Xiong Guangzhou, Ji Lei, Cheng Mingyue, Ning Kang
Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Microbiol Spectr. 2023 Mar 14;11(2):e0016723. doi: 10.1128/spectrum.00167-23.
Microbiota residing on the urban transit systems (UTSs) can be shared by travelers and have niche-specific assemblage. However, it remains unclear how the assemblages are influenced by city characteristics, rendering city-specific and microbial-aware urban planning challenging. Here, we analyzed 3,359 UTS microbial samples collected from 16 cities around the world. We found the stochastic process dominated in all UTS microbiota assemblages, with the explanation rate () of the neutral community model (NCM) higher than 0.7. Moreover, city characteristics predominantly drove such assemblage, largely responsible for the variation in the stochasticity ratio (50.1%). Furthermore, by utilizing an artificial intelligence model, we quantified the ability of UTS microbes in discriminating between cities and found that the ability was also strongly affected by city characteristics, especially climate and continent. From these, we found that although the NCM of the New York City UTS microbiota was 0.831, the accuracy of the microbial-based city characteristic classifier was higher than 0.9. This is the first study to demonstrate the effects of city characteristics on the UTS microbiota assemblage, paving the way for city-specific and microbial-aware applications. We analyzed the urban transit system microbiota assemblage across 16 cities. The stochastic process was dominant in the urban transit system microbiota assemblage. The urban transit system microbe's ability in discriminating between cities was quantified using transfer learning based on random forest (RF) methods. Certain urban transit system microbes were strongly affected by city characteristics.
居住在城市交通系统(UTS)中的微生物群可被旅行者共享,且具有特定生态位的组合。然而,这些组合如何受到城市特征的影响仍不清楚,这使得针对特定城市且考虑微生物因素的城市规划具有挑战性。在此,我们分析了从全球16个城市收集的3359份UTS微生物样本。我们发现随机过程在所有UTS微生物群组合中占主导地位,中性群落模型(NCM)的解释率()高于0.7。此外,城市特征主要驱动了这种组合,在很大程度上导致了随机性比率的变化(50.1%)。此外,通过利用人工智能模型,我们量化了UTS微生物区分不同城市的能力,发现该能力也受到城市特征的强烈影响,尤其是气候和大陆。由此,我们发现尽管纽约市UTS微生物群的NCM为0.831,但基于微生物的城市特征分类器的准确率高于0.9。这是第一项证明城市特征对UTS微生物群组合有影响的研究,为针对特定城市且考虑微生物因素的应用铺平了道路。我们分析了16个城市的城市交通系统微生物群组合。随机过程在城市交通系统微生物群组合中占主导地位。基于随机森林(RF)方法的迁移学习被用于量化城市交通系统微生物区分不同城市的能力。某些城市交通系统微生物受到城市特征的强烈影响。