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  • Zhao, Ke; Li, Jing; Zhang, Xiaoyue; Chen, Qiang; Liu, Maoke; Ao, Xiaolin; Gu, Yunfu; Liao, Decong; Xu, Kaiwei; Ma, Monggeng; Yu, Xiumei; Xiang, Quanju; Chen, Ji; Zhang, Xiaoping; Penttinen, Petri (2018)
    Many of the plant associated microbes may directly and indirectly contribute to plant growth and stress resistance. Our aim was to assess the plant growth-promoting and antimicrobial activities of actinobacteria isolated from Glycyrrhiza inflata Bat. plants to find strains that could be applied in agricultural industry, for example in reclaiming saline soils. We isolated 36 and 52 strains that showed morphological characteristics of actinobacteria from one year old and three year old G. inflata plants, respectively. Based on 16S rRNA gene sequence analysis, the strains represented ten actinobacterial genera. Most of the strains had plant growth promoting characteristics in vitro, tolerated 200 mM NaCl and inhibited the growth of at least one indicator organism. The eight selected Streptomyces strains increased the germination rate of G. inflata seeds under salt stress. In addition, the four best seed germination promoters promoted the growth of G. inflata in vivo. The best promoters of G. inflata growth, strains SCAU5283 and SCAU5215, inhibited a wide range of indicator organisms, and may thus be considered as promising candidates to be applied in inoculating G. inflata.
  • Wang, Yinyin; Jafari, Mohieddin; Tang, Yun; Tang, Jing (2019)
    Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM. Author summary In East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.