ITPI: Initial Transcription Process-Based Identification Method of Bioactive Components in Traditional Chinese Medicine FormulaReport as inadecuate




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Evidence-Based Complementary and Alternative Medicine - Volume 2016 2016, Article ID 8250323, 7 pages -

Research Article

School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China

Institute of Information on Traditional Chinese Medicine of China, Academy of Chinese Medical Sciences, Beijing 100700, China

China Minority Traditional Medical Center, Minzu University of China, Beijing 100081, China

Received 25 August 2015; Revised 9 February 2016; Accepted 11 February 2016

Academic Editor: Kuzhuvelil B. Harikumar

Copyright © 2016 Baixia Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Identification of bioactive components is an important area of research in traditional Chinese medicine TCM formula. The reported identification methods only consider the interaction between the components and the target proteins, which is not sufficient to explain the influence of TCM on the gene expression. Here, we propose the Initial Transcription Process-based Identification ITPI method for the discovery of bioactive components that influence transcription factors TFs. In this method, genome-wide chip detection technology was used to identify differentially expressed genes DEGs. The TFs of DEGs were derived from GeneCards. The components influencing the TFs were derived from STITCH. The bioactive components in the formula were identified by evaluating the molecular similarity between the components in formula and the components that influence the TF of DEGs. Using the formula of Tian-Zhu-San TZS as an example, the reliability and limitation of ITPI were examined and 16 bioactive components that influence TFs were identified.





Author: Baixia Zhang, Yanwen Li, Yanling Zhang, Zhiyong Li, Tian Bi, Yusu He, Kuokui Song, and Yun Wang

Source: https://www.hindawi.com/



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