Qian Gordon, Ho Joshua W K
School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Biophys Rev. 2020 Aug;12(4):851-863. doi: 10.1007/s12551-020-00724-2. Epub 2020 Jul 7.
Research in the human gut microbiome has bloomed with advances in next generation sequencing (NGS) and other high-throughput molecular profiling technologies. This has enabled the generation of multi-omics datasets which holds promises for big data-enabled knowledge acquisition in the form of understanding the normal physiological and pathological involvement of gut microbiomes. Ample evidence suggests that distinct microbial compositions in the human gut are associated with different diseases. However, the biological mechanisms underlying these associations are often unclear. There is a need to move beyond statistical associations to discover how changes in the gut microbiota mechanistically affect host physiology and disease development. This review summarises state-of-the-art big data and systems biology approaches for mechanism discovery.
随着下一代测序(NGS)和其他高通量分子谱分析技术的进步,人类肠道微生物组的研究蓬勃发展。这使得多组学数据集得以生成,有望通过理解肠道微生物群的正常生理和病理参与情况,以大数据驱动的方式获取知识。大量证据表明,人类肠道中不同的微生物组成与不同疾病相关。然而,这些关联背后的生物学机制往往尚不清楚。有必要超越统计关联,去发现肠道微生物群的变化如何在机制上影响宿主生理和疾病发展。本综述总结了用于机制发现的最新大数据和系统生物学方法。