidly evaluating Sulfur-fumigation caused holistic quality variation in medicinal herbs, Moutan Cortex as an example. Anal. Methods 8, 1034–1043 (2016). https://doi.org/10.1039/c5ay01404bCrossRef

218.

M.R. Li, X.Y. Wang, L.F. Han, L. Jia, E.W. Liu, Z. Li, H.H. Yu, Y.C. Wang, X.M. Gao, W.Z. Yang, Integration of multicomponent characterization, untargeted metabolomics and mass spectrometry imaging to unveil the holistic chemical transformations and key markers associated with wine steaming of Ligustri Lucidi Fructus. J. Chromatogr. A. 1624, 461228 (2020). https://doi.org/10.1016/j.chroma.2020.461228CrossRefPubMed

219.

X.Y. Su, Y.J. Wu, Y. Li, Y.F. Huang, Y. Liu, P. Luo, Z.F. Zhang, Effect of different post-harvest processing methods on the chemical constituents of Notopterygium franchetii by an UHPLC-QTOF-MS-MS metabolomics approach. Molecules 24, 3188 (2019). https://doi.org/10.3390/molecules24173188CrossRefPubMedPubMedCentral

220.

H.M. Zhang, S.L. Li, H. Zhang, Y. Wang, Z.L. Zhao, S.L. Chen, H.X. Xu, Holistic quality evaluation of commercial white and red ginseng using a UPLC-QTOF-MS/MS-based metabolomics approach. J. Pharmaceut. Biomed. Anal. 62, 258–273 (2012). https://doi.org/10.1016/j.jpba.2012.01.010CrossRef

221.

P.P. Zou, Y.L. Song, W. Lei, J. Li, P.F. Tu, Y. Jiang, Application of H-1 NMR-based metabolomics for discrimination of different parts and development of a new processing workflow for Cistanche deserticola. Acta Pharm. Sin. B 7, 647–656 (2017). https://doi.org/10.1016/j.apsb.2017.07.003CrossRefPubMedPubMedCentral

222.

X.K. Liu, Y.L. Guo, G.Z. Cai, J.Y. Gong, Y. Wang, S.Y. Liu, Chemical composition analysis of Schisandra chinensis fructus and its three processed products using UHPLC-Q-Orbitrap/MS-based metabolomics approach. Nat. Prod. Res. (2020). https://doi.org/10.1080/14786419.2020.1858416CrossRefPubMed

223.

H. Yan, Z.J. Pu, Z.Y. Zhang, G.S. Zhou, D.Q. Zou, S. Guo, C. Li, Z.L. Zhan, J.A. Duan, Research on biomarkers of different growth periods and different drying processes of citrus Wilsonii Tanaka based on plant metabolomics. Front. Plant Sci. 12, 700367 (2021). https://doi.org/10.3389/fpls.2021.700367CrossRefPubMedPubMedCentral

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19. Omics in Quality Research of Chinese Medicines

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