Early life microbial succession in the gut follows common patterns in humans across the globe

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life. Using 3,154 samples from 1,827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.61 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of “microbiome age” for assessing early ..., Raw metagenomic sequence reads were processed using tools from the bioBakery suite, following already-established protocols [1]. Initially, KneadData v0.10.0 was employed with default settings to trim low-quality reads and eliminate human sequences, using the hg37 reference database. Subsequently, MetaPhlAn v3.1.0, utilizing the mpa_v31_CHOCOPhlAn_201901 database, was applied with default parameters to map microbial marker genes and generate taxonomic profiles. The taxonomic profiles, along with the same reads obtained in the initial step, were then processed with HUMAnN v3.7 to produce stratified functional profiles. [1] - Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife 10, (2021)., , # Data from: Early life microbial succession in the gut follows common patterns in humans across the globe [https://doi.org/10.5061/dryad.dbrv15f9z](https://doi.org/10.5061/dryad.dbrv15f9z) This dataset contains: * Pre-formatted selected Taxonomic profiles from stool metagenomic sequencing obtaine from a combination of: * 8 previously-published and publicly-available studies: **Asnicar, F. et al. (2017)** [1], **Backhed, F. et al. (2015)** [2], **Kostic, A. D. et al. (2015)** [3], **Pehrsson, E. et al. (2016)** [4], **Shao, Y. et. al (2019)** [5], **Vatanen, T. et al. (2016)** [6], **Yassour, M. et al. (2018)** [7], **Bonham, K. et al. (2023)** [8]; and * 4 studies from the Wellcome Leap 1kD program: **Fatori, D. et al. (2024)** [9], **Hemmingway, A. et al. (2020)** [10], **O'Sullivan, J. et al. (2024)** [11] and the publication accompanying this dataset, **Bottino, G. et al. (2024)**, which introduces the samples from the Khula study, **Zieff, M. et al. (2024)** [12] * Pre-forma...,解析婴儿肠道微生物组（microbiome）的群落演替动态，对于理解儿童健康与发育至关重要，但目前尚无标准化模型。本研究基于宏基因组（metagenome）测序得到的肠道微生物分类群相对丰度，对出生后1.5年内的儿童年龄进行预测，时间分辨率可达±3个月。本研究使用覆盖12个国家的1827名婴儿的3154份样本，训练了随机森林（random forest）模型，最终得到的均方根误差为2.61个月。本研究鉴定出了预测年龄的关键分类群标志物，包括双歧杆菌属（Bifidobacterium）物种丰度的下降，以及普拉梭菌（Faecalibacterium prausnitzii）和毛螺菌科（Lachnospiraceae）丰度的上升。不同人群的婴儿均呈现保守的微生物演替模式，提示存在通用的发育轨迹。功能分析验证了与喂养转变及饮食暴露相关的关键微生物基因的变化趋势。本模型可为早期……评估提供"微生物组年龄"的标准化基准。 原始宏基因组测序读段按照已建立的实验流程[1]，使用bioBakery工具套件进行处理。首先，使用hg37参考数据库，以默认参数运行KneadData v0.10.0，对低质量读段进行修剪并去除人类宿主序列。随后，使用mpa_v31_CHOCOPhlAn_201901数据库，以默认参数运行MetaPhlAn v3.1.0，完成微生物标记基因的比对并生成分类群丰度谱。随后，将得到的分类群丰度谱与第一步保留的原始读段一同使用HUMAnN v3.7进行处理，得到分层的功能丰度谱。 [1] Beghini F 等. 利用bioBakery 3整合多样微生物群落的分类、功能及菌株水平谱分析. Elife 10, 2021. # 数据集来源：全球不同人群的婴儿肠道微生物早期演替均遵循共同模式 [https://doi.org/10.5061/dryad.dbrv15f9z](https://doi.org/10.5061/dryad.dbrv15f9z) 本数据集包含： * 经预格式化的粪便宏基因组测序分类群丰度谱，数据来源于以下两部分： * 8项已发表的公开研究：**Asnicar F 等 (2017)** [1]、**Backhed F 等 (2015)** [2]、**Kostic A D 等 (2015)** [3]、**Pehrsson E 等 (2016)** [4]、**Shao Y 等 (2019)** [5]、**Vatanen T 等 (2016)** [6]、**Yassour M 等 (2018)** [7]以及**Bonham K 等 (2023)** [8]；以及 * 4项来自Wellcome Leap 1kD计划的研究：**Fatori D 等 (2024)** [9]、**Hemmingway A 等 (2020)** [10]、**O'Sullivan J 等 (2024)** [11]，以及本数据集配套发表的**Bottino G 等 (2024)**（该研究介绍了Khula队列的样本）与**Zieff M 等 (2024)** [12] * 经预格式化的……