Integration of metabolomics and other omics: from microbes to microbiome

• Published in: Applied microbiology and biotechnology 2024-12, Vol.108 (1), p.538
• Main Authors: Go, Daewon, Yeon, Gun-Hwi, Park, Soo Jin, Lee, Yujin, Koh, Hyun Gi, Koo, Hyunjin, Kim, Kyoung Heon, Jin, Yong-Su, Sung, Bong Hyun, Kim, Jungyeon
• Format: Article
• Language: English
• Subjects: Bacteria - classification; Bacteria - genetics; Bacteria - metabolism; Biological analysis; Biomedical and Life Sciences; Biotechnology; Genomics - methods; Intermediates; Life Sciences; Metabolic engineering; Metabolic Engineering - methods; Metabolic Networks and Pathways; Metabolic pathways; Metabolism; Metabolites; Metabolome; Metabolomics; Metabolomics - methods; Microbial Genetics and Genomics; Microbiology; Microbiomes; Microbiota - physiology; Microorganisms; Mini-Review; Tracks (paths)
• ISSN: 0175-7598; 1432-0614; 1432-0614
• DOI: 10.1007/s00253-024-13384-z

Metabolomics is a cutting-edge omics technology that identifies metabolites in organisms and their environments and tracks their fluctuations. This field has been extensively utilized to elucidate previously unknown metabolic pathways and to identify the underlying causes of metabolic changes, given its direct association with phenotypic alterations. However, metabolomics inherently has limitations that can lead to false positives and false negatives. First, most metabolites function as intermediates in multiple biochemical reactions, making it challenging to pinpoint which specific reaction is responsible for the observed changes in metabolite levels. Consequently, metabolic processes that are anticipated to vary with metabolite concentrations may not exhibit significant changes, generating false positives. Second, the range of metabolites identified is contingent upon the analytical conditions employed. Until now, no analytical instrument or protocol has been developed that can capture all metabolites simultaneously. Therefore, some metabolites are changed but are not detected, generating false negatives. In this review, we offer a novel and systematic assessment of the limitations of omics technologies and propose-specific strategies to minimize false positives and false negatives through multi-omics approaches. Additionally, we provide examples of multi-omics applications in microbial metabolic engineering and host-microbiome interactions, helping other researchers gain a better understanding of these strategies. Key points • Metabolomics identifies metabolic shifts but has inherent false positive/negatives. • Multi-omics approaches help overcome metabolomics’ inherent limitations.