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代谢组学与其他组学的整合:从微生物到微生物群落

Integration of metabolomics and other omics: from microbes to microbiome.

作者信息

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

机构信息

Institute of Food Industrialization, Institutes of Green Bioscience and Technology, Seoul National University, Pyeongchang, Gangwon-Do, 25354, Republic of Korea.

Synthetic Biology and Bioengineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.

出版信息

Appl Microbiol Biotechnol. 2024 Dec 19;108(1):538. doi: 10.1007/s00253-024-13384-z.

Abstract

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.

摘要

代谢组学是一项前沿的组学技术,可识别生物体内及其环境中的代谢物,并追踪其波动情况。鉴于该技术与表型改变直接相关,已被广泛用于阐明此前未知的代谢途径,并确定代谢变化的潜在原因。然而,代谢组学本身存在局限性,可能导致假阳性和假阴性结果。首先,大多数代谢物在多种生化反应中起中间产物的作用,因此很难确定是哪种特定反应导致了观察到的代谢物水平变化。因此,预期会随代谢物浓度变化的代谢过程可能并未表现出显著变化,从而产生假阳性结果。其次,所识别的代谢物范围取决于所采用的分析条件。到目前为止,尚未开发出能够同时捕获所有代谢物的分析仪器或方案。因此,一些代谢物发生了变化但未被检测到,从而产生假阴性结果。在本综述中,我们对组学技术的局限性进行了新颖而系统的评估,并提出了通过多组学方法将假阳性和假阴性结果降至最低的具体策略。此外,我们还提供了多组学在微生物代谢工程和宿主-微生物组相互作用中的应用实例,以帮助其他研究人员更好地理解这些策略。要点:• 代谢组学可识别代谢变化,但存在固有的假阳性/阴性结果。• 多组学方法有助于克服代谢组学的固有局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c987/11659354/6344f94328cc/253_2024_13384_Fig1_HTML.jpg

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