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Integration of metabolomics and other omics: from microbes to microbiome

D.E. GoInstitute of Food Industrialization, Institutes of Green Bioscience and Technology, Seoul National University, Pyeongchang, Gangwon-Do, 25354, Republic of KoreaGun-Hwi YeonDepartment of Biosystems and Bioengineering, KRIBB School of Biotechnology, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of KoreaSoo‐Jin ParkDepartment of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USAYujin LeeGraduate School of International Agricultural Technology, Seoul National University, Pyeongchang-Gun, 25354, Gangwon-Do, Republic of KoreaHyun Gi KohDepartment of Biological and Chemical Engineering, Hongik University, Sejong, 30016, Republic of KoreaHyunjin KooGraduate School of International Agricultural Technology, Seoul National University, Pyeongchang-Gun, 25354, Gangwon-Do, Republic of KoreaKyoung Heon KimDepartment of Biotechnology, Graduate School, Korea University, Seoul, 02841, Republic of KoreaYong‐Su JinCarl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USABong Hyun SungDepartment of Biosystems and Bioengineering, KRIBB School of Biotechnology, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea. [email protected]Jungyeon KimGraduate School of International Agricultural Technology, Seoul National University, Pyeongchang-Gun, 25354, Gangwon-Do, Republic of Korea. [email protected]
2024en
ABI

Аннотация

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.

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