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
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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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