The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Gut microbiota, a new frontier to understand traditional Chinese medicines
Abstract
Graphical abstract Figure. No Caption available. Abstract As an important component of complementary and alternative medicines, traditional Chinese medicines (TCM) are gaining more and more attentions around the world because of the powerful therapeutic effects and less side effects. However, there are still some doubts about TCM because of the questionable TCM theories and unclear biological active compounds. In recent years, gut microbiota has emerged as an important frontier to understand the development and progress of diseases. Together with this trend, an increasing number of studies have indicated that drug molecules can interact with gut microbiota after oral administration. In this context, more and more studies pertaining to TCM have paid attention to gut microbiota and have yield rich information for understanding TCM. After oral administration, TCM can interact with gut microbiota: (1) TCM can modulate the composition of gut microbiota; (2) TCM can modulate the metabolism of gut microbiota; (3) gut microbiota can transform TCM compounds. During the interactions, two types of metabolites can be produced: gut microbiota metabolites (of food and host origin) and gut microbiota transformed TCM compounds. In this review, we summarized the interactions between TCM and gut microbiota, and the pharmacological effects and features of metabolites produced during interactions between TCM and gut microbiota. Then, focusing on gut microbiota and metabolites, we summarized the aspects in which gut microbiota has facilitated our understanding of TCM. At the end of this review, the outlooks for further research of TCM and gut microbiota were also discussed.
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Cites
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
A metagenome-wide association study of gut microbiota in type 2 diabetes
Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Diversity, stability and resilience of the human gut microbiota
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K., & Knight, R. (2012) · Nature · doi:10.1038/nature11550 · Cited by 5,533
den Besten, G., van Eunen, K., Groen, A. K., Venema, K., Reijngoud, D.-J., & Bakker, B. M. (2013) · Journal of Lipid Research · doi:10.1194/jlr.r036012 · Cited by 5,048
Host-gut microbiota metabolic interactions
Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., & Pettersson, S. (2012) · Science · doi:10.1126/science.1223813 · Cited by 4,760
Gut microbiota in health and disease
Sekirov, I., Russell, S. L., Antunes, L. C. M., & Finlay, B. B. (2010) · Physiological Reviews · doi:10.1152/physrev.00045.2009 · Cited by 4,182
Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43
Maslowski, K. M., Vieira, A. T., Ng, A., Kranich, J., Sierro, F., Yu, D., Schilter, H. C., Rolph, M. S., Mackay, F., Artis, D., et al. (2009) · Nature · doi:10.1038/nature08530 · Cited by 3,241
Bile salt biotransformations by human intestinal bacteria
Ridlon, J. M., Kang, D.-J., & Hylemon, P. B. (2006) · Journal of Lipid Research · doi:10.1194/jlr.r500013-jlr200 · Cited by 2,829
The role of short chain fatty acids in health and disease
Tan, J., McKenzie, C., Potamitis, M., Thorburn, A. N., Mackay, C. R., & Macia, L. (2014) · Advances in immunology · doi:10.1016/b978-0-12-800100-4.00003-9 · Cited by 2,425
Tolhurst, G., Heffron, H., Lam, Y. S., Parker, H. E., Habib, A. M., Diakogiannaki, E., Cameron, J., Grosse, J., Reimann, F., & Gribble, F. M. (2012) · Diabetes · doi:10.2337/db11-1019 · Cited by 2,404
Metadata
- Authors
- Feng, W., Ao, H., Peng, C., & Yan, D.
- Venue
- Pharmacological Research
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 142
- Pages
- 176-191
- ISSN
- 1043-6618 (ISSN-L)
- OpenAlex
- W2915379541
- Cited by
- 377
- References
- 38
- FWCI
- 13.32
- Type
- journal article
- Language
- en
- Open access
- closed
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