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Ganoderma boninense mycelia for phytochemicals and secondary metabolites with antibacterial activity
Syahriel Abdullah , Se-Eun Jang , Min-Kyu Kwak , KhimPhin Chong
J. Microbiol. 2020;58(12):1054-1064.   Published online December 2, 2020
DOI: https://doi.org/10.1007/s12275-020-0208-z
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  • 8 Web of Science
  • 8 Crossref
AbstractAbstract
Antiplasmodial nortriterpenes with 3,4-seco-27-norlanostane skeletons, almost entirely obtained from fruiting bodies, represent the main evidential source for bioactive secondary metabolites derived from a relatively unexplored phytopathogenic fungus, Ganoderma boninense. Currently lacking is convincing evidence for antimicrobial secondary metabolites in this pathogen, excluding that obtained from commonly observed phytochemicals in the plants. Herein, we aimed to demonstrate an efficient analytical approach for the production of antibacterial secondary metabolites using the mycelial extract of G. boninense. Three experimental cultures were prepared from fruiting bodies (GBFB), mycelium cultured on potato dextrose agar (PDA) media (GBMA), and liquid broth (GBMB). Through solvent extraction, culture type-dependent phytochemical distributions were diversely exhibited. Water-extracted GBMB produced the highest yield (31.21 ± 0.61%, p < 0.05), but both GBFB and GBMA elicited remarkably higher yields than GBMB when polar-organic solvent extraction was employed. Greater quantities of phytochemicals were also obtained from GBFB and GBMA, in sharp contrast to those gleaned from GBMB. However, the highest antibacterial activity was observed in chloroform-extracted GBMA against all tested bacteria. From liquid-liquid extractions (LLE), it was seen that mycelia extraction with combined chloroform-methanol-water at a ratio of 1:1:1 was superior at detecting antibacterial activities with the most significant quantities of antibacterial compounds. The data demonstrate a novel means of assessing antibacterial compounds with mycelia by LLE which avoids the shortcomings of standardized
method
ologies. Additionally, the antibacterial extract from the mycelia demonstrate that previously unknown bioactive secondary metabolites of the less studied subsets of Ganoderma may serve as active and potent antimicrobial compounds.

Citations

Citations to this article as recorded by  
  • Medium composition optimization and characterization of polysaccharides extracted from Ganoderma boninense along with antioxidant activity
    Qian-Zhu Li, Chuan Xiong, Wei Chee Wong, Li-Wei Zhou
    International Journal of Biological Macromolecules.2024; 260: 129528.     CrossRef
  • Cytotoxic Potential of Diospyros villosa Leaves and Stem Bark Extracts and Their Silver Nanoparticles
    Oluwatosin Temilade Adu, Yougasphree Naidoo, Johnson Lin, Depika Dwarka, John Mellem, Hosakatte Niranjana Murthy, Yaser Hassan Dewir
    Plants.2023; 12(4): 769.     CrossRef
  • The antitumor effect of mycelia extract of the medicinal macrofungus Inonotus hispidus on HeLa cells via the mitochondrial-mediated pathway
    Shao-Jun Tang, Chen-Xia Shao, Yi Yang, Rui Ren, Lei Jin, Dan Hu, Shen-Lian Wu, Pin Lei, Yue-Lin He, Jun Xu
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  • Impacts of Plant-derived Secondary Metabolites for Improving Flora in Type 2 Diabetes
    Lin Zehao Li, Yan Yan, Qinghe Song, Zhibin Wang, Wei Zhang, Yanli Hou, Xiandang Zhang
    Current Diabetes Reviews.2023;[Epub]     CrossRef
  • Bioactive Compounds of Ganoderma boninense Inhibited Methicillin-Resistant Staphylococcus aureus Growth by Affecting Their Cell Membrane Permeability and Integrity
    Yow-San Chan, Khim-Phin Chong
    Molecules.2022; 27(3): 838.     CrossRef
  • Natural Products Targeting Liver X Receptors or Farnesoid X Receptor
    Jianglian She, Tanwei Gu, Xiaoyan Pang, Yonghong Liu, Lan Tang, Xuefeng Zhou
    Frontiers in Pharmacology.2022;[Epub]     CrossRef
  • Biophysical characterization of antibacterial compounds derived from pathogenic fungi Ganoderma boninense
    Syahriel Abdullah, Yoon Sin Oh, Min-Kyu Kwak, KhimPhin Chong
    Journal of Microbiology.2021; 59(2): 164.     CrossRef
  • Enhanced Accumulation of Betulinic Acid in Transgenic Hairy Roots of Senna obtusifolia Growing in the Sprinkle Bioreactor and Evaluation of Their Biological Properties in Various Biological Models
    Tomasz Kowalczyk, Przemysław Sitarek, Monika Toma, Patricia Rijo, Eva Domínguez‐Martín, Irene Falcó, Gloria Sánchez, Tomasz Śliwiński
    Chemistry & Biodiversity.2021;[Epub]     CrossRef
Machine learning methods for microbiome studies
Junghyun Namkung
J. Microbiol. 2020;58(3):206-216.   Published online February 27, 2020
DOI: https://doi.org/10.1007/s12275-020-0066-8
  • 45 View
  • 0 Download
  • 66 Web of Science
  • 61 Crossref
AbstractAbstract
Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis
methods
can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.

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