Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

Abstract

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.

UPLC-ESI-MRM/MS for Absolute Quantification and MS/MS Structural Elucidation of Six Specialized Pyranonaphthoquinone Metabolites From Ventilago harmandiana

“…Among these, plants provide a massive and unique source of specialized metabolites with a great potential for various pharmaceutical applications ( Fang et al, 2019 ). Specialized metabolites, historically referred to as secondary metabolites, are chemical compounds that are not essential for supporting life of a given organism and are often extracted from medicinal plants for the study of their chemical structures and their biological activities ( Khoomrung et al, 2017 ). The plant Ventilago genus of the Rhamnaceae family contains more than 40 species and is found across India, South Asia ( Cooke and Johnson, 1963 ), and recently in Thailand ( Kongsriprapan et al, 2012 ).…”

Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data

“…The workflow of metabolomics data analysis is illustrated in Fig. 1 [30]. For the proposed data processing step, each measured metabolite is fitted to an LMM.…”

Deep metabolome: Applications of deep learning in metabolomics

“…A raw MS data file of an individual sample contains a set of chromatograms recorded in sequence. Literally, each chromatogram, consisting of mass spectra or fingerprint of the detected metabolite represents the abundance of an ionized molecule [42] . Raw data files are subjected to a series of data processing steps and information extraction into an expression matrix (containing retention times, accurate mass spectrum and intensity values) of the measured metabolites for subsequent analyses [43] .…”