Gao Jingyuan, Yi Xinzhu, Wang Zhang
Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China.
Comput Struct Biotechnol J. 2023 Oct 12;21:4933-4943. doi: 10.1016/j.csbj.2023.10.016. eCollection 2023.
The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.
呼吸道微生物组的研究已进入多组学时代。通过整合从呼吸道标本中检测到的不同组学数据类型，如宏基因组、宏转录组、宏蛋白质组、代谢组、培养组和放射组，可以全面了解肺部微生物组及其在呼吸道疾病中与宿主免疫和炎症的相互作用。多组学的力量推动该领域从对微生物组改变的关联性评估发展到对慢性、急性和其他类型呼吸道疾病发病机制中肺部微生物组的因果性理解。然而，多组学在呼吸道微生物组中的应用在样本处理、数据整合和下游验证方面仍然面临独特的挑战。在这篇综述中，我们首先介绍适用于研究呼吸道微生物组的呼吸道样本类型和组学数据类型。接下来，我们描述多组学整合的方法，重点是降维、多组学关联和预测。然后，我们总结多组学在研究呼吸道疾病微生物组方面的应用进展。最后，我们讨论当前的挑战并分享我们对该领域未来前景的看法。
