Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-1101, USA.
Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-1101, USA; Department of Paediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, 20520, Finland.
Clin Microbiol Infect. 2021 Feb;27(2):283.e1-283.e7. doi: 10.1016/j.cmi.2020.05.033. Epub 2020 Jun 4.
Little is known about maturation of the airway microbiota during early childhood and the consequences of early-life antibiotic exposure.
In a population-based birth cohort of 902 healthy Finnish children, we applied deep neural network models to investigate the relationship between the nasal microbiota (measured by 16S rRNA gene sequencing at up to three time points) and child age during the first 24 months. We also performed stratified analyses according to antibiotic exposure during the age period 0-2 months.
The dense deep neural network analysis successfully modelled the relationship between 232 bacterial genera and child age with a mean absolute error of 4.3 (95%CI 4.0-4.7) months. Similarly, the recurrent neural network analysis also successfully modelled the relationship between 215 genera and child age with a mean absolute error of 0.45 (95%CI 0.42-0.47) months. Among the genera, Staphylococcus spp. and members of the Corynebacteriaceae decreased with age, while Dolosigranulum and Moraxella increased with age in the first 2 years of life (all false discovery rate (FDR) = 0.001). In children without early-life antibiotic exposure, Dolosigranulum increased with age (FDR = 0.001). By contrast, in those with early-life antibiotic exposure, Haemophilus increased with age (FDR = 0.002).
In this prospective birth cohort of healthy children, we demonstrated the development of the nasal microbiota, with shifts in specific genera constituting maturation, in the first 2 years of life. Antibiotic exposures during early infancy were related to different age-discriminatory bacteria.
人们对婴幼儿期气道微生物群的成熟过程以及生命早期抗生素暴露的后果知之甚少。
在一项基于人群的 902 名健康芬兰儿童的队列研究中，我们应用深度神经网络模型来研究鼻腔微生物群（通过多达三个时间点的 16S rRNA 基因测序进行测量）与生命头 24 个月期间儿童年龄之间的关系。我们还根据 0-2 个月期间的抗生素暴露情况进行了分层分析。
密集的深度神经网络分析成功地建立了 232 个细菌属与儿童年龄之间的关系，平均绝对误差为 4.3（95%CI 4.0-4.7）个月。同样，递归神经网络分析也成功地建立了 215 个属与儿童年龄之间的关系，平均绝对误差为 0.45（95%CI 0.42-0.47）个月。在这些属中，葡萄球菌属和棒状杆菌科成员随着年龄的增长而减少，而杜洛西格兰姆菌和莫拉氏菌在生命的前 2 年中随着年龄的增长而增加（所有错误发现率（FDR）= 0.001）。在没有生命早期抗生素暴露的儿童中，杜洛西格兰姆菌随着年龄的增长而增加（FDR= 0.001）。相比之下，在有生命早期抗生素暴露的儿童中，嗜血杆菌随着年龄的增长而增加（FDR= 0.002）。
在这项针对健康儿童的前瞻性出生队列研究中，我们证明了鼻腔微生物群的发展，在生命的头 2 年中，特定属的变化构成了成熟。生命早期的抗生素暴露与不同的年龄判别细菌有关。
