Bowman Jeff S
Scripps Institution of Oceanography, UC San Diego, La Jolla, California, USA.
Center for Microbiome Innovation, UC San Diego, La Jolla, California, USA.
mSystems. 2021 Aug 31;6(4):e0099321. doi: 10.1128/mSystems.00993-21.
Microbes serve as sensitive indicators of ecosystem change due to their vast diversity and tendency to change in abundance in response to environmental conditions. Although we most frequently observe these changes to study the microbial community itself, it is increasingly common to use them to understand the surrounding environment. In this way microbial communities can be thought of as powerful sensors capable of reporting shifts in chemical or physical conditions with high fidelity. In this commentary, I further explore this idea by drawing a comparison to the olfactory system, where populations of sensory neurons respond to the presence of specific odorants. The possible combinations of sensory neurons that can transduce a signal are virtually limitless. Yet, the brain can deconvolute the signal into recognizable and actionable data. The further development of machine learning techniques and its application hold great promise for our ability to interpret microbes to detect environmental change.
由于微生物具有巨大的多样性以及随环境条件变化而改变丰度的倾向，它们可作为生态系统变化的敏感指标。尽管我们最常观察这些变化是为了研究微生物群落本身，但利用它们来了解周围环境的情况也越来越普遍。通过这种方式，微生物群落可被视为强大的传感器，能够高保真地报告化学或物理条件的变化。在这篇评论中，我通过与嗅觉系统进行比较来进一步探讨这一观点，在嗅觉系统中，感觉神经元群体对特定气味剂的存在做出反应。能够转导信号的感觉神经元的可能组合几乎是无限的。然而，大脑能够将信号解卷积为可识别且可采取行动的数据。机器学习技术的进一步发展及其应用对于我们解读微生物以检测环境变化的能力具有巨大的前景。
