Translational Metabolomics: Current Challenges and Future Opportunities

Abstract

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Intermittent Fasting Enhanced the Cognitive Function in Older Adults with Mild Cognitive Impairment by Inducing Biochemical and Metabolic changes: A 3-Year Progressive Study

“…Since metabolomics provide a favorable opportunity to generate novel biomarkers and hypotheses for addressing the molecular mechanism of diseases, this study investigated the metabolite profiles of plasma among older adults with MCI of different IF groups [63]. Results from this present study clearly indicated that the practice of IF was related to lipid metabolism (synthesis and degradation of ketone bodies and butanoate metabolism).…”

The Application of Metabolomics to Probiotic and Prebiotic Interventions in Human Clinical Studies

“…The range of analytical platforms used in the studies discussed here are representative of the rest of the metabolomics field. The advantages and disadvantages of these platforms has been extensively reviewed and a summary of the different platforms used in fecal metabolome analysis is presented in the review by Karu et al [73,74]. Given the strengths and weaknesses of each platform, especially in terms of sensitivity, quantitation and reproducibility, the use of multiple platforms would be highly advantageous.…”

Deep metabolome: Applications of deep learning in metabolomics

“…For comparison, Sequence Read Archive (SRA) database, a repository for next-generation sequencing data, has ∼ 1,000 human-related studies [90] , whereas Metabolomics Workbench database has 68 human-related projects [91] . Ultimately, this might be due to different cost structures between genomics [92] and metabolomics [93] , [94] , as well as lack of interdisciplinary research opportunities among deep learning practitioners, metabolomics experts, and other omics scientists [93] .…”