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Ritualistic and Medicinal Plants from Marajó-PA Island
Published in Mahendra Rai, Shandesh Bhattarai, Chistiane M. Feitosa, Ethnopharmacology of Wild Plants, 2021
Paulo Wender Portal Gomes, Luiza Helena da Silva Martins, Paulo Weslem Portal Gomes, Emilli Roberta Sousa Pereira, Abraão de Jesus Barbosa Muribeca, Andrea Komesu, Mahendra Rai
In the current scenario about medicinal plants, the word “Metabolome” is used to describe all the small molecules that make up a cell, and this science, belonging to the class of “omics”, has a very important correlation with biology, that is, with genomics (DNA), transcriptomic (RNA) and proteomics. All this was only possible as mass spectrometry emerged in studies aimed at identifying and quantifying the metabolome, including studies with secondary metabolites of medicinal plants (Wang et al. 2019).
Omics Technology: Novel Approach for Screening of Plant-Based Traditional Medicines
Published in Megh R. Goyal, Hafiz Ansar Rasul Suleria, Ademola Olabode Ayeleso, T. Jesse Joel, Sujogya Kumar Panda, The Therapeutic Properties of Medicinal Plants, 2019
Rojita Mishra, Satpal Singh Bisht, Mahendra Rana
Metabolomics mainly involves targeted and global metabolite analysis. Metabolite analysis or complete metabolome can be analyzed using techniques like GC-MS, LC-MS. Metabolite profiling [19] also involves various TLC, HPLC, NMR spectroscopy, Raman Spectroscopy, etc.
Lifestyle Influences on the Microbiome
Published in David Perlmutter, The Microbiome and the Brain, 2019
The impact of diet on the gut metabolome as it relates to health is presently at the forefront of microbiome research.51 One prominent example of the focus of this research is the work being done to explore the role of trimethylamine oxide (TMAO) in cardiovascular disease and stroke. Plasma levels of TMAO are positively associated with the risk of major cardiovascular events.52 One effect of TMAO is increased platelet adherence and promotion of thrombosis.53 TMAO is the product of gut microbial metabolism of dietary choline to trimethylamine (TMA), followed by hepatic oxidation of TMA to TMAO. In mice, blocking TMAO production by feeding dimethylbutanol, a non-lethal inhibitor of TMA synthesis, prevents the development of choline-induced atherosclerosis, indicating that TMAO contributes to the pathogenesis of arterial disease in this model.54 Resveratrol, a flavonoid found in red wine, reduces TMA production by the gut microbiome, which is one possible mechanism by which red wine consumption decreases the incidence of coronary heart disease.55 High fat feeding, on the other hand, has been shown to produce a short-term increase in plasma TMAO among healthy young men.56
Saliva diagnostics: emerging techniques and biomarkers for salivaomics in cancer detection
Published in Expert Review of Molecular Diagnostics, 2022
Jieren Liu, Dongna Huang, Yuanzhe Cai, Zhihua Cao, Zhiyu Liu, Shuo Zhang, Lin Zhao, Xin Wang, Yuchuan Wang, Feijuan Huang, Zhengzhi Wu
Metabolome is the complete set of small molecular metabolites of living tissues including metabolic intermediates such as carbohydrates, lipids, amino acids, nucleic acids, hormones, and other signaling molecules [65]. Salivary metabolites are important in elucidating the pathways underlying different diseases, thus making it ideal for the early detection of a wide range of diseases, including oral cancer and periodontal diseases [66]. Comprehensive metabolite analysis of saliva samples showed the combination of eight metabolites (leucine, isoleucine, tryptophan, valine, glutamic acid, phenylalanine, glutamine and aspartic acid) that was able to discriminate healthy controls from pancreatic cancer subjects [17]. Many studies have confirmed that the analysis of salivary metabolites may be a clinically viable method for the diagnosis of oral cancer [67]. A combination of pipecolate and S-adenosylmethionine (SAM) in saliva samples showed high potential to discriminate oral cancers from healthy controls [68]. A recent GC-MS-based metabolomics study identified salivary metabolites such as malic acid, maltose, protocatechuic acid, lactose, 2-ketoadipic, and catechol to be possible salivary biomarkers for OSCC [69].
Mass spectrometry-based metabolomics diagnostics – myth or reality?
Published in Expert Review of Proteomics, 2021
Oxana P. Trifonova, Dmitri L. Maslov, Elena E. Balashova, Petr G. Lokhov
The main biological challenges for the translation of metabolomics-based results into the clinic are the biological diversity of the metabolome and the limited specificity of most metabolites. The metabolome is dynamic and most closely linked to the phenotype of the organism, reflecting all biochemical processes in the organism including those due to environmental effects [21]. Thus, metabolomics analysis enables the detection of any variations associated with both differences in lifestyle (diet, physical activity, and use of drugs or supplements) of various individuals (inter-individual variation) and changes occurring in the lifestyle of an individual (intra-individual variation). One must also consider age-associated changes in the metabolome. Therefore, to reduce inter- and intra-individual variation, the cohorts analyzed for identifying disease-associated metabolites should comprise the appropriate individuals based on – sex, age, disease type, and drug use, etc. Moreover, the sample size of compared groups should be equal and large enough to provide statistical power. In the case of datasets that are highly heterogeneous, large sample sizes are needed. Using patient questionnaires for group forming can be an additional useful tool for collecting homogeneous discovery populations for revealing important metabolites [18–20,59].
Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production
Published in Gut Microbes, 2021
Johannes Hertel, Almut Heinken, Filippo Martinelli, Ines Thiele
To validate the simulation results regarding glutarate and butyrate, we integrated the simulation data systematically with fecal metabolome measurements in 347 individuals of the same cohort, including quantifications of glutarate and butyrate concentrations.16 Note that the fecal metabolome is a representative of human metabolism, diet intake, and microbial metabolism such that it cannot be expected that the microbiome can fully explain variegation in fecal metabolite profiles. However, as the microbiome is one source of variance in fecal metabolite content and as the simulations predict systematic variance in microbiome secretion profiles across the modeled individuals, we expected that the association pattern between microbes and metabolite production capacities is reflective of the association pattern between microbes and fecal metabolite concentrations. For statistical analyses, fecal glutarate and butyrate concentrations were log-transformed, minimizing the skewness of the distributions.